title: Alternative analytical methods for detecting matching effects in treatment outcomes.
abstract: Project MATCH presented a unique opportunity for a team of statisticians, data analysts and content experts to come together and explore the strengths and weaknesses of the application of various statistical models to the data of the type being collected in this large trial. The following models were evaluated: multilevel models, event history models, multiple were structural equation modeling, time series models, ordinal repeated measures designs and generalized estimating equations. No one model was found to be the perfect solution and each seemed to have something to recommend it. Future research on these methods will shed light on many issues raised. It is hoped that alcohol researchers will find useful guidelines within this chapter as they plan and carry out their studies.
authors.affiliation: Department of Psychology, College of Social Sciences, University of Houston, Texas 77204-5341.
authors.forename: J P
authors.initials: JP
authors.lastname: Carbonari
authors.forename: P W
authors.initials: PW
authors.lastname: Wirtz
authors.forename: L R
authors.initials: LR
authors.lastname: Muenz
authors.forename: R L
authors.initials: RL
authors.lastname: Stout
mesh_terms: D000437:Alcoholism / Q000523:psychology / Q000534:rehabilitation*; D015982:Bias; D002985:Clinical Protocols; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data*; D010043:Outcome and Process Assessment, Health Care; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*