Missing data in randomised controlled trials: a practical guide

Objective: Missing data are ubiquitous in clinical trials, yet recent research suggests many statisticians and investigators appear uncertain how to handle them. The objective is to set out a principled approach for handling missing data in clinical trials, and provide examples and code to facilitate its adoption. Data sources: An asthma trial from GlaxoSmithKline, a asthma trial from AstraZeneca, and a dental pain trial from GlaxoSmithKline. Methods: Part I gives a non-technical review how missing data are typically handled in clinical trials, and the issues raised by missing data. When faced with missing data, we show no analysis can avoid making additional untestable assumptions. This leads to a proposal for a systematic, principled approach for handling missing data in clinical trials, which in turn informs a critique of current Committee of Proprietary Medicinal Products guidelines for missing data, together with many of the ad-hoc statistical methods currently employed. Part II shows how primary analyses in a range of settings can be carried out under the so-called missing at random assumption. This key assumption has a central role in underpinning the most important classes of primary analysis, such as those based on likelihood. However its validity cannot be assessed from the data under analysis, so in Part III, two main approaches are developed and illustrated for the assessment of the sensitivity of the primary analyses to this assumption. Results: The literature review revealed missing data are often ignored, or poorly handled in the analysis. Current guidelines, and frequently used ad-hoc statistical methods are shown to be flawed. A principled, yet practical, alternative approach is developed, which examples show leads inferences with greater validity. SAS code is given to facilitate its direct application. Conclusions: From the design stage onwards, a principled approach to handling missing data should be adopted. Such an approach follows well-defined and accepted statistical arguments, using models and assumptions that are transparent, and hence open to criticism and debate. This monograph outlines how this principled approach can be practically, and directly, applied to the majority of trials with longitudinal follow-up.