Intent-to-treat analysis for longitudinal clinical trials: coping with the challenge of missing values.

Drop-out is a common phenomenon in clinical trials of drug treatments involving longitudinal assessments for a fixed duration of follow-up. For these trials intent-to-treat (IT) analysis is usually preferred because time effects are seen in practice. The IT analysis mandates that all subjects randomized to a treatment arm should be included in the analysis. The purpose of the present paper is to acquaint both clinicians and statisticians with recent statistical methodological advances in handling drop-outs and their usage for IT analysis. We discuss a sensitivity analysis of 12-month outcome data to investigate the efficacy of drug therapy from a longitudinal double-blind placebo-controlled clinical trial in the maintenance therapy of geriatric major depressive illness. Outcome measures consist of monthly Hamilton depression scores. The sensitivity analysis includes endpoint analysis, last observation carried forward analysis, repeated measures models and imputation models. Imputation models are based on multiple imputations of missing responses deriving from an 'as-treated' model. The model used imputed doses from a plausible treatment scenario after drop-out and a 'propensity-adjusted' model where the imputations for the drop-outs were obtained from the adhering subjects with the same probability to remain on study (propensity) given the observed trajectory prior to withdrawal. Issues related to bias and efficiency of the estimates obtained by different analyses are discussed. We recommend a more widespread use of imputation models for the IT analysis.

RCT Entities:   Population Interventions Outcomes