Background: Missing outcome data from randomized trials lead to greater
uncertainty and possible bias in estimating the effect of an experimental treatment.
An intention-to-treat analysis should take account of all randomized participants
even if they have missing observations.
Purpose: To review and develop imputation methods for missing outcome
data in meta-analysis of clinical trials with binary outcomes.
Methods: We review some common strategies, such as simple imputation of
positive or negative outcomes, and develop a general approach involving `informative
missingness odds ratios' (IMORs). We describe several choices for weighting studies
in the meta-analysis, and illustrate methods using a meta-analysis of trials of
haloperidol for schizophrenia.
Results: IMORs describe the relationship between the unknown risk among
missing participants and the known risk among observed participants. They are allowed
to differ between treatment groups and across trials. Application of IMORs and other
methods to the haloperidol trials reveals the overall conclusion to be robust to
different assumptions about the missing data.
Limitations: The methods are based on summary data from each trial
(number of observed positive outcomes, number of observed negative outcomes and
number of missing outcomes) for each intervention group. This limits the options for
analysis, and greater flexibility would be available with individual participant
data.
Conclusions: We propose that available reasons for missingness be used
to determine appropriate IMORs. We also recommend a strategy for undertaking
sensitivity analyses, in which the IMORs are varied over plausible ranges. Clinical
Trials 2008; 5: 225—239. http://ctj.sagepub.com
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