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Intent-to-Treat Analysis for Longitudinal Studies with Drop-Outs

Roderick Little and Linda Yau
Biometrics
Vol. 52, No. 4 (Dec., 1996), pp. 1324-1333
DOI: 10.2307/2532847
Stable URL: http://www.jstor.org/stable/2532847
Page Count: 10
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Intent-to-Treat Analysis for Longitudinal Studies with Drop-Outs
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Abstract

We consider intent-to-treat (IT) analysis of clinical trials involving longitudinal data subject to drop-out. Common methods, such as Last Observation Carried Forward imputation or incomplete-data methods based on models that assume random dropout, have serious drawbacks in the IT setting. We propose a method that involves multiple imputation of the missing values following drop-out based on an "as treated" model, using actual dose after drop-out if this is known, or imputed doses that incorporate a variety of plausible alternative assumptions if unknown. The multiply-imputed data sets are then analyzed using IT methods, where subjects are classified by randomization group rather than by the dose actually received. Results from the multiply-imputed data sets are combined using the methods of Rubin (1987, Multiple Imputation for Nonresponse in Surveys). A novel feature of the proposed method is that the models for imputation differ from the model used for the analysis of the filled-in data. The method is applied to data on a clinical trial for Tacrine in the treatment of Alzheimer's disease.

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