Table 5.
Failures in 5000 simulated datasets when exposure prevalence was 15% and sample size was 1500 for primary missing data scenario (6 patterns, 50% complete cases)
| Risk difference 0% |
Risk difference 5% |
|||
|---|---|---|---|---|
| Missing data approacha | R | SAS | R | SAS |
|
| ||||
| MAR | ||||
| CC | 12 (0.2%) | 25 (0.5%) | 0 (0.0%) | 1 (<0.1%) |
| MI | 18 (0.4%) | 25 (0.5%) | 0 (0.0%) | 1 (<0.1%) |
| Weightingb | 12 (0.2%) | 25 (0.5%) | 0 (0.0%) | 1 (<0.1%) |
| MNAR | ||||
| CC | 165 (3.3%) | 202 (4.0%) | 32 (0.6%) | 52 (1.0%) |
| MI | 163 (3.3%) | 168 (3.4%) | 22 (0.4%) | 38 (0.8%) |
| Weightingb | 162 (3.2%) | 202 (4.0%) | 34 (0.7%) | 52 (1.0%) |
Abbreviations: MAR, missing at random; CC, complete case analysis; MI, multiple imputation; MNAR, missing not at random
a
All approaches addressed confounding using inverse probability of treatment weights
b
UMLE used to estimate the missingness weights