discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable.

The panel concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data, and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations here, in the body of the report the panel offers additional recommendations on the conduct of clinical trials and techniques for analysis of trial data.

Modern statistical analysis tools—such as maximum likelihood, multiple imputation, Bayesian methods, and methods based on generalized estimating equations—can reduce the potential bias arising from missing data by making principled use of auxiliary information available for nonrespondents. The panel encourages increased use of these methods. However, all of these methods ultimately rely on untestable assumptions concerning the factors leading to the missing values and how they relate to the study outcomes. Therefore, the assumptions underlying these methods need to be clearly communicated to medical experts so that they can assess their validity. Sensitivity analyses are also important to assess the degree to which the treatment effects rely on the assumptions used.


There is no “foolproof” way to analyze data subject to substantial amounts of missing data; that is, no method recovers the robustness and unbiasedness of estimates derived from randomized allocation of treatments. Hence, the panel’s first set of recommendations emphasizes the role of design and trial conduct to limit the amount and impact of missing data.

A requisite for consideration of trial design is to clearly define the target population, and the outcomes that will form the basis for decisions about efficacy and safety. The treatment of missing data depends on how these outcomes are defined, and lack of clarity in their definition translates into a lack of clarity in how to deal with missing data issues. In addition, given the difficulties of adequately addressing missing data at the analysis stage, the design process needs to pay more attention to the potential hazards arising from substantial numbers of missing values.

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