Intent-to-treat analysis for longitudinal studies with drop-outs.
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, were 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.
Little
R
R
Department of Biostatistics, University of Michigan, Ann Arbor 48109, USA.
Yau
L
L
D000544:Alzheimer Disease / Q000188:drug therapy; D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D018697:Nootropic Agents / Q000008:administration & dosage / Q000627:therapeutic use; D010352:Patient Dropouts; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D013619:Tacrine / Q000008:administration & dosage / Q000627:therapeutic use
D018697:Nootropic Agents; D013619:Tacrine