Abstract
Missing data result in less precise and possibly biased effect estimates in single studies. Bias arising from studies with incomplete outcome data is naturally propagated in a meta‐analysis. Conventional analysis using only individuals with available data is adequate when the meta‐analyst can be confident that the data are missing at random (MAR) in every study—that is, that the probability of missing data does not depend on unobserved variables, conditional on observed variables. Usually, such confidence is unjustified as participants may drop out due to lack of improvement or adverse effects. The MAR assumption cannot be tested, and a sensitivity analysis to assess how robust results are to reasonable deviations from the MAR assumption is important. Two methods may be used based on plausible alternative assumptions about the missing data. Firstly, the distribution of reasons for missing data may be used to impute the missing values. Secondly, the analyst may specify the magnitude and uncertainty of possible departures from the missing at random assumption, and these may be used to correct bias and reweight the studies. This is achieved by employing a pattern mixture model and describing how the outcome in the missing participants is related to the outcome in the completers. Ideally, this relationship is informed using expert opinion. The methods are illustrated in two examples with binary and continuous outcomes. We provide recommendations on what trial investigators and systematic reviewers should do to minimize the problem of missing outcome data in meta‐analysis.
Highlights
Missing outcome data are a common occurrence even in well‐conducted randomized clinical trials (RCTs)
This manuscript explores what we may know about missing data, describes the analysis options in single studies, discusses the methods available in meta‐analysis, and makes suggestions for practice, with a primary focus on aggregate data (AD) meta‐analysis
Data are missing at random (MAR) if missing data have the same distribution as observed data, conditional on other variables included in the analysis
Summary
Missing outcome data are a common occurrence even in well‐conducted randomized clinical trials (RCTs) They may compromise the validity of the analysis of a single study[1] and are a threat to the validity of a meta‐analysis. The threat has been neglected in the meta‐analysis literature as researchers typically assume that the problem has been dealt with at the trial level This manuscript explores what we may know about missing data, describes the analysis options in single studies, discusses the methods available in meta‐analysis, and makes suggestions for practice, with a primary focus on aggregate data (AD) meta‐analysis. Blood pressure data are likely to be MCAR if they are missing because of breakdown of an automatic sphygmomanometer.[4] Data are missing at random (MAR) if missing data have the same distribution as observed data, conditional on other variables included in the analysis.
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