Intent-to-treat analysis for longitudinal studies with drop-outs

Biometrics. 1996 Dec;52(4):1324-33.


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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Alzheimer Disease / drug therapy
  • Biometry / methods*
  • Data Interpretation, Statistical
  • Humans
  • Likelihood Functions
  • Longitudinal Studies
  • Models, Statistical
  • Nootropic Agents / administration & dosage
  • Nootropic Agents / therapeutic use
  • Patient Dropouts
  • Randomized Controlled Trials as Topic / statistics & numerical data*
  • Tacrine / administration & dosage
  • Tacrine / therapeutic use


  • Nootropic Agents
  • Tacrine