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Table 1 Summary table of proposed general approaches for handling missing participant data for dichotomous outcomes

From: Handling trial participants with missing outcome data when conducting a meta-analysis: a systematic survey of proposed approaches

  Complete case analysis Imputations for participants with missing outcome data Take uncertainty into account Relation with ITT principle Assesses risk of bias associated with missing data Testing of the proposed approach
Based on reasons for missingness Relative to risk among followed up Best-case scenario Worst-case scenario Other imputation method
Gamble and Hollis [10]
As primary analysis (if missing data non-informative) As primary analysis (if specified missing data mechanism) As sensitivity analysis As sensitivity analysis Various separate imputations Handling MPD needed for ITT analysis Simulation study
Higgins et al. [12]  
As primary analysis (point of reference) As primary analysis (preferred) (Using IMOR) Applied in 1 meta-analysis of 17 RCTs
Akl et al. [2]
As primary analysis As primary analysis Yes (using RILTFU/FU) As a way to assess risk of bias Relative to observed incidence in trials included in meta-analysis Handling MPD differentiated from ITT Applied in 2 meta-analyses (with 20 and 22 RCTs, respectively)
Mavridis et al. [14]      
(Using IMOR)a Applied in one meta-analysis
  1. RILTFU/FU refers to the event incidence among those lost to follow-up (LTFU) relative to the event incidence among those followed up (FU)
  2. ITT intention to treat, IMOR informative missingness odds ratio