A modelling strategy for the analysis of clinical trials with partly missing longitudinal data

Standard statistical analyses of randomized controlled trials with partially missing outcome data often exclude valuable information from individuals with incomplete follow-up. This may lead to biased estimates of the intervention effect and loss of precision. We consider a randomized trial with a repeatedly measured outcome, in which the value of the outcome on the final occasion is of primary interest. We propose a modelling strategy in which the model is successively extended to include baseline values of the outcome, then intermediate values of the outcome, and finally values of other outcome variables. Likelihood-based estimation of random effects models is used, allowing the incorporation of data from individuals with some missing outcomes. Each estimated intervention effect is free of non-response bias under a different missing-at-random assumption. These assumptions become more plausible as the more complex models are fitted, so we propose using the trend in estimated intervention effects to assess the nature of any non-response bias. The methods are applied to data from a trial comparing intensive case management with standard case management for severely psychotic patients. All models give similar estimates of the intervention effect and we conclude that non-response bias is likely to be small.