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

TLDR

Intent‑to‑treat analysis of longitudinal trials with dropout is challenging because standard approaches such as last‑observation‑carried‑forward or random‑dropout models have serious limitations. We propose a method that imputes post‑dropout values using an as‑treated model—leveraging known doses or plausible alternatives—before applying IT analysis. The approach creates multiple imputed datasets, analyzes each with IT classification by randomization group, combines results via Rubin’s rules, and uniquely uses distinct models for imputation and analysis, as demonstrated on a Tacrine Alzheimer’s trial.

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, 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.