Dr Daniel Jackson, MRC Biostatistics Unit

How much can we learn about missing data?  An exploration of a clinical trial in psychiatry

by Dan Jackson, Ian R White and Morven Leese

When a randomised controlled trial has missing outcome data, any analysis is based on untestable assumptions, for example that the data are missing at random, or less commonly on other assumptions about the mising data mechanism.  Given such assumptions, there is an extensive literature on suitable analysis methods.  However, little is known about what assumptions are appropriate.  We use two sources of ancillary data to explore the missing data mechanism in a trial of adherence therapy in patients with schizophrenia: carer-reported (proxy) outcomes and the number of contact attempts.  This requires making additional assumptions whose plausibility we discuss.  We also perform sensitivity analyses to departures from missing at random.  Wider use of techniques such as these will help to inform the choice of suitable assumptions for the analysis of randomised controlled trials.