title: Handling intra-cluster correlation when analyzing the effects of decision support on health care process measures.
abstract: The clinical worksite constitutes a naturally clustered environment, posing challenges in the statistical analysis of quality improvement interventions such as computerized decision support. Ignoring clustering in the analysis may lead to biased effect estimates, underestimating the variance and hence type I errors. This paper presents a secondary analysis on data from a previously published, cluster randomized trial in cardiac rehabilitation. We compared six different statistical analysis methods (weighted and unweighted t-test; adjusted χ2 test; normal and multilevel logistic regression analysis; and generalized estimation equations). There were considerable differences in both point estimates and p-values derived by the methods, and differences were larger with increasing intracluster correlation.
authors.affiliation: Dept. of Medical Informatics, Academic Medical Center, University of Amsterdam, The Netherlands. n.b.peek@amc.nl
authors.forename: Niels
authors.initials: N
authors.lastname: Peek
authors.forename: Rick
authors.initials: R
authors.lastname: Goud
authors.forename: Nicolette
authors.initials: N
authors.lastname: de Keizer
mesh_terms: D000072038:Cardiac Rehabilitation; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000453:epidemiology*; D003627:Data Interpretation, Statistical; D020000:Decision Support Systems, Clinical / Q000706:statistics & numerical data*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D009426:Netherlands / Q000453:epidemiology; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data*; D015995:Prevalence; D013223:Statistics as Topic; D016896:Treatment Outcome