Cluster Randomized Trials with Treatment Noncompliance

Autor/inn/en	Jo, Booil; Asparouhov, Tihomir; Muthen, Bengt O.; Ialongo, Nicholas S.; Brown, C. Hendricks
Titel	Cluster Randomized Trials with Treatment Noncompliance
Quelle	In: Psychological Methods, 13 (2008) 1, S.1-18 (18 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1082-989X
Schlagwörter	Individual Characteristics; Intervention; Statistical Inference; Inferences; Compliance (Psychology); Evaluation Methods; Research Methodology; Environmental Influences; Correlation; Field Studies; Monte Carlo Methods; Simulation; Program Effectiveness; Computation + Suchen Sie Ihr Suchwort? Personality characteristic; Personality traits; Persönlichkeitsmerkmal; Inferential statistics; Schließende Statistik; Inference; Inferenz; Research method; Forschungsmethode; Environmental influence; Umwelteinfluss; Korrelation; Praxisforschung; Monte-Carlo-Methode; Simulation program; Simulationsprogramm
Abstract	Cluster randomized trials (CRTs) have been widely used in field experiments treating a cluster of individuals as the unit of randomization. This study focused particularly on situations where CRTs are accompanied by a common complication, namely, treatment noncompliance or, more generally, intervention nonadherence. In CRTs, compliance may be related not only to individual characteristics but also to the environment of clusters individuals belong to. Therefore, analyses ignoring the connection between compliance and clustering may not provide valid results. Although randomized field experiments often suffer from both noncompliance and clustering of the data, these features have been studied as separate rather than concurrent problems. On the basis of Monte Carlo simulations, this study demonstrated how clustering and noncompliance may affect statistical inferences and how these two complications can be accounted for simultaneously. In particular, the effect of the intervention on individuals who not only were assigned to active intervention but also abided by this intervention assignment (complier average causal effect) was the focus. For estimation of intervention effects considering noncompliance and data clustering, an ML-EM estimation method was employed. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10