Classical analytical methods for detecting matching effects on treatment outcome.

P W Wirtz; J P Carbonari; L R Muenz; R L Stout; J S Tonigan; G J Connors

doi:10.15288/jsas.1994.s12.76

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Classical analytical methods for detecting matching effects on treatment outcome.

P W Wirtz·J P Carbonari·L R Muenz·R L Stout·J S Tonigan·G J Connors

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Abstract

This article presents a classical approach for analyzing repeated measures designs with specific application to treatment matching studies. The generic treatment matching hypothesis is formulated under the multivariate general linear model, transforming the dependent variables to account for the repeated measures structure of the data. Issues of primary importance in the use of this approach (such as correcting for inflated Type I error and robustness of statistical tests to parametric assumptions) are discussed. The article concludes with an assessment of the strengths and weaknesses of this approach compared with alternative approaches.

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Alcoholism Clinical Protocols Humans Linear Models Models, Statistical Multicenter Studies as Topic Multivariate Analysis Outcome and Process Assessment, Health Care Patient Selection Randomized Controlled Trials as Topic Journal Article Research Support, U.S. Gov't, P.H.S.Review

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P W Wirtz, J P Carbonari, L R Muenz, R L Stout, J S Tonigan, G J Connors. 1994. Classical analytical methods for detecting matching effects on treatment outcome.. https://doi.org/10.15288/jsas.1994.s12.76

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