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057 Handling Trial Participants with Missing Data in Meta-Analyses Of Dichotomous Outcomes: Guidance for Systematic Reviewers
  1. E Akl1,
  2. B Johnston2,4,
  3. P Alonso-Coello3,
  4. I Neumann4,
  5. S Ebrahim4,
  6. M Briel5,
  7. D Cook4,
  8. G Guyatt4
  1. 1American University of Beirut, Beirut, Lebanon
  2. 2Sick Kids Research Institute, Toronto, Canada
  3. 3Ibero-American Cochrane Centre, Barcelona, Spain
  4. 4McMaster University, Hamilton, Ontario
  5. 5Basel Institute for Clinical Epidemiology, Basel, Switzerland

Abstract

Background Systematic reviewers including all randomised participants in their meta-analyses need to make assumptions about the outcomes of those with missing data.

Objectives To provide systematic review authors with guidance on dealing with participants with missing data for dichotomous outcomes.

Methods The authors used an iterative process of suggesting guidance and obtaining feedback to arrive at a proposed approach.

Results For participants with missing data, systematic reviewers can use a range of plausible assumptions in the intervention and control arms. Extreme assumptions include ‘all’ or ‘none’ of the participants had an event, but these assumptions are not plausible. Less extreme assumptions may draw on the incidence rates within the trial (e.g., same incidence in the trial control arm) or in all trials included in the meta-analysis (e.g., highest incidence among control arms of all included trials). The primary meta-analysis may use either a complete case analysis or a plausible assumption. Sensitivity meta-analyses to test the robustness of the primary meta-analysis results should include extreme plausible assumptions. When the meta-analysis results are robust to extreme plausible assumptions, inferences are strengthened. Vulnerability to extreme plausible assumptions suggests rating down confidence in estimates of effect for risk of bias.

Conclusions This guide proposes an approach to establishing confidence in estimates of effect when systematic reviewers are faced with missing participant data for binary dichotomous outcomes in randomised trials.

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