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Psykologtidningen

Sveriges Psykologförbund

Studiemetod som ger svar på individnivå

12 mars 2020 publicerad av Peter Örn

Medan RCT-studier ger genomsnittliga svar för gruppen visar single case-studier förändringar för en unik individ i en specifik kontext. Metoden överbryggar klyftan mellan forskning och klinik och den får nu allt större uppmärksamhet inom psykologisk forskning skriver psykologiforskarna Rikard Wicksell och Johan W.S Vlaeyen.

Vården blir alltmer individualiserad. Genombrott inom genetiken har banat väg för personalized medicine utifrån idén att varje patient är unik och att förmågan att anpassa behandlingen till de unika behoven är avgörande för behandlingseffekten. Lättillgängliga data skapar också förutsättningar för att involvera patienten mer i besluts- och förändringsprocesser.

Randomiserade kontrollerade studier (RCT) har under lång tid ansetts vara gold standard för att utvärdera behandlingseffekter och verksamma mekanismer. Men de flesta av våra evidensbaserade behandlingar har endast en moderat effekt och dessutom med en stor individuell variation. Utan att förringa betydelsen av RCT som utvärderingsmetod finns uppenbara svårigheter att dra meningsfulla slutsatser om effekter på en enskild individ. Geriatrikern John Evans sammanfattade kärnfullt dilemmat med gruppbaserade jämförelser: »Managers and trialists may be happy for treatments to work on average patient’s doctors expect to do better than that” [1].

I takt med att en mer nyanserad bild av RCT:s betydelse växt fram har många forskare lyft dilemmat med att den »genomsnittliga personen« inte existerar och att resultaten (effekten av interventionen på gruppnivå) därför inte kan anses vara ett riktmärke för hur en enskild individ kan förväntas svara på behandlingen. Dessutom är det väl känt att denna typ av studier vanligtvis har studiekriterier som gör att komplexa patienter exkluderas exempelvis patienter med samsjuklighet. Den kanske allra mest uppenbara svårigheten är behovet av och tillgången till stora forskningssampel. Detta skapar ofta betydande problem både avseende tid och kostnad för att genomföra studien.

Sammantaget har generaliserbarheten (och därmed validiteten) av stora randomiserade kliniska prövningar ifrågasatts. För att studera sambandet mellan intervention och förändring för en enskild individ behövs en annan modell. Det har gjort att SCED på senare tid har fått allt större uppmärksamhet inte minst i psykologisk behandlingsforskning.

Single-case experimental designs (SCED)

SCED (betecknas ibland n=1 och N-of-1) bygger enkelt uttryckt på en serie av observationer under en fördefinierad period som innefattar en baslinje med randomiserad längd och (minst) en experimentell manipulation (interventionen).

Under lång tid har SCED haft en framträdande roll i psykologisk och medicinsk forskning där studier av ett fåtal individer lagt grunden för avgörande kunskapsutveckling. Ett av de mest kända exemplen är Pavlovs studier som byggde på observationer av reaktionsmönster hos enskilda individer vilka validerades genom att replikera fynden med andra deltagare [2]. SCED är i dag ett väletablerat och tillförlitligt sätt att utvärdera förändringen för en enskild enhet (en patient, en grupp, en familj ett sjukhus, en region etcetera). Metodens betydelse har nyligen lyfts fram i tidskriften Nature. I en artikel om »imprecision medicine« beskrivs att de vanligaste förskrivna läkemedlen i USA hjälper en bråkdel av de som använder dem (4–25 procent). Artikelförfattarens budskap är att precision medicine kräver något mer än klassiska kliniska prövningar (RCT) och argumenterar för att lösningen är: »transforming everyday cli-nical care into solid N-of-1 trials« [3].

SCED har flera viktiga fördelar jämfört med gruppbaserade studier. Framförallt ges möjligheten att studera förändring för en unik individ i en specifik kontext vilket klargör de förutsättningar som råder när en intervention fungerar. Kontinuerlig information om utvecklingen ger också möjligheten till grafisk visualisering i realtid.

Genom dessa möjligheter överbryggar SCED också klyftan mellan forskning och klinik. Skillnaderna mellan vetenskapliga studier och väl genomförd behandling minskar när data på individnivå används systematiskt för såväl kunskapsutveckling som beslut i sjukvården. Både för kliniska ändamål och forskning utgör SCED en värdefull metod för att exempelvis följa utvecklingen av bieffekter i fall när klinikern känner sig osäker på lämpligheten av en viss behandling eller när teamet kring patienten inte är överens. En kontinuerlig insamling av data och monitorering av effekterna skapar en tydlighet som är gynnsam både för patienten och för teamet.

Bakgrunden till SCED

Utvecklingen av SCED är besläktad med ambitionen att skapa en yrkesroll för psykologer som sammanför det vetenskapliga förhållningssättet med den kliniska uppgiften. Konferensen i Boulder i Colorado 1949 arrangerades av American Psychological Association (APA) i en efterkrigstid där psykisk ohälsa ökade som en effekt av hemvändande soldater. Under två veckor diskuterades professionsfrågor som utbildning licens samarbete med andra professioner och etiska aspekter. Konferensen mest framträdande slutsats var definitionen av psykologen som en scientist-practitioner med träning både i vetenskaplig metod och kliniska färdigheter och som naturligt och systematiskt samlar in och analyserar data för att fatta beslut förbättra de kliniska metoderna utveckla professionen och bidra till kunskapsutvecklingen.

I dag 70 år senare kan konstateras att vi har en bit kvar men trots allt är på rätt väg. Klyftan mellan klinik och forskning är dock tydlig inom psykologin såväl som inom andra ämnesområden av relevans för hälso- och sjukvård [4].

Varför RCT och inte SCED som gold standard?

Psykologin är i grunden en lära om individen. Trots detta så är merparten av den kunskap vi har om psykologiska faktorer baserad på data från grupper. Hur kommer det sig att vi styrt bort från traditionen att orientera analysen utifrån den enskilde patienten till förmån för utvärderingar som bygger på gruppdata och avvikelser från medelvärden?

I början av 1900-talet inspirerad av Darwins studier om evolutionen fanns en allt ökande trend att studera individuella skillnader och avvikelser från den »genomsnittlige« individen. Detta lade grunden för metodologin (inklusive statistiska modeller) bakom gruppbaserad forskning. En viktig representant för denna nya inriktning var Sir Ronald Fisher vars engage-mang bland annat rörde huruvida studier på enskilda sampel kan generera kunskap av betydelse för en hel population. Med utvecklingen av sofistikerade statistiska test kom Fisher att bana väg för en ökande andel gruppbaserad forskning som över tid kom att etablera randomiserade kontrollerade studier som den optimala modellen en »gold standard« [5]. Gruppbaserad forskning var dock inte lösningen på alla problem och genererade dessutom en del nya som att det unika hos individen och kontexten fick mindre uppmärksamhet.

I juni 2000 samlades kliniska forskare vid the Institute of Medicine för att diskutera den allt ökande farhågan för att viktiga forskningsfynd ofta inte nådde hela vägen fram till en till-lämpning där tydliga nyttoeffekter på individer och samhället kan identifieras. Detta bidrog till en ökad angelägenhet kring det som kommit att kallas translational science att överföra nya fynd och metoder från grundforskning till förbättrade modeller för den kliniska användningen. Två typer av translationell forskning kan identifieras. Dels studier som görs i grundforskningsmiljöer där forskare eftersträvar att fynden ska kunna omsättas i praktiska metoder dels kliniska studier där nya rön från grundforskningen ligger till grund för exempelvis nya interventioner som testas i kliniska prövningar med verkliga patienter.

På senare tid har argumenterats för att samhällets forskningsresurser i alltför stor utsträckning ägnas åt den första typen vilket är bekymmersamt av flera skäl. Förutom att grundforskningsfynd ofta fastnar på den långa och krokiga vägen mot förbättrade kliniska metoder som förbättrar folkhälsan finns i dag också en medvetenhet om att sjukvården skulle ha större nytta av att studera hur existerande (evidensbaserade) metoder kan tillgängliggöras och användas än att utveckla nya metoder.

Behovet av den kliniska forskningen är således skriande och bör klargöra hur verkliga patienter snarare än den »genomsnittlige individen« svarar på behandlingen vilket med tydlighet indikerar värdet av SCED.

Visuella analyser

Den huvudsakliga uppgiften i kliniska studier är att bedöma om en förändring har skett. Inom SCED har visuella analysmetoder förespråkats under lång tid. I den enklaste modellen jämförs en baslinje (A) med en förändringsfas (B) där en tydlig effekt bör kunna åskådliggöras genom en graf.

Tre aspekter är av särskild betydelse för att generera tydliga effekter: robusta mått (exempelvis frekvensen av beteenden) som är känsliga för den förändring som utvärderas en stabil baslinje (ej för stor variation) samt en tydlig och helst snabb effekt. Detta är inte alltid enkelt. En tydlig idé om behandlingsmekanismer och måttens känslighet för förändring blir därför viktigt.

En systematisk visuell analys ger en stor mängd relevant information som är svåråtkomlig i gruppdata hur stabil baslinjedata är om någon uppenbar förändring skett när den upp-stått och hur stabil denna effekt är. För att underlätta den visuella analysen och värdera resultaten finns användbara riktlinjer för tolkning [6].

Statistiska metoder när visuell analys inte räcker

Även om det i många fall kan vara tillräckligt med visuella analysmetoder så finns tillfällen då statistiska svar efterfrågas. Enkla statistiska tillvägagångssätt kan användas som komplement till visuella analyser för att bedöma storleken på effekten som att räkna antalet observationer i förändringsfasen (B) ovanför baslinjefasens (A) medelvärde. Men även mer avancerade statistiska analyser har sin plats inom SCED. För drygt 40 år sedan skrev Donald Hartmann en betydelsefull artikel där han poängterade att av statistiska analyser som är utvecklade för användning på gruppdata medför stora begränsningar och tolkningsproblem när de används för enskilda individer. Han pekade i riktning mot andra modeller som sedan dess vidare-utvecklats och med framgång använts för SCED [7].

Utöver robusta statistiska modeller finns användarvänliga digitala lösningar som möjliggör analyser av individdata exempelvis Shiny som utvecklats vid universitetet i Leuven och er-bjuds via the Open Science Framework vid universitetet i Barcelona https://osf.io/t6ws6/.

Sammanställa data och dra slutsatser

Antalet SCED-studier inom klinisk psykologi har ökat lavinartat de senaste 50 åren. Dock brister många i metodkvalitet och/eller i hur väl studien har beskrivits vilket inverkar negativt på den vetenskapliga nyttan av studierna. En viktig utveckling skedde för cirka 20 år sedan då CONSORT (Consolidated Standards Of Reporting Trials) Statement formulerades i syfte att klargöra hur en vetenskaplig rapport bör formuleras. Dessa rekommendationer följs världen över och i dag finns tydliga riktlinjer också för SCED bland annat Single-Case Reporting guideline In Behavioural interventions (SCRIBE) [8]. SCRIBE har anammats av American Psychologial Association och består av en checklista med 26 items inom sex områden vilka täcker alla delar av den vetenskapliga studien från titel till metod resultat och diskussion.

Genom riktlinjer för genomförande och rapportering av SCED kan kvaliteten på studierna och dess betydelse för kunskapsutvecklingen successivt öka.

Vägen framåt

SCED har utvecklats väsentligt de senaste decennierna bland annat statistiska modeller och digitala lösningar för datasamling och visualisering av resultat. Dessutom har riktlinjer för genomförande och rapportering förbättrat förutsättningarna för kvalitet transparens och värdering av resultaten. Sedan 2017 finns också ett internationellt nätverk med syftet att tillhan-dahålla kunskap och skapa förutsättningar för diskussion och samarbete relaterat till SCED the International Collaborative Network for N-of-1 Clinical Trials and Single-Case Experimen-tal Designs (ICN) https://nof1andsced.wixsite.com/home.

År 2018 anordnades den första internationella konferensen om SCED genom ett samarbete mellan Karolinska Iinstitutet och KU Leuven University: N=1 Stockholm symposium med nära 250 deltagare från såväl Europa som USA och Kanada. I samband med planeringen av ett specialnummer i tidskriften Psychological records riktades en förfrågan om att skriva en artikel om SCED utifrån symposiet och denna artikel utgör grunden för denna text [9]. Nästa möte hålls den 7–8 april 2020 i Leuven i Belgien.

1. Evans JG (1995). Evidence-based and evidence-biased medicine. Age and Ageing 24(6) 461-463. 2. Barlow DH Nock MK & Hersen M (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change 3rd Edition. New York: Pearson. 3. Schork NJ (2015). Personalized medicine: Time for one-person trials. Nature 520(7549) 609-611. 4. Smith LS & Wilkins N (2018). Mind the Gap: Approaches to Addressing the Research-to-Practice Practice-to-Research Chasm. J Public Health Manag Pract 24 Suppl 1 Suppl Injury and Violence Prevention S6-S11. 5. Grossman J & Mackenzie F J (2005). The randomized controlled trial: gold standard or merely standard? Perspect Biol Med 48(4) 516-534. 6. Morley S (2017). Single Case Methods in Clinical Psychology. A Practical Guide. 7. Manolov R & Moeyaert M (2017). How Can Single-Case Data Be Analyzed? Software Resources Tutorial and Reflections on Analysis. Behav Modif 41(2) 179-228. 8. Tate RL Perdices M Rosenkoetter U et al. (2016). The Single-Case Reporting guideline In Behavioural Interventions (SCRIBE) 2016 Statement. Archives of Scientific Psychology. 4(1) 1-9. 9. Vlaeyen JWS Wicksell RK Simons LE et al. From Boulder to Stockholm in 70 years: Single case experimental designs in health research. Under review.

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From Boulder to Stockholm in 70 Years: Single Case Experimental Designs in Clinical Research

• Theoretical article
• Published: 03 June 2020
• Volume 70 , pages 659–670, ( 2020 )

Cite this article

• Johan W. S. Vlaeyen   ORCID: orcid.org/0000-0003-0437-6665 1 , 2   na1 ,
• Rikard K. Wicksell 3   na1 ,
• Laura E. Simons 4 ,
• Charlotte Gentili 3 ,
• Tamal Kumar De 5 ,
• Robyn L. Tate 6 ,
• Sunita Vohra 7 ,
• Salima Punja 7 ,
• Steven J. Linton 8 ,
• Falko F. Sniehotta 9 &
• Patrick Onghena 5  

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With the objective of increasing the magnitude of treatment effects in behavioral health, there is steadily growing interest in tailoring assessments and interventions to better match individual needs. This aligns with the central idea that behavior can be adequately understood by considering the unique characteristics of the individual and context. Thus, data collected at an individual level provides critical evidence that can be used to inform health care decisions, improve treatment, or refine theories. Yet, the majority of research in behavioral health is based on group-level analyses. Recent developments in the field of single-case experimental design (SCED) has provided new opportunities to utilize individual data. The present article provides a state-of-the art overview regarding key aspects of SCED, including a historical background to why and how SCED emerged, declined, and recently reemerged as well as methodological aspects such as design issues, challenges related to reliability and validity of repeated observations, innovations in visual and statistical analyses of individual data, strategies to deal with missing values, methodology to examine effect size, and approaches to summarize data from a large number of SCEDs using multilevel models and meta-analyses of replication data. Finally, the article discusses key concerns and actions needed to move the field forward.

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Acknowledgements

We wish to acknowledge the people who were instrumental for and during the symposium: the participants in the strategic meeting during day 1 (in addition to the authors of the article): Maria Tillfors, Ida Flink, Katja Boersma, Mike Kemani, Lance McCracken, Julia Glombiewski, and Allen Finley, as well as the attendants of the open meeting during day 2.

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Johan W. S. Vlaeyen and Rikard K. Wicksell contributed equally to this work.

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Health Psychology Research, Faculty of Psychology and Educational Sciences, KU Leuven, Tiensestraat 102, 3000, Leuven, Belgium

Johan W. S. Vlaeyen

Experimental Health Psychology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands

Department of Clinical Neuroscience, Psychology Division, Karolinska Institutet, Stockholm, Sweden

Rikard K. Wicksell & Charlotte Gentili

Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA

Laura E. Simons

Methodology of Educational Sciences, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium

Tamal Kumar De & Patrick Onghena

John Walsh Centre for Rehabilitation Research, The Kolling Institute of Medical Research, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia

Robyn L. Tate

Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada

Sunita Vohra & Salima Punja

Center for Health and Medical Psychology, School of Law, Psychology and Social Work, Örebro University, Örebro, Sweden

Steven J. Linton

Institute of Health & Society, Newcastle University, Newcastle upon Tyne, UK

Falko F. Sniehotta

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“Replication, replication, replication” (Morley, 2018). This article is dedicated to Stephen Morley (1950–2017), a wonderful friend and colleague who made significant contributions to the applications of single-case experimental design (SCED). It also summarizes the contributions of the Stockholm symposium, “Small is Beautiful,” held October 2018, on single-case experimental designs. The objective of the meeting was to ignite discussions among researchers and clinicians actively using or interested in applying this methodology in their work, and to discuss strategies to disseminate, implement, and further develop the SCED approach.

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Vlaeyen, J.W.S., Wicksell, R.K., Simons, L.E. et al. From Boulder to Stockholm in 70 Years: Single Case Experimental Designs in Clinical Research. Psychol Rec 70 , 659–670 (2020). https://doi.org/10.1007/s40732-020-00402-5

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The Advantages and Limitations of Single Case Study Analysis

As Andrew Bennett and Colin Elman have recently noted, qualitative research methods presently enjoy “an almost unprecedented popularity and vitality… in the international relations sub-field”, such that they are now “indisputably prominent, if not pre-eminent” (2010: 499). This is, they suggest, due in no small part to the considerable advantages that case study methods in particular have to offer in studying the “complex and relatively unstructured and infrequent phenomena that lie at the heart of the subfield” (Bennett and Elman, 2007: 171). Using selected examples from within the International Relations literature[1], this paper aims to provide a brief overview of the main principles and distinctive advantages and limitations of single case study analysis. Divided into three inter-related sections, the paper therefore begins by first identifying the underlying principles that serve to constitute the case study as a particular research strategy, noting the somewhat contested nature of the approach in ontological, epistemological, and methodological terms. The second part then looks to the principal single case study types and their associated advantages, including those from within the recent ‘third generation’ of qualitative International Relations (IR) research. The final section of the paper then discusses the most commonly articulated limitations of single case studies; while accepting their susceptibility to criticism, it is however suggested that such weaknesses are somewhat exaggerated. The paper concludes that single case study analysis has a great deal to offer as a means of both understanding and explaining contemporary international relations.

The term ‘case study’, John Gerring has suggested, is “a definitional morass… Evidently, researchers have many different things in mind when they talk about case study research” (2006a: 17). It is possible, however, to distil some of the more commonly-agreed principles. One of the most prominent advocates of case study research, Robert Yin (2009: 14) defines it as “an empirical enquiry that investigates a contemporary phenomenon in depth and within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident”. What this definition usefully captures is that case studies are intended – unlike more superficial and generalising methods – to provide a level of detail and understanding, similar to the ethnographer Clifford Geertz’s (1973) notion of ‘thick description’, that allows for the thorough analysis of the complex and particularistic nature of distinct phenomena. Another frequently cited proponent of the approach, Robert Stake, notes that as a form of research the case study “is defined by interest in an individual case, not by the methods of inquiry used”, and that “the object of study is a specific, unique, bounded system” (2008: 443, 445). As such, three key points can be derived from this – respectively concerning issues of ontology, epistemology, and methodology – that are central to the principles of single case study research.

First, the vital notion of ‘boundedness’ when it comes to the particular unit of analysis means that defining principles should incorporate both the synchronic (spatial) and diachronic (temporal) elements of any so-called ‘case’. As Gerring puts it, a case study should be “an intensive study of a single unit… a spatially bounded phenomenon – e.g. a nation-state, revolution, political party, election, or person – observed at a single point in time or over some delimited period of time” (2004: 342). It is important to note, however, that – whereas Gerring refers to a single unit of analysis – it may be that attention also necessarily be given to particular sub-units. This points to the important difference between what Yin refers to as an ‘holistic’ case design, with a single unit of analysis, and an ’embedded’ case design with multiple units of analysis (Yin, 2009: 50-52). The former, for example, would examine only the overall nature of an international organization, whereas the latter would also look to specific departments, programmes, or policies etc.

Secondly, as Tim May notes of the case study approach, “even the most fervent advocates acknowledge that the term has entered into understandings with little specification or discussion of purpose and process” (2011: 220). One of the principal reasons for this, he argues, is the relationship between the use of case studies in social research and the differing epistemological traditions – positivist, interpretivist, and others – within which it has been utilised. Philosophy of science concerns are obviously a complex issue, and beyond the scope of much of this paper. That said, the issue of how it is that we know what we know – of whether or not a single independent reality exists of which we as researchers can seek to provide explanation – does lead us to an important distinction to be made between so-called idiographic and nomothetic case studies (Gerring, 2006b). The former refers to those which purport to explain only a single case, are concerned with particularisation, and hence are typically (although not exclusively) associated with more interpretivist approaches. The latter are those focused studies that reflect upon a larger population and are more concerned with generalisation, as is often so with more positivist approaches[2]. The importance of this distinction, and its relation to the advantages and limitations of single case study analysis, is returned to below.

Thirdly, in methodological terms, given that the case study has often been seen as more of an interpretivist and idiographic tool, it has also been associated with a distinctly qualitative approach (Bryman, 2009: 67-68). However, as Yin notes, case studies can – like all forms of social science research – be exploratory, descriptive, and/or explanatory in nature. It is “a common misconception”, he notes, “that the various research methods should be arrayed hierarchically… many social scientists still deeply believe that case studies are only appropriate for the exploratory phase of an investigation” (Yin, 2009: 6). If case studies can reliably perform any or all three of these roles – and given that their in-depth approach may also require multiple sources of data and the within-case triangulation of methods – then it becomes readily apparent that they should not be limited to only one research paradigm. Exploratory and descriptive studies usually tend toward the qualitative and inductive, whereas explanatory studies are more often quantitative and deductive (David and Sutton, 2011: 165-166). As such, the association of case study analysis with a qualitative approach is a “methodological affinity, not a definitional requirement” (Gerring, 2006a: 36). It is perhaps better to think of case studies as transparadigmatic; it is mistaken to assume single case study analysis to adhere exclusively to a qualitative methodology (or an interpretivist epistemology) even if it – or rather, practitioners of it – may be so inclined. By extension, this also implies that single case study analysis therefore remains an option for a multitude of IR theories and issue areas; it is how this can be put to researchers’ advantage that is the subject of the next section.

Having elucidated the defining principles of the single case study approach, the paper now turns to an overview of its main benefits. As noted above, a lack of consensus still exists within the wider social science literature on the principles and purposes – and by extension the advantages and limitations – of case study research. Given that this paper is directed towards the particular sub-field of International Relations, it suggests Bennett and Elman’s (2010) more discipline-specific understanding of contemporary case study methods as an analytical framework. It begins however, by discussing Harry Eckstein’s seminal (1975) contribution to the potential advantages of the case study approach within the wider social sciences.

Eckstein proposed a taxonomy which usefully identified what he considered to be the five most relevant types of case study. Firstly were so-called configurative-idiographic studies, distinctly interpretivist in orientation and predicated on the assumption that “one cannot attain prediction and control in the natural science sense, but only understanding ( verstehen )… subjective values and modes of cognition are crucial” (1975: 132). Eckstein’s own sceptical view was that any interpreter ‘simply’ considers a body of observations that are not self-explanatory and “without hard rules of interpretation, may discern in them any number of patterns that are more or less equally plausible” (1975: 134). Those of a more post-modernist bent, of course – sharing an “incredulity towards meta-narratives”, in Lyotard’s (1994: xxiv) evocative phrase – would instead suggest that this more free-form approach actually be advantageous in delving into the subtleties and particularities of individual cases.

Eckstein’s four other types of case study, meanwhile, promote a more nomothetic (and positivist) usage. As described, disciplined-configurative studies were essentially about the use of pre-existing general theories, with a case acting “passively, in the main, as a receptacle for putting theories to work” (Eckstein, 1975: 136). As opposed to the opportunity this presented primarily for theory application, Eckstein identified heuristic case studies as explicit theoretical stimulants – thus having instead the intended advantage of theory-building. So-called p lausibility probes entailed preliminary attempts to determine whether initial hypotheses should be considered sound enough to warrant more rigorous and extensive testing. Finally, and perhaps most notably, Eckstein then outlined the idea of crucial case studies , within which he also included the idea of ‘most-likely’ and ‘least-likely’ cases; the essential characteristic of crucial cases being their specific theory-testing function.

Whilst Eckstein’s was an early contribution to refining the case study approach, Yin’s (2009: 47-52) more recent delineation of possible single case designs similarly assigns them roles in the applying, testing, or building of theory, as well as in the study of unique cases[3]. As a subset of the latter, however, Jack Levy (2008) notes that the advantages of idiographic cases are actually twofold. Firstly, as inductive/descriptive cases – akin to Eckstein’s configurative-idiographic cases – whereby they are highly descriptive, lacking in an explicit theoretical framework and therefore taking the form of “total history”. Secondly, they can operate as theory-guided case studies, but ones that seek only to explain or interpret a single historical episode rather than generalise beyond the case. Not only does this therefore incorporate ‘single-outcome’ studies concerned with establishing causal inference (Gerring, 2006b), it also provides room for the more postmodern approaches within IR theory, such as discourse analysis, that may have developed a distinct methodology but do not seek traditional social scientific forms of explanation.

Applying specifically to the state of the field in contemporary IR, Bennett and Elman identify a ‘third generation’ of mainstream qualitative scholars – rooted in a pragmatic scientific realist epistemology and advocating a pluralistic approach to methodology – that have, over the last fifteen years, “revised or added to essentially every aspect of traditional case study research methods” (2010: 502). They identify ‘process tracing’ as having emerged from this as a central method of within-case analysis. As Bennett and Checkel observe, this carries the advantage of offering a methodologically rigorous “analysis of evidence on processes, sequences, and conjunctures of events within a case, for the purposes of either developing or testing hypotheses about causal mechanisms that might causally explain the case” (2012: 10).

Harnessing various methods, process tracing may entail the inductive use of evidence from within a case to develop explanatory hypotheses, and deductive examination of the observable implications of hypothesised causal mechanisms to test their explanatory capability[4]. It involves providing not only a coherent explanation of the key sequential steps in a hypothesised process, but also sensitivity to alternative explanations as well as potential biases in the available evidence (Bennett and Elman 2010: 503-504). John Owen (1994), for example, demonstrates the advantages of process tracing in analysing whether the causal factors underpinning democratic peace theory are – as liberalism suggests – not epiphenomenal, but variously normative, institutional, or some given combination of the two or other unexplained mechanism inherent to liberal states. Within-case process tracing has also been identified as advantageous in addressing the complexity of path-dependent explanations and critical junctures – as for example with the development of political regime types – and their constituent elements of causal possibility, contingency, closure, and constraint (Bennett and Elman, 2006b).

Bennett and Elman (2010: 505-506) also identify the advantages of single case studies that are implicitly comparative: deviant, most-likely, least-likely, and crucial cases. Of these, so-called deviant cases are those whose outcome does not fit with prior theoretical expectations or wider empirical patterns – again, the use of inductive process tracing has the advantage of potentially generating new hypotheses from these, either particular to that individual case or potentially generalisable to a broader population. A classic example here is that of post-independence India as an outlier to the standard modernisation theory of democratisation, which holds that higher levels of socio-economic development are typically required for the transition to, and consolidation of, democratic rule (Lipset, 1959; Diamond, 1992). Absent these factors, MacMillan’s single case study analysis (2008) suggests the particularistic importance of the British colonial heritage, the ideology and leadership of the Indian National Congress, and the size and heterogeneity of the federal state.

Most-likely cases, as per Eckstein above, are those in which a theory is to be considered likely to provide a good explanation if it is to have any application at all, whereas least-likely cases are ‘tough test’ ones in which the posited theory is unlikely to provide good explanation (Bennett and Elman, 2010: 505). Levy (2008) neatly refers to the inferential logic of the least-likely case as the ‘Sinatra inference’ – if a theory can make it here, it can make it anywhere. Conversely, if a theory cannot pass a most-likely case, it is seriously impugned. Single case analysis can therefore be valuable for the testing of theoretical propositions, provided that predictions are relatively precise and measurement error is low (Levy, 2008: 12-13). As Gerring rightly observes of this potential for falsification:

“a positivist orientation toward the work of social science militates toward a greater appreciation of the case study format, not a denigration of that format, as is usually supposed” (Gerring, 2007: 247, emphasis added).

In summary, the various forms of single case study analysis can – through the application of multiple qualitative and/or quantitative research methods – provide a nuanced, empirically-rich, holistic account of specific phenomena. This may be particularly appropriate for those phenomena that are simply less amenable to more superficial measures and tests (or indeed any substantive form of quantification) as well as those for which our reasons for understanding and/or explaining them are irreducibly subjective – as, for example, with many of the normative and ethical issues associated with the practice of international relations. From various epistemological and analytical standpoints, single case study analysis can incorporate both idiographic sui generis cases and, where the potential for generalisation may exist, nomothetic case studies suitable for the testing and building of causal hypotheses. Finally, it should not be ignored that a signal advantage of the case study – with particular relevance to international relations – also exists at a more practical rather than theoretical level. This is, as Eckstein noted, “that it is economical for all resources: money, manpower, time, effort… especially important, of course, if studies are inherently costly, as they are if units are complex collective individuals ” (1975: 149-150, emphasis added).

Limitations

Single case study analysis has, however, been subject to a number of criticisms, the most common of which concern the inter-related issues of methodological rigour, researcher subjectivity, and external validity. With regard to the first point, the prototypical view here is that of Zeev Maoz (2002: 164-165), who suggests that “the use of the case study absolves the author from any kind of methodological considerations. Case studies have become in many cases a synonym for freeform research where anything goes”. The absence of systematic procedures for case study research is something that Yin (2009: 14-15) sees as traditionally the greatest concern due to a relative absence of methodological guidelines. As the previous section suggests, this critique seems somewhat unfair; many contemporary case study practitioners – and representing various strands of IR theory – have increasingly sought to clarify and develop their methodological techniques and epistemological grounding (Bennett and Elman, 2010: 499-500).

A second issue, again also incorporating issues of construct validity, concerns that of the reliability and replicability of various forms of single case study analysis. This is usually tied to a broader critique of qualitative research methods as a whole. However, whereas the latter obviously tend toward an explicitly-acknowledged interpretive basis for meanings, reasons, and understandings:

“quantitative measures appear objective, but only so long as we don’t ask questions about where and how the data were produced… pure objectivity is not a meaningful concept if the goal is to measure intangibles [as] these concepts only exist because we can interpret them” (Berg and Lune, 2010: 340).

The question of researcher subjectivity is a valid one, and it may be intended only as a methodological critique of what are obviously less formalised and researcher-independent methods (Verschuren, 2003). Owen (1994) and Layne’s (1994) contradictory process tracing results of interdemocratic war-avoidance during the Anglo-American crisis of 1861 to 1863 – from liberal and realist standpoints respectively – are a useful example. However, it does also rest on certain assumptions that can raise deeper and potentially irreconcilable ontological and epistemological issues. There are, regardless, plenty such as Bent Flyvbjerg (2006: 237) who suggest that the case study contains no greater bias toward verification than other methods of inquiry, and that “on the contrary, experience indicates that the case study contains a greater bias toward falsification of preconceived notions than toward verification”.

The third and arguably most prominent critique of single case study analysis is the issue of external validity or generalisability. How is it that one case can reliably offer anything beyond the particular? “We always do better (or, in the extreme, no worse) with more observation as the basis of our generalization”, as King et al write; “in all social science research and all prediction, it is important that we be as explicit as possible about the degree of uncertainty that accompanies out prediction” (1994: 212). This is an unavoidably valid criticism. It may be that theories which pass a single crucial case study test, for example, require rare antecedent conditions and therefore actually have little explanatory range. These conditions may emerge more clearly, as Van Evera (1997: 51-54) notes, from large-N studies in which cases that lack them present themselves as outliers exhibiting a theory’s cause but without its predicted outcome. As with the case of Indian democratisation above, it would logically be preferable to conduct large-N analysis beforehand to identify that state’s non-representative nature in relation to the broader population.

There are, however, three important qualifiers to the argument about generalisation that deserve particular mention here. The first is that with regard to an idiographic single-outcome case study, as Eckstein notes, the criticism is “mitigated by the fact that its capability to do so [is] never claimed by its exponents; in fact it is often explicitly repudiated” (1975: 134). Criticism of generalisability is of little relevance when the intention is one of particularisation. A second qualifier relates to the difference between statistical and analytical generalisation; single case studies are clearly less appropriate for the former but arguably retain significant utility for the latter – the difference also between explanatory and exploratory, or theory-testing and theory-building, as discussed above. As Gerring puts it, “theory confirmation/disconfirmation is not the case study’s strong suit” (2004: 350). A third qualification relates to the issue of case selection. As Seawright and Gerring (2008) note, the generalisability of case studies can be increased by the strategic selection of cases. Representative or random samples may not be the most appropriate, given that they may not provide the richest insight (or indeed, that a random and unknown deviant case may appear). Instead, and properly used , atypical or extreme cases “often reveal more information because they activate more actors… and more basic mechanisms in the situation studied” (Flyvbjerg, 2006). Of course, this also points to the very serious limitation, as hinted at with the case of India above, that poor case selection may alternatively lead to overgeneralisation and/or grievous misunderstandings of the relationship between variables or processes (Bennett and Elman, 2006a: 460-463).

As Tim May (2011: 226) notes, “the goal for many proponents of case studies […] is to overcome dichotomies between generalizing and particularizing, quantitative and qualitative, deductive and inductive techniques”. Research aims should drive methodological choices, rather than narrow and dogmatic preconceived approaches. As demonstrated above, there are various advantages to both idiographic and nomothetic single case study analyses – notably the empirically-rich, context-specific, holistic accounts that they have to offer, and their contribution to theory-building and, to a lesser extent, that of theory-testing. Furthermore, while they do possess clear limitations, any research method involves necessary trade-offs; the inherent weaknesses of any one method, however, can potentially be offset by situating them within a broader, pluralistic mixed-method research strategy. Whether or not single case studies are used in this fashion, they clearly have a great deal to offer.

References 

Bennett, A. and Checkel, J. T. (2012) ‘Process Tracing: From Philosophical Roots to Best Practice’, Simons Papers in Security and Development, No. 21/2012, School for International Studies, Simon Fraser University: Vancouver.

Bennett, A. and Elman, C. (2006a) ‘Qualitative Research: Recent Developments in Case Study Methods’, Annual Review of Political Science , 9, 455-476.

Bennett, A. and Elman, C. (2006b) ‘Complex Causal Relations and Case Study Methods: The Example of Path Dependence’, Political Analysis , 14, 3, 250-267.

Bennett, A. and Elman, C. (2007) ‘Case Study Methods in the International Relations Subfield’, Comparative Political Studies , 40, 2, 170-195.

Bennett, A. and Elman, C. (2010) Case Study Methods. In C. Reus-Smit and D. Snidal (eds) The Oxford Handbook of International Relations . Oxford University Press: Oxford. Ch. 29.

Berg, B. and Lune, H. (2012) Qualitative Research Methods for the Social Sciences . Pearson: London.

Bryman, A. (2012) Social Research Methods . Oxford University Press: Oxford.

David, M. and Sutton, C. D. (2011) Social Research: An Introduction . SAGE Publications Ltd: London.

Diamond, J. (1992) ‘Economic development and democracy reconsidered’, American Behavioral Scientist , 35, 4/5, 450-499.

Eckstein, H. (1975) Case Study and Theory in Political Science. In R. Gomm, M. Hammersley, and P. Foster (eds) Case Study Method . SAGE Publications Ltd: London.

Flyvbjerg, B. (2006) ‘Five Misunderstandings About Case-Study Research’, Qualitative Inquiry , 12, 2, 219-245.

Geertz, C. (1973) The Interpretation of Cultures: Selected Essays by Clifford Geertz . Basic Books Inc: New York.

Gerring, J. (2004) ‘What is a Case Study and What Is It Good for?’, American Political Science Review , 98, 2, 341-354.

Gerring, J. (2006a) Case Study Research: Principles and Practices . Cambridge University Press: Cambridge.

Gerring, J. (2006b) ‘Single-Outcome Studies: A Methodological Primer’, International Sociology , 21, 5, 707-734.

Gerring, J. (2007) ‘Is There a (Viable) Crucial-Case Method?’, Comparative Political Studies , 40, 3, 231-253.

King, G., Keohane, R. O. and Verba, S. (1994) Designing Social Inquiry: Scientific Inference in Qualitative Research . Princeton University Press: Chichester.

Layne, C. (1994) ‘Kant or Cant: The Myth of the Democratic Peace’, International Security , 19, 2, 5-49.

Levy, J. S. (2008) ‘Case Studies: Types, Designs, and Logics of Inference’, Conflict Management and Peace Science , 25, 1-18.

Lipset, S. M. (1959) ‘Some Social Requisites of Democracy: Economic Development and Political Legitimacy’, The American Political Science Review , 53, 1, 69-105.

Lyotard, J-F. (1984) The Postmodern Condition: A Report on Knowledge . University of Minnesota Press: Minneapolis.

MacMillan, A. (2008) ‘Deviant Democratization in India’, Democratization , 15, 4, 733-749.

Maoz, Z. (2002) Case study methodology in international studies: from storytelling to hypothesis testing. In F. P. Harvey and M. Brecher (eds) Evaluating Methodology in International Studies . University of Michigan Press: Ann Arbor.

May, T. (2011) Social Research: Issues, Methods and Process . Open University Press: Maidenhead.

Owen, J. M. (1994) ‘How Liberalism Produces Democratic Peace’, International Security , 19, 2, 87-125.

Seawright, J. and Gerring, J. (2008) ‘Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options’, Political Research Quarterly , 61, 2, 294-308.

Stake, R. E. (2008) Qualitative Case Studies. In N. K. Denzin and Y. S. Lincoln (eds) Strategies of Qualitative Inquiry . Sage Publications: Los Angeles. Ch. 17.

Van Evera, S. (1997) Guide to Methods for Students of Political Science . Cornell University Press: Ithaca.

Verschuren, P. J. M. (2003) ‘Case study as a research strategy: some ambiguities and opportunities’, International Journal of Social Research Methodology , 6, 2, 121-139.

Yin, R. K. (2009) Case Study Research: Design and Methods . SAGE Publications Ltd: London.

[1] The paper follows convention by differentiating between ‘International Relations’ as the academic discipline and ‘international relations’ as the subject of study.

[2] There is some similarity here with Stake’s (2008: 445-447) notion of intrinsic cases, those undertaken for a better understanding of the particular case, and instrumental ones that provide insight for the purposes of a wider external interest.

[3] These may be unique in the idiographic sense, or in nomothetic terms as an exception to the generalising suppositions of either probabilistic or deterministic theories (as per deviant cases, below).

[4] Although there are “philosophical hurdles to mount”, according to Bennett and Checkel, there exists no a priori reason as to why process tracing (as typically grounded in scientific realism) is fundamentally incompatible with various strands of positivism or interpretivism (2012: 18-19). By extension, it can therefore be incorporated by a range of contemporary mainstream IR theories.

— Written by: Ben Willis Written at: University of Plymouth Written for: David Brockington Date written: January 2013

Further Reading on E-International Relations

• Identity in International Conflicts: A Case Study of the Cuban Missile Crisis
• Imperialism’s Legacy in the Study of Contemporary Politics: The Case of Hegemonic Stability Theory
• Recreating a Nation’s Identity Through Symbolism: A Chinese Case Study
• Ontological Insecurity: A Case Study on Israeli-Palestinian Conflict in Jerusalem
• Terrorists or Freedom Fighters: A Case Study of ETA
• A Critical Assessment of Eco-Marxism: A Ghanaian Case Study

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Single-Case Design, Analysis, and Quality Assessment for Intervention Research

Michele a. lobo.

1 Biomechanics & Movement Science Program, Department of Physical Therapy, University of Delaware, Newark, DE, USA

Mariola Moeyaert

2 Division of Educational Psychology & Methodology, State University of New York at Albany, Albany, NY, USA

Andrea Baraldi Cunha

Iryna babik, background and purpose.

The purpose of this article is to describe single-case studies, and contrast them with case studies and randomized clinical trials. We will highlight current research designs, analysis techniques, and quality appraisal tools relevant for single-case rehabilitation research.

Summary of Key Points

Single-case studies can provide a viable alternative to large group studies such as randomized clinical trials. Single case studies involve repeated measures, and manipulation of and independent variable. They can be designed to have strong internal validity for assessing causal relationships between interventions and outcomes, and external validity for generalizability of results, particularly when the study designs incorporate replication, randomization, and multiple participants. Single case studies should not be confused with case studies/series (ie, case reports), which are reports of clinical management of one patient or a small series of patients.

Recommendations for Clinical Practice

When rigorously designed, single-case studies can be particularly useful experimental designs in a variety of situations, even when researcher resources are limited, studied conditions have low incidences, or when examining effects of novel or expensive interventions. Readers will be directed to examples from the published literature in which these techniques have been discussed, evaluated for quality, and implemented.

Introduction

The purpose of this article is to present current tools and techniques relevant for single-case rehabilitation research. Single-case (SC) studies have been identified by a variety of names, including “n of 1 studies” and “single-subject” studies. The term “single-case study” is preferred over the previously mentioned terms because previous terms suggest these studies include only one participant. In fact, as will be discussed below, for purposes of replication and improved generalizability, the strongest SC studies commonly include more than one participant.

A SC study should not be confused with a “case study/series “ (also called “case report”. In a typical case study/series, a single patient or small series of patients is involved, but there is not a purposeful manipulation of an independent variable, nor are there necessarily repeated measures. Most case studies/series are reported in a narrative way while results of SC studies are presented numerically or graphically. 1 , 2 This article defines SC studies, contrasts them with randomized clinical trials, discusses how they can be used to scientifically test hypotheses, and highlights current research designs, analysis techniques, and quality appraisal tools that may be useful for rehabilitation researchers.

In SC studies, measurements of outcome (dependent variables) are recorded repeatedly for individual participants across time and varying levels of an intervention (independent variables). 1 – 5 These varying levels of intervention are referred to as “phases” with one phase serving as a baseline or comparison, so each participant serves as his/her own control. 2 In contrast to case studies and case series in which participants are observed across time without experimental manipulation of the independent variable, SC studies employ systematic manipulation of the independent variable to allow for hypothesis testing. 1 , 6 As a result, SC studies allow for rigorous experimental evaluation of intervention effects and provide a strong basis for establishing causal inferences. Advances in design and analysis techniques for SC studies observed in recent decades have made SC studies increasingly popular in educational and psychological research. Yet, the authors believe SC studies have been undervalued in rehabilitation research, where randomized clinical trials (RCTs) are typically recommended as the optimal research design to answer questions related to interventions. 7 In reality, there are advantages and disadvantages to both SC studies and RCTs that should be carefully considered in order to select the best design to answer individual research questions. While there are a variety of other research designs that could be utilized in rehabilitation research, only SC studies and RCTs are discussed here because SC studies are the focus of this article and RCTs are the most highly recommended design for intervention studies. 7

When designed and conducted properly, RCTs offer strong evidence that changes in outcomes may be related to provision of an intervention. However, RCTs require monetary, time, and personnel resources that many researchers, especially those in clinical settings, may not have available. 8 RCTs also require access to large numbers of consenting participants that meet strict inclusion and exclusion criteria that can limit variability of the sample and generalizability of results. 9 The requirement for large participant numbers may make RCTs difficult to perform in many settings, such as rural and suburban settings, and for many populations, such as those with diagnoses marked by lower prevalence. 8 To rely exclusively on RCTs has the potential to result in bodies of research that are skewed to address the needs of some individuals while neglecting the needs of others. RCTs aim to include a large number of participants and to use random group assignment to create study groups that are similar to one another in terms of all potential confounding variables, but it is challenging to identify all confounding variables. Finally, the results of RCTs are typically presented in terms of group means and standard deviations that may not represent true performance of any one participant. 10 This can present as a challenge for clinicians aiming to translate and implement these group findings at the level of the individual.

SC studies can provide a scientifically rigorous alternative to RCTs for experimentally determining the effectiveness of interventions. 1 , 2 SC studies can assess a variety of research questions, settings, cases, independent variables, and outcomes. 11 There are many benefits to SC studies that make them appealing for intervention research. SC studies may require fewer resources than RCTs and can be performed in settings and with populations that do not allow for large numbers of participants. 1 , 2 In SC studies, each participant serves as his/her own comparison, thus controlling for many confounding variables that can impact outcome in rehabilitation research, such as gender, age, socioeconomic level, cognition, home environment, and concurrent interventions. 2 , 11 Results can be analyzed and presented to determine whether interventions resulted in changes at the level of the individual, the level at which rehabilitation professionals intervene. 2 , 12 When properly designed and executed, SC studies can demonstrate strong internal validity to determine the likelihood of a causal relationship between the intervention and outcomes and external validity to generalize the findings to broader settings and populations. 2 , 12 , 13

Single Case Research Designs for Intervention Research

There are a variety of SC designs that can be used to study the effectiveness of interventions. Here we discuss: 1) AB designs, 2) reversal designs, 3) multiple baseline designs, and 4) alternating treatment designs, as well as ways replication and randomization techniques can be used to improve internal validity of all of these designs. 1 – 3 , 12 – 14

The simplest of these designs is the AB Design 15 ( Figure 1 ). This design involves repeated measurement of outcome variables throughout a baseline control/comparison phase (A ) and then throughout an intervention phase (B). When possible, it is recommended that a stable level and/or rate of change in performance be observed within the baseline phase before transitioning into the intervention phase. 2 As with all SC designs, it is also recommended that there be a minimum of five data points in each phase. 1 , 2 There is no randomization or replication of the baseline or intervention phases in the basic AB design. 2 Therefore, AB designs have problems with internal validity and generalizability of results. 12 They are weak in establishing causality because changes in outcome variables could be related to a variety of other factors, including maturation, experience, learning, and practice effects. 2 , 12 Sample data from a single case AB study performed to assess the impact of Floor Play intervention on social interaction and communication skills for a child with autism 15 are shown in Figure 1 .

An example of results from a single-case AB study conducted on one participant with autism; two weeks of observation (baseline phase A) were followed by seven weeks of Floor Time Play (intervention phase B). The outcome measure Circles of Communications (reciprocal communication with two participants responding to each other verbally or nonverbally) served as a behavioral indicator of the child’s social interaction and communication skills (higher scores indicating better performance). A statistically significant improvement in Circles of Communication was found during the intervention phase as compared to the baseline. Note that although a stable baseline is recommended for SC studies, it is not always possible to satisfy this requirement, as you will see in Figures 1 – 4 . Data were extracted from Dionne and Martini (2011) 15 utilizing Rohatgi’s WebPlotDigitizer software. 78

If an intervention does not have carry-over effects, it is recommended to use a Reversal Design . 2 For example, a reversal A 1 BA 2 design 16 ( Figure 2 ) includes alternation of the baseline and intervention phases, whereas a reversal A 1 B 1 A 2 B 2 design 17 ( Figure 3 ) consists of alternation of two baseline (A 1 , A 2 ) and two intervention (B 1 , B 2 ) phases. Incorporating at least four phases in the reversal design (i.e., A 1 B 1 A 2 B 2 or A 1 B 1 A 2 B 2 A 3 B 3 …) allows for a stronger determination of a causal relationship between the intervention and outcome variables, because the relationship can be demonstrated across at least three different points in time – change in outcome from A 1 to B 1 , from B 1 to A 2 , and from A 2 to B 2 . 18 Before using this design, however, researchers must determine that it is safe and ethical to withdraw the intervention, especially in cases where the intervention is effective and necessary. 12

An example of results from a single-case A 1 BA 2 study conducted on eight participants with stable multiple sclerosis (data on three participants were used for this example). Four weeks of observation (baseline phase A 1 ) were followed by eight weeks of core stability training (intervention phase B), then another four weeks of observation (baseline phase A 2 ). Forward functional reach test (the maximal distance the participant can reach forward or lateral beyond arm’s length, maintaining a fixed base of support in the standing position; higher scores indicating better performance) significantly improved during intervention for Participants 1 and 3 without further improvement observed following withdrawal of the intervention (during baseline phase A 2 ). Data were extracted from Freeman et al. (2010) 16 utilizing Rohatgi’s WebPlotDigitizer software. 78

An example of results from a single-case A 1 B 1 A 2 B 2 study conducted on two participants with severe unilateral neglect after a right-hemisphere stroke. Two weeks of conventional treatment (baseline phases A 1, A 2 ) alternated with two weeks of visuo-spatio-motor cueing (intervention phases B 1 , B 2 ). Performance was assessed in two tests of lateral neglect, the Bells Cancellation Test (Figure A; lower scores indicating better performance) and the Line Bisection Test (Figure B; higher scores indicating better performance). There was a statistically significant intervention-related improvement in participants’ performance on the Line Bisection Test, but not on the Bells Test. Data were extracted from Samuel at al. (2000) 17 utilizing Rohatgi’s WebPlotDigitizer software. 78

A recent study used an ABA reversal SC study to determine the effectiveness of core stability training in 8 participants with multiple sclerosis. 16 During the first four weekly data collections, the researchers ensured a stable baseline, which was followed by eight weekly intervention data points, and concluded with four weekly withdrawal data points. Intervention significantly improved participants’ walking and reaching performance ( Figure 2 ). 16 This A 1 BA 2 design could have been strengthened by the addition of a second intervention phase for replication (A 1 B 1 A 2 B 2 ). For instance, a single-case A 1 B 1 A 2 B 2 withdrawal design aimed to assess the efficacy of rehabilitation using visuo-spatio-motor cueing for two participants with severe unilateral neglect after a severe right-hemisphere stroke. 17 Each phase included 8 data points. Statistically significant intervention-related improvement was observed, suggesting that visuo-spatio-motor cueing might be promising for treating individuals with very severe neglect ( Figure 3 ). 17

The reversal design can also incorporate a cross over design where each participant experiences more than one type of intervention. For instance, a B 1 C 1 B 2 C 2 design could be used to study the effects of two different interventions (B and C) on outcome measures. Challenges with including more than one intervention involve potential carry-over effects from earlier interventions and order effects that may impact the measured effectiveness of the interventions. 2 , 12 Including multiple participants and randomizing the order of intervention phase presentations are tools to help control for these types of effects. 19

When an intervention permanently changes an individual’s ability, a return to baseline performance is not feasible and reversal designs are not appropriate. Multiple Baseline Designs (MBDs) are useful in these situations ( Figure 4 ). 20 MBDs feature staggered introduction of the intervention across time: each participant is randomly assigned to one of at least 3 experimental conditions characterized by the length of the baseline phase. 21 These studies involve more than one participant, thus functioning as SC studies with replication across participants. Staggered introduction of the intervention allows for separation of intervention effects from those of maturation, experience, learning, and practice. For example, a multiple baseline SC study was used to investigate the effect of an anti-spasticity baclofen medication on stiffness in five adult males with spinal cord injury. 20 The subjects were randomly assigned to receive 5–9 baseline data points with a placebo treatment prior to the initiation of the intervention phase with the medication. Both participants and assessors were blind to the experimental condition. The results suggested that baclofen might not be a universal treatment choice for all individuals with spasticity resulting from a traumatic spinal cord injury ( Figure 4 ). 20

An example of results from a single-case multiple baseline study conducted on five participants with spasticity due to traumatic spinal cord injury. Total duration of data collection was nine weeks. The first participant was switched from placebo treatment (baseline) to baclofen treatment (intervention) after five data collection sessions, whereas each consecutive participant was switched to baclofen intervention at the subsequent sessions through the ninth session. There was no statistically significant effect of baclofen on viscous stiffness at the ankle joint. Data were extracted from Hinderer at al. (1990) 20 utilizing Rohatgi’s WebPlotDigitizer software. 78

The impact of two or more interventions can also be assessed via Alternating Treatment Designs (ATDs) . In ATDs, after establishing the baseline, the experimenter exposes subjects to different intervention conditions administered in close proximity for equal intervals ( Figure 5 ). 22 ATDs are prone to “carry-over effects” when the effects of one intervention influence the observed outcomes of another intervention. 1 As a result, such designs introduce unique challenges when attempting to determine the effects of any one intervention and have been less commonly utilized in rehabilitation. An ATD was used to monitor disruptive behaviors in the school setting throughout a baseline followed by an alternating treatment phase with randomized presentation of a control condition or an exercise condition. 23 Results showed that 30 minutes of moderate to intense physical activity decreased behavioral disruptions through 90 minutes after the intervention. 23 An ATD was also used to compare the effects of commercially available and custom-made video prompts on the performance of multi-step cooking tasks in four participants with autism. 22 Results showed that participants independently performed more steps with the custom-made video prompts ( Figure 5 ). 22

An example of results from a single case alternating treatment study conducted on four participants with autism (data on two participants were used for this example). After the observation phase (baseline), effects of commercially available and custom-made video prompts on the performance of multi-step cooking tasks were identified (treatment phase), after which only the best treatment was used (best treatment phase). Custom-made video prompts were most effective for improving participants’ performance of multi-step cooking tasks. Data were extracted from Mechling at al. (2013) 22 utilizing Rohatgi’s WebPlotDigitizer software. 78

Regardless of the SC study design, replication and randomization should be incorporated when possible to improve internal and external validity. 11 The reversal design is an example of replication across study phases. The minimum number of phase replications needed to meet quality standards is three (A 1 B 1 A 2 B 2 ), but having four or more replications is highly recommended (A 1 B 1 A 2 B 2 A 3 …). 11 , 14 In cases when interventions aim to produce lasting changes in participants’ abilities, replication of findings may be demonstrated by replicating intervention effects across multiple participants (as in multiple-participant AB designs), or across multiple settings, tasks, or service providers. When the results of an intervention are replicated across multiple reversals, participants, and/or contexts, there is an increased likelihood a causal relationship exists between the intervention and the outcome. 2 , 12

Randomization should be incorporated in SC studies to improve internal validity and the ability to assess for causal relationships among interventions and outcomes. 11 In contrast to traditional group designs, SC studies often do not have multiple participants or units that can be randomly assigned to different intervention conditions. Instead, in randomized phase-order designs , the sequence of phases is randomized. Simple or block randomization is possible. For example, with simple randomization for an A 1 B 1 A 2 B 2 design, the A and B conditions are treated as separate units and are randomly assigned to be administered for each of the pre-defined data collection points. As a result, any combination of A-B sequences is possible without restrictions on the number of times each condition is administered or regard for repetitions of conditions (e.g., A 1 B 1 B 2 A 2 B 3 B 4 B 5 A 3 B 6 A 4 A 5 A 6 ). With block randomization for an A 1 B 1 A 2 B 2 design, two conditions (e.g., A and B) would be blocked into a single unit (AB or BA), randomization of which to different time periods would ensure that each condition appears in the resulting sequence more than two times (e.g., A 1 B 1 B 2 A 2 A 3 B 3 A 4 B 4 ). Note that AB and reversal designs require that the baseline (A) always precedes the first intervention (B), which should be accounted for in the randomization scheme. 2 , 11

In randomized phase start-point designs , the lengths of the A and B phases can be randomized. 2 , 11 , 24 – 26 For example, for an AB design, researchers could specify the number of time points at which outcome data will be collected, (e.g., 20), define the minimum number of data points desired in each phase (e.g., 4 for A, 3 for B), and then randomize the initiation of the intervention so that it occurs anywhere between the remaining time points (points 5 and 17 in the current example). 27 , 28 For multiple-baseline designs, a dual-randomization, or “regulated randomization” procedure has been recommended. 29 If multiple-baseline randomization depends solely on chance, it could be the case that all units are assigned to begin intervention at points not really separated in time. 30 Such randomly selected initiation of the intervention would result in the drastic reduction of the discriminant and internal validity of the study. 29 To eliminate this issue, investigators should first specify appropriate intervals between the start points for different units, then randomly select from those intervals, and finally randomly assign each unit to a start point. 29

Single Case Analysis Techniques for Intervention Research

The What Works Clearinghouse (WWC) single-case design technical documentation provides an excellent overview of appropriate SC study analysis techniques to evaluate the effectiveness of intervention effects. 1 , 18 First, visual analyses are recommended to determine whether there is a functional relation between the intervention and the outcome. Second, if evidence for a functional effect is present, the visual analysis is supplemented with quantitative analysis methods evaluating the magnitude of the intervention effect. Third, effect sizes are combined across cases to estimate overall average intervention effects which contributes to evidence-based practice, theory, and future applications. 2 , 18

Visual Analysis

Traditionally, SC study data are presented graphically. When more than one participant engages in a study, a spaghetti plot showing all of their data in the same figure can be helpful for visualization. Visual analysis of graphed data has been the traditional method for evaluating treatment effects in SC research. 1 , 12 , 31 , 32 The visual analysis involves evaluating level, trend, and stability of the data within each phase (i.e., within-phase data examination) followed by examination of the immediacy of effect, consistency of data patterns, and overlap of data between baseline and intervention phases (i.e., between-phase comparisons). When the changes (and/or variability) in level are in the desired direction, are immediate, readily discernible, and maintained over time, it is concluded that the changes in behavior across phases result from the implemented treatment and are indicative of improvement. 33 Three demonstrations of an intervention effect are necessary for establishing a functional relation. 1

Within-phase examination

Level, trend, and stability of the data within each phase are evaluated. Mean and/or median can be used to report the level, and trend can be evaluated by determining whether the data points are monotonically increasing or decreasing. Within-phase stability can be evaluated by calculating the percentage of data points within 15% of the phase median (or mean). The stability criterion is satisfied if about 85% (80% – 90%) of the data in a phase fall within a 15% range of the median (or average) of all data points for that phase. 34

Between-phase examination

Immediacy of effect, consistency of data patterns, and overlap of data between baseline and intervention phases are evaluated next. For this, several nonoverlap indices have been proposed that all quantify the proportion of measurements in the intervention phase not overlapping with the baseline measurements. 35 Nonoverlap statistics are typically scaled as percent from 0 to 100, or as a proportion from 0 to 1. Here, we briefly discuss the Nonoverlap of All Pairs ( NAP ), 36 the Extended Celeration Line ( ECL ), the Improvement Rate Difference ( IRD) , 37 and the TauU and the TauU-adjusted, TauU adj , 35 as these are the most recent and complete techniques. We also examine the Percentage of Nonoverlapping Data ( PND ) 38 and the Two Standard Deviations Band Method, as these are frequently used techniques. In addition, we include the Percentage of Nonoverlapping Corrected Data ( PNCD ) – an index applying to the PND after controlling for baseline trend. 39

Nonoverlap of all pairs (NAP)

Each baseline observation can be paired with each intervention phase observation to make n pairs (i.e., N = n A * n B ). Count the number of overlapping pairs, n o , counting all ties as 0.5. Then define the percent of the pairs that show no overlap. Alternatively, one can count the number of positive (P), negative (N), and tied (T) pairs 2 , 36 :

Extended Celeration Line (ECL)

ECL or split middle line allows control for a positive Phase A trend. Nonoverlap is defined as the proportion of Phase B ( n b ) data that are above the median trend plotted from Phase A data ( n B< sub > Above Median trend A </ sub > ), but then extended into Phase B: ECL = n B Above Median trend A n b ∗ 100

As a consequence, this method depends on a straight line and makes an assumption of linearity in the baseline. 2 , 12

Improvement rate difference (IRD)

This analysis is conceptualized as the difference in improvement rates (IR) between baseline ( IR B ) and intervention phases ( IR T ). 38 The IR for each phase is defined as the number of “improved data points” divided by the total data points in that phase. IRD, commonly employed in medical group research under the name of “risk reduction” or “risk difference” attempts to provide an intuitive interpretation for nonoverlap and to make use of an established, respected effect size, IR B - IR B , or the difference between two proportions. 37

TauU and TauU adj

Each baseline observation can be paired with each intervention phase observation to make n pairs (i.e., n = n A * n B ). Count the number of positive (P), negative (N), and tied (T) pairs, and use the following formula: TauU = P - N P + N + τ

The TauU adj is an adjustment of TauU for monotonic trend in baseline. Each baseline observation can be paired with each intervention phase observation to make n pairs (i.e., n = n A * n B ). Each baseline observation can be paired with all later baseline observations (n A *(n A -1)/2). 2 , 35 Then the baseline trend can be computed: TauU adf = P - N - S trend P + N + τ ; S trend = P A – NA

Online calculators might assist researchers in obtaining the TauU and TauU adjusted coefficients ( http://www.singlecaseresearch.org/calculators/tau-u ).

Percentage of nonoverlapping data (PND)

If anticipating an increase in the outcome, locate the highest data point in the baseline phase and then calculate the percent of the intervention phase data points that exceed it. If anticipating a decrease in the outcome, find the lowest data point in the baseline phase and then calculate the percent of the treatment phase data points that are below it: PND = n B Overlap A n b ∗ 100 . A PND < 50 would mark no observed effect, PND = 50–70 signifies a questionable effect, and PND > 70 suggests the intervention was effective. 40 The percentage of nonoverlapping (PNDC) corrected was proposed in 2009 as an extension of the PND. 39 Prior to applying the PND, a data correction procedure is applied eliminating pre-existing baseline trend. 38

Two Standard Deviation Band Method

When the stability criterion described above is met within phases, it is possible to apply the two standard deviation band method. 12 , 41 First, the mean of the data for a specific condition is calculated and represented with a solid line. In the next step, the standard deviation of the same data is computed and two dashed lines are represented: one located two standard deviations above the mean and the other – two standard deviations below. For normally distributed data, few points (less than 5%) are expected to be outside the two standard deviation bands if there is no change in the outcome score due to the intervention. However, this method is not considered a formal statistical procedure, as the data cannot typically be assumed to be normal, continuous, or independent. 41

Statistical Analysis

If the visual analysis indicates a functional relationship (i.e., three demonstrations of the effectiveness of the intervention effect), it is recommended to proceed with the quantitative analyses, reflecting the magnitude of the intervention effect. First, effect sizes are calculated for each participant (individual-level analysis). Moreover, if the research interest lies in the generalizability of the effect size across participants, effect sizes can be combined across cases to achieve an overall average effect size estimate (across-case effect size).

Note that quantitative analysis methods are still being developed in the domain of SC research 1 and statistical challenges of producing an acceptable measure of treatment effect remain. 14 , 42 , 43 Therefore, the WWC standards strongly recommend conducting sensitivity analysis and reporting multiple effect size estimators. If consistency across different effect size estimators is identified, there is stronger evidence for the effectiveness of the treatment. 1 , 18

Individual-level effect size analysis

The most common effect sizes recommended for SC analysis are: 1) standardized mean difference Cohen’s d ; 2) standardized mean difference with correction for small sample sizes Hedges’ g ; and 3) the regression-based approach which has the most potential and is strongly recommended by the WWC standards. 1 , 44 , 45 Cohen’s d can be calculated using following formula: d = X A ¯ - X B ¯ s p , with X A ¯ being the baseline mean, X B ¯ being the treatment mean, and s p indicating the pooled within-case standard deviation. Hedges’ g is an extension of Cohen’s d , recommended in the context of SC studies as it corrects for small sample sizes. The piecewise regression-based approach does not only reflect the immediate intervention effect, but also the intervention effect across time:

i stands for the measurement occasion ( i = 0, 1,… I ). The dependent variable is regressed on a time indicator, T , which is centered around the first observation of the intervention phase, D , a dummy variable for the intervention phase, and an interaction term of these variables. The equation shows that the expected score, Ŷ i , equals β 0 + β 1 T i in the baseline phase, and ( β 0 + β 2 ) + ( β 1 + β 3 ) T i in the intervention phase. β 0 , therefore, indicates the expected baseline level at the start of the intervention phase (when T = 0), whereas β 1 marks the linear time trend in the baseline scores. The coefficient β 2 can then be interpreted as an immediate effect of the intervention on the outcome, whereas β 3 signifies the effect of the intervention across time. The e i ’s are residuals assumed to be normally distributed around a mean of zero with a variance of σ e 2 . The assumption of independence of errors is usually not met in the context of SC studies because repeated measures are obtained within a person. As a consequence, it can be the case that the residuals are autocorrelated, meaning that errors closer in time are more related to each other compared to errors further away in time. 46 – 48 As a consequence, a lag-1 autocorrelation is appropriate (taking into account the correlation between two consecutive errors: e i and e i –1 ; for more details see Verbeke & Molenberghs, (2000). 49 In Equation 1 , ρ indicates the autocorrelation parameter. If ρ is positive, the errors closer in time are more similar; if ρ is negative, the errors closer in time are more different, and if ρ equals zero, there is no correlation between the errors.

Across-case effect sizes

Two-level modeling to estimate the intervention effects across cases can be used to evaluate across-case effect sizes. 44 , 45 , 50 Multilevel modeling is recommended by the WWC standards because it takes the hierarchical nature of SC studies into account: measurements are nested within cases and cases, in turn, are nested within studies. By conducting a multilevel analysis, important research questions can be addressed (which cannot be answered by single-level analysis of SC study data), such as: 1) What is the magnitude of the average treatment effect across cases? 2) What is the magnitude and direction of the case-specific intervention effect? 3) How much does the treatment effect vary within cases and across cases? 4) Does a case and/or study level predictor influence the treatment’s effect? The two-level model has been validated in previous research using extensive simulation studies. 45 , 46 , 51 The two-level model appears to have sufficient power (> .80) to detect large treatment effects in at least six participants with six measurements. 21

Furthermore, to estimate the across-case effect sizes, the HPS (Hedges, Pustejovsky, and Shadish) , or single-case educational design ( SCEdD)-specific mean difference, index can be calculated. 52 This is a standardized mean difference index specifically designed for SCEdD data, with the aim of making it comparable to Cohen’s d of group-comparison designs. The standard deviation takes into account both within-participant and between-participant variability, and is typically used to get an across-case estimator for a standardized change in level. The advantage of using the HPS across-case effect size estimator is that it is directly comparable with Cohen’s d for group comparison research, thus enabling the use of Cohen’s (1988) benchmarks. 53

Valuable recommendations on SC data analyses have recently been provided. 54 , 55 They suggest that a specific SC study data analytic technique can be chosen based on: (1) the study aims and the desired quantification (e.g., overall quantification, between-phase quantifications, randomization, etc.), (2) the data characteristics as assessed by visual inspection and the assumptions one is willing to make about the data, and (3) the knowledge and computational resources. 54 , 55 Table 1 lists recommended readings and some commonly used resources related to the design and analysis of single-case studies.

Recommend readings and resources related to the design and analysis of single-case studies.

Quality Appraisal Tools for Single-Case Design Research

Quality appraisal tools are important to guide researchers in designing strong experiments and conducting high-quality systematic reviews of the literature. Unfortunately, quality assessment tools for SC studies are relatively novel, ratings across tools demonstrate variability, and there is currently no “gold standard” tool. 56 Table 2 lists important SC study quality appraisal criteria compiled from the most common scales; when planning studies or reviewing the literature, we recommend readers consider these criteria. Table 3 lists some commonly used SC quality assessment and reporting tools and references to resources where the tools can be located.

Summary of important single-case study quality appraisal criteria.

Quality assessment and reporting tools related to single-case studies.

When an established tool is required for systematic review, we recommend use of the What Works Clearinghouse (WWC) Tool because it has well-defined criteria and is developed and supported by leading experts in the SC research field in association with the Institute of Education Sciences. 18 The WWC documentation provides clear standards and procedures to evaluate the quality of SC research; it assesses the internal validity of SC studies, classifying them as “Meeting Standards”, “Meeting Standards with Reservations”, or “Not Meeting Standards”. 1 , 18 Only studies classified in the first two categories are recommended for further visual analysis. Also, WWC evaluates the evidence of effect, classifying studies into “Strong Evidence of a Causal Relation”, “Moderate Evidence of a Causal Relation”, or “No Evidence of a Causal Relation”. Effect size should only be calculated for studies providing strong or moderate evidence of a causal relation.

The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 is another useful SC research tool developed recently to improve the quality of single-case designs. 57 SCRIBE consists of a 26-item checklist that researchers need to address while reporting the results of SC studies. This practical checklist allows for critical evaluation of SC studies during study planning, manuscript preparation, and review.

Single-case studies can be designed and analyzed in a rigorous manner that allows researchers strength in assessing causal relationships among interventions and outcomes, and in generalizing their results. 2 , 12 These studies can be strengthened via incorporating replication of findings across multiple study phases, participants, settings, or contexts, and by using randomization of conditions or phase lengths. 11 There are a variety of tools that can allow researchers to objectively analyze findings from SC studies. 56 While a variety of quality assessment tools exist for SC studies, they can be difficult to locate and utilize without experience, and different tools can provide variable results. The WWC quality assessment tool is recommended for those aiming to systematically review SC studies. 1 , 18

SC studies, like all types of study designs, have a variety of limitations. First, it can be challenging to collect at least five data points in a given study phase. This may be especially true when traveling for data collection is difficult for participants, or during the baseline phase when delaying intervention may not be safe or ethical. Power in SC studies is related to the number of data points gathered for each participant so it is important to avoid having a limited number of data points. 12 , 58 Second, SC studies are not always designed in a rigorous manner and, thus, may have poor internal validity. This limitation can be overcome by addressing key characteristics that strengthen SC designs ( Table 2 ). 1 , 14 , 18 Third, SC studies may have poor generalizability. This limitation can be overcome by including a greater number of participants, or units. Fourth, SC studies may require consultation from expert methodologists and statisticians to ensure proper study design and data analysis, especially to manage issues like autocorrelation and variability of data. 2 Fifth, while it is recommended to achieve a stable level and rate of performance throughout the baseline, human performance is quite variable and can make this requirement challenging. Finally, the most important validity threat to SC studies is maturation. This challenge must be considered during the design process in order to strengthen SC studies. 1 , 2 , 12 , 58

SC studies can be particularly useful for rehabilitation research. They allow researchers to closely track and report change at the level of the individual. They may require fewer resources and, thus, can allow for high-quality experimental research, even in clinical settings. Furthermore, they provide a tool for assessing causal relationships in populations and settings where large numbers of participants are not accessible. For all of these reasons, SC studies can serve as an effective method for assessing the impact of interventions.

Acknowledgments

This research was supported by the National Institute of Health, Eunice Kennedy Shriver National Institute of Child Health & Human Development (1R21HD076092-01A1, Lobo PI) and the Delaware Economic Development Office (Grant #109).

Some of the information in this manuscript was presented at the IV Step Meeting in Columbus, OH, June 2016.

Case-Control Studies

Fall-kontrollstudier, svensk definition.

Studier som utgår från en grupp individer med en viss, fastställd sjukdom och en kontrollgrupp (jämförelsegrupp, referensgrupp) utan denna sjukdom. Sambandet mellan ett kännetecken och sjukdomen under söks genom jämförelse mellan personer med sjukdomen och personer utan med hänsyn till förekomstfrekvens eller nivåer av kännetecknet i de båda grupperna.

Engelsk definition

Comparisons that start with the identification of persons with the disease or outcome of interest and a control (comparison, referent) group without the disease or outcome of interest. The relationship of an attribute is examined by comparing both groups with regard to the frequency or levels of outcome over time.

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Engelska synonymer

Case-Control Study — Studies, Case-Control — Study, Case-Control — Case-Comparison Studies — Case Comparison Studies — Case-Comparison Study — Studies, Case-Comparison — Study, Case-Comparison — Case-Compeer Studies — Studies, Case-Compeer — Case-Referrent Studies — Case Referrent Studies — Case-Referrent Study — Studies, Case-Referrent — Study, Case-Referrent — Case-Referent Studies — Case Referent Studies — Case-Referent Study — Studies, Case-Referent — Study, Case-Referent — Case-Base Studies — Case Base Studies — Studies, Case-Base — Case Control Studies — Case Control Study — Studies, Case Control — Study, Case Control — Nested Case-Control Studies — Case-Control Studies, Nested — Case-Control Study, Nested — Nested Case Control Studies — Nested Case-Control Study — Studies, Nested Case-Control — Study, Nested Case-Control — Matched Case-Control Studies — Case-Control Studies, Matched — Case-Control Study, Matched — Matched Case Control Studies — Matched Case-Control Study — Studies, Matched Case-Control — Study, Matched Case-Control

Single case studies vs. multiple case studies: A comparative study

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Please note you do not have access to teaching notes, passive house-based energy efficiency design criteria: a case study of a residential building in cairo.

Open House International

ISSN : 0168-2601

Article publication date: 3 July 2024

The study aims to tackle Egypt's rising electricity consumption due to climate change and population growth, focusing on the building sector, which accounts for up to 60% of the issue, by developing new energy-efficient design guidelines for Egyptian buildings.

Design/methodology/approach

This study comprises six key steps. A literature review focuses on energy consumption and efficiency in buildings, monitoring a single-family building in Cairo, using Energy Plus for simulation and verification, performing multi-objective optimization, comparing energy performance between base and controlled cases, and developing a localized version of the Passive House (PH) called Energy Efficiency Design Criteria (EEDC).

The research shows that applying the (EEDC) suggested by this study can decrease energy consumption by up to 58% and decrease cooling consumption by up to 63% in residential buildings in Egypt while providing thermal comfort and reducing greenhouse gas emissions. This can benefit users, alleviate local power grid strain, contribute to Egypt's economy, and serve as a model for other countries with similar climates.

Originality/value

To date, no studies have focused on developing energy-efficient design standards tailored to the Egyptian climate and context using the Passive House Criteria concept. This study contributes to the field by identifying key principles, design details, and goal requirements needed to promote energy-efficient design standards for residential buildings in Egypt.

• Passive House Criteria (PHC)
• Energy efficiency
• Electricity consumption
• Energy efficiency design criteria (EEDC)
• Thermal comfort

Abdulfattah, H.A.M. , Fikry, A.A. and Hamed, R.E. (2024), "Passive House-based energy efficiency design criteria: a case study of a residential building in Cairo", Open House International , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/OHI-02-2024-0045

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Texas' anti-abortion heartbeat law aimed to save babies, but more infants died.

Texas lawmakers touted their heartbeat law as an effort to save lives , but the state's near-total ban on abortion appears to have triggered an increase in infant deaths, according to a new study published Monday . 

The findings in JAMA Pediatrics show that infant deaths rose after Texas’ Senate Bill 8, which banned all abortion after about six weeks from conception. S.B. 8 became Texas law in September 2021 and U.S. Supreme Court overturned the constitutional right to abortion just over nine months later, on June 24, 2022. The high court ruling in the Dobbs case prompted more than a dozen states to issue near-total bans on abortion. Observers speculate that evidence will also show increases in infant deaths in those states, akin to what Texas has seen, the study said.

“It just points to some of the devastating consequences of abortion bans that maybe people weren't thinking about when they passed these laws,” Alison Gemmill, an assistant professor at Johns Hopkins University’s Bloomberg School of Public Health who authored the study, told USA TODAY. She called the deaths following the Texas heartbeat law its “spillover effects on moms and babies.”

Abortion bans: More than 171K patients traveled out-of-state for abortions in 2023, new data shows

In the wake of the law's passage in Texas, more babies died before their first birthday, likely due to birth defects or genetic problems that wouldn't have allowed them to live, the study found. These pregnancies would typically have been terminated by abortion, according to researchers. The Texas heartbeat law does not provide exceptions for pregnancies involving such conditions. Mothers are legally obligated to carry these babies to birth under state law.

In the peer-reviewed Journal of the American Medical Association, Gemmill and researchers from Johns Hopkins and Michigan State University wrote that the Texas law was linked to "unexpected increases in infant and neonatal deaths" between 2021 and 2022. Prior research drew a correlation between the uptick in infant deaths and anti-abortion laws taking effect, however, no studies until now have attributed the fatalities directly to the laws prohibiting the termination of these pregnancies.

"Abortion care is an essential component of comprehensive healthcare, and when it is restricted, the human impacts are devastating," Wendy Davis, a senior adviser for Planned Parenthood Texas Votes, said in a statement. Davis, who filibustered for abortion rights when she was a Democratic state senator, noted that the study only covered 2022, not the results in 2023 and 2024 in the wake of a more restrictive abortion ban that came with the Dobbs decision. This "likely means the situation on the ground today is even more dire," Davis said.

Texas Gov. Greg Abbott's office did not dispute the study's findings but defended the Republican-controlled state's anti-abortion record. This effort included the 2021 heartbeat law "to save the innocent unborn, and now thousands of children have been given a chance at life," Andrew Mahaleris, a spokesperson for Abbott, said in a statement to USA TODAY. He said the governor has taken "significant action to protect the sanctity of life" and offered resources to expectant mothers "so they can choose life for their child."

Anti-abortion advocates also didn't contest the uptick in infant deaths cited in the study. Advocates for the heartbeat law and other legislation to restrict abortions say such bans protect life. They say terminating a fetus with a terminal illness is “choosing to kill that child intentionally.”

The overwhelming majority of such abortions happen before the fetus is viable. In Texas, legislation has dramatically reduced the number of abortions performed in the state.

Amy O’Donnell, a spokesperson for Texas Alliance for Life, said the study’s findings didn’t come as a surprise. She said babies born with disabilities and even fatal anomalies deserve a chance at life, even if that means a newborn dies after birth from a condition doctors anticipated would be lethal. The death of a child is not easy, she acknowledged. She noted that her nonprofit offers resources for families grieving from such losses.

“In Texas, we celebrate every unborn child's life saved. We treasure the fact that our laws are protecting women's lives,” she said. “We don't apologize for the fact that we don't support discrimination against children facing disabilities or fatal diagnoses in or out of the womb. And that's the line that we just believe should not be crossed.”

Gemmill, of Johns Hopkins, said babies that died shortly after being born with birth defects "probably caused a lot of unnecessary trauma to families."

Maternal health: Chronic hypertension has soared among pregnant women. Treatment is not keeping pace

The researchers examined death records beginning after the heartbeat law went into effect. The study created a “synthetic Texas” that simulated outcomes that would have happened had the law not been in effect and compared the numbers to national trends during that period. In 2021, 1,985 Texas infants died before their first birthday. The next year, with S.B. 8 in effect, the fatalities jumped to 2,240, a 12.9% increase that came as the U.S. experienced an overall increase of less than 2%. Deaths attributable to congenital anomalies or birth defects spiked nearly 23% in Texas compared to a 3% decrease nationally.

“It suggests that, really, this policy was responsible for this increase in infant deaths in Texas,” Gemmill said.

The study is significant because of Texas’ role as a conservative state with urban and rural areas that may reflect what happens in the rest of the U.S., according to Dr. Tracey Wilkinson, an associate professor of pediatrics and obstetrics and gynecology at the Indiana University School of Medicine. Texas has been living under restrictions longer than other states that enacted abortion bans after the Dobbs ruling.

“When people ask me why this is happening, it’s really simple,” said Wilkinson, who was not involved with the new study. “When you take away people’s ability to make decisions (about) if and when they have pregnancies, you’re going to see outcomes like increasing infant and maternal mortality.”

The study did not examine the effects of infant deaths on the health of mothers who were legally required to deliver dead babies to term, nor did it look at the mental health effects of carrying infants and delivering them, only to see them die. The study also raises but does not tackle questions about the financial cost to families of carrying and delivering terminally ill newborns. 

Gemmill is now working to understand the impact of abortion restrictions on parents of different races and ethnicities. Prior research has shown that Black mothers and babies face higher death rates than other groups.

The study reflects what Molly Duane, a senior staff attorney at the abortion rights advocacy nonprofit Center for Reproductive Rights, has seen in the courtroom arguing against Texas' laws. She recently represented women who sued the state after they were denied medical abortions. One of her clients, Samatha Casiano, was required by law to carry a child that developed without a brain. In late May, the Texas Supreme Court ruled pregnant patients must have a “life-threatening condition” in order to terminate a pregnancy.

Duane questioned the claim by anti-abortion activists that Texas is a “pro-life” state, given the study's findings. “Women are hurting, families are hurting, babies are dying, and no one in the state is taking responsibility for any of that real human suffering,” she said.

In late 2023, a U.S. Centers for Disease Control and Prevention report found increases in infant deaths for the first time in more than 20 years. The states identified in the report with increased fatalities were states that restricted abortion access, however, experts cautioned at the time that they could not say what had caused the spike in fatalities.

The Texas study went one step further, finding one state where abortion restrictions resulted in more deaths.