Addressing dichotomous data for participants excluded from trial analysis: a guide for systematic reviewers

Abstract

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.

Figure 1. Status of participants and their outcome data at both the trial and systematic review levels.

† It is possible that the investigators collected but did not report the data. * Data analysis might have included assumptions about missing participant data. ITT =  intention to treat; CCA =  complete case analysis. § Refers to ineligible participants mistakenly randomized and meeting the conditions for appropriate exclusion (see text), and to participants with other reasons for appropriate exclusion (e.g., subsequently found not to have condition of interest, or never underwent a procedure for which the intervention is intended).

Figure 2. Flow of participants in a trial that excluded participants from analysis for missing participant data.

Figure 3. Matrix of assumptions about missing participant data respectively in intervention and control arms of a trial.

Assumptions of incidence among participants with missing data in the intervention arm typically decrease going from right (100%) to left. Assumptions of incidence among participants with missing data in the control arm typically decrease going from bottom (100%) to top. Assumptions shaded in green take into account the incidence rates in all trials included in the systematic review, and not only the trial under consideration. Assumptions shaded in yellow take into account the incidence rates within the trial under consideration. RILTFU/FU can have different values in the control and intervention groups respectively. Assumptions shaded in orange are extreme and typically implausible.