• Xueya Cai, Jennifer S Gewandter, Hua He, Dennis C Turk, Robert H Dworkin, and Michael P McDermott.
• Departments of Biostatistics and Computational Biology and.
• Pain. 2020 Oct 1; 161 (10): 2308-2320.

AbstractIn clinical trials of treatments for chronic pain, the percentage of participants who withdraw early can be as high as 50%. Major reasons for early withdrawal in these studies include perceived lack of efficacy and adverse events. Commonly used strategies for accommodating missing data include last observation carried forward, baseline observation carried forward, and more principled methods such as mixed-model repeated-measures and multiple imputation. All these methods require strong and untestable assumptions concerning the conditional distribution of outcomes after dropout, given the observed data. We review recent developments in statistical methods for handling missing data in clinical trials, including implications of the increased emphasis being placed on precise formulation of the study objectives and the estimand (treatment effect to be estimated) of interest. A flexible method that seems to be well suited for the analysis of chronic pain clinical trials is control-based imputation, which allows a variety of assumptions to be made concerning the conditional distribution of postdropout outcomes that can be tailored to the estimand of interest. These assumptions can depend, for example, on the stated reasons for dropout. We illustrate these methods using data from 4 clinical trials of pregabalin for the treatment of painful diabetic peripheral neuropathy and postherpetic neuralgia. When planning chronic pain clinical trials, careful consideration of the trial objectives should determine the definition of the trial estimand, which in turn should inform methods used to accommodate missing data in the statistical analysis.

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