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 |