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Addressing dichotomous data for participants excluded from trial analysis: a guide for systematic reviewers
Elie A Akl1, Bradley C Johnston, Pablo Alonso-Coello
1Department of Internal Medicine, American University of Beirut, Beirut, Lebanon. ea32@aub.edu.lb
Introduction:
Systematic reviewer authors intending to include all randomized participants in their meta-analyses need to make assumptions about the outcomes of participants with missing data.
Objective:
The objective of this paper is to provide systematic reviewer authors with a relatively simple guidance for addressing dichotomous data for participants excluded from analyses of randomized trials.
Methods:
This guide is based on a review of the Cochrane handbook and published methodological research. The guide deals with participants excluded from the analysis who were considered 'non-adherent to the protocol' but for whom data are available, and participants with missing data.
Results:
Systematic reviewer authors should include data from 'non-adherent' participants excluded from the primary study authors' analysis but for whom data are available. For missing, unavailable participant data, authors may conduct a complete case analysis (excluding those with missing data) as the primary analysis. Alternatively, they may conduct a primary analysis that makes plausible assumptions about the outcomes of participants with missing data. When the primary analysis suggests important benefit, sensitivity meta-analyses using relatively extreme assumptions that may vary in plausibility can inform the extent to which risk of bias impacts the confidence in the results of the primary analysis. The more plausible assumptions draw on the outcome event rates within the trial or in all trials included in the meta-analysis. The proposed guide does not take into account the uncertainty associated with assumed events.
Conclusions:
This guide proposes methods for handling participants excluded from analyses of randomized trials. These methods can help in establishing the extent to which risk of bias impacts meta-analysis results.
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