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The Stata Journal
Volume 16 Number 2: pp. 443-463



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Reference-based sensitivity analysis via multiple imputation for longitudinal trials with protocol deviation

Suzie Cro
MRC Clinical Trials Unit at UCL
London School of Hygiene and Tropical Medicine
London, UK
s.cro@ucl.ac.uk
Tim P. Morris
MRC Clinical Trials Unit at UCL
London School of Hygiene and Tropical Medicine
London, UK
tim.morris@ucl.ac.uk
Michael G. Kenward
London School of Hygiene and Tropical Medicine
London, UK
mike.kenward@lshtm.ac.uk
James R. Carpenter
MRC Clinical Trials Unit at UCL
London School of Hygiene and Tropical Medicine
London, UK
james.carpenter@lshtm.ac.uk
Abstract.  Randomized controlled trials provide essential evidence for the evaluation of new and existing medical treatments. Unfortunately, the statistical analysis is often complicated by the occurrence of protocol deviations, which mean we cannot always measure the intended outcomes for individuals who deviate, resulting in a missing-data problem. In such settings, however one approaches the analysis, an untestable assumption about the distribution of the unobserved data must be made. To understand how far the results depend on these assumptions, the primary analysis should be supplemented by a range of sensitivity analyses, which explore how the conclusions vary over a range of different credible assumptions for the missing data. In this article, we describe a new command, mimix, that can be used to perform reference-based sensitivity analyses for randomized controlled trials with longitudinal quantitative outcome data, using the approach proposed by Carpenter, Roger, and Kenward (2013, Journal of Biopharmaceutical Statistics 23: 1352–1371). Under this approach, we make qualitative assumptions about how individuals’ missing outcomes relate to those observed in relevant groups in the trial, based on plausible clinical scenarios. Statistical analysis then proceeds using the method of multiple imputation.
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View all articles by these authors: Suzie Cro, Tim P. Morris, Michael G. Kenward, James R. Carpenter

View all articles with these keywords: mimix, clinical trial, protocol deviation, missing data, multiple imputation, sensitivity analysis

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