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Table 4 Estimated treatment effects for different imputation strategies when missingness is covariate dependent

From: Imputation strategies for missing binary outcomes in cluster randomized trials

Imputation level Imputation strategies Analysis model OR4 and 95% CI5 for Complete Data: GEE2 1.14 (0.76 1.70) RE3 1.12 (0.72 1.76)
    OR4 and 95% CI5 for Different Percentage of missingness
    5% 10% 15% 20% 30% 50%
Within cluster Logistic regression GEE2 1.14 (0.76 1.72) 1.14 (0.76 1.72)     
   RE3 1.12 (0.71 1.78) 1.13 (0.71 1.78)     
  Propensity score GEE2 1.14 (0.75 1.72) 1.14 (0.75 1.73) 1.14 (0.74 1.75) 1.14 (0.73 1.78) 1.15 (0.71 1.84) 1.18 (0.68 2.04)
   RE3 1.12 (0.70 1.79) 1.12 (0.70 1.79) 1.12 (0.69 1.82) 1.12 (0.68 1.86) 1.12 (0.65 1.93) 1.15 (0.61 2.18)
  MCMC1 GEE2 1.13 (0.75 1.71) 1.13 (0.75 1.70) 1.13 (0.74 1.71) 1.12 (0.74 1.72) 1.12 (0.72 1.74) 1.12 (0.69 1.80)
   RE3 1.11 (0.70 1.77) 1.11 (0.70 1.76) 1.11 (0.69 1.77) 1.11 (0.69 1.78) 1.10 (0.67 1.81) 1.10 (0.64 1.88)
Across cluster Propensity score GEE2 1.14 (0.77 1.68) 1.14 (0.77 1.67) 1.14 (0.78 1.67) 1.14 (0.79 1.67) 1.15 (0.79 1.67) 1.15 (0.76 1.72)
   RE3 1.18 (0.88 1.59) 1.18 (0.87 1.59) 1.18 (0.87 1.60) 1.18 (0.86 1.61) 1.18 (0.85 1.64) 1.17 (0.78 1.76)
  Random-effects GEE2 1.15 (0.78 1.69) 1.16 (0.80 1.70) 1.18 (0.81 1.72) 1.19 (0.81 1.75) 1.22 (0.81 1.83) 1.31 (0.83 2.06)
  logistic regression RE3 1.14 (0.75 1.74) 1.16 (0.77 1.74) 1.18 (0.79 1.76) 1.19 (0.80 1.78) 1.22 (0.80 1.86) 1.31 (0.83 2.05)
  Fixed-effects GEE2 1.14 (0.76 1.71) 1.15 (0.76 1.73) 1.15 (0.76 1.76) 1.16 (0.75 1.79) 1.17 (0.73 1.86) 1.17 (0.67 2.04)
  Logistic regression RE4 1.13 (0.72 1.77) 1.14 (0.72 1.79) 1.14 (0.71 1.83) 1.15 (0.71 1.86) 1.15 (0.68 1.94) 1.15 (0.61 2.18)
Ignore cluster Logistic regression GEE2 1.14 (0.78 1.67) 1.14 (0.79 1.65) 1.15 (0.80 1.64) 1.15 (0.81 1.64) 1.16 (0.83 1.63) 1.15 (0.81 1.63)
   RE3 1.13 (0.74 1.72) 1.14 (0.76 1.70) 1.15 (0.78 1.68) 1.15 (0.80 1.67) 1.16 (0.82 1.65) 1.15 (0.81 1.63)
  Propensity score GEE2 1.14 (0.78 1.67) 1.14 (0.79 1.65) 1.15 (0.81 1.64) 1.15 (0.82 1.63) 1.15 (0.83 1.61) 1.15 (0.82 1.62)
   RE3 1.13 (0.75 1.72) 1.14 (0.77 1.69) 1.15 (0.79 1.67) 1.15 (0.80 1.66) 1.15 (0.82 1.63) 1.15 (0.82 1.62)
  MCMC1 GEE2 1.14 (0.78 1.67) 1.14 (0.79 1.65) 1.15 (0.80 1.63) 1.15 (0.81 1.62) 1.15 (0.82 1.59) 1.13 (0.82 1.57)
   RE3 1.13 (0.74 1.72) 1.14 (0.77 1.69) 1.14 (0.78 1.67) 1.15 (0.80 1.65) 1.15 (0.81 1.61) 1.13 (0.82 1.57)
Complete case analysis GEE2 1.14 (0.76 1.70) 1.14 (0.76 1.71) 1.14 (0.76 1.72) 1.15 (0.76 1.73) 1.15 (0.75 1.75) 1.15 (0.73 1.80)
   RE3 1.13 (0.72 1.75) 1.13 (0.72 1.76) 1.13 (0.72 1.77) 1.14 (0.72 1.78) 1.14 (0.72 1.80) 1.15 (0.71 1.85)
  1. Note:
  2. 1. MCMC = Markov chain Monte Carlo. For MCMC methods, we round the imputed values to 1 if it is equal or greater than 0.5 and to 0 otherwise.
  3. 2. GEE = Generalized estimation equation method
  4. 3. RE = Random-effects logistic regression
  5. 4. OR = Odds ratio
  6. 5. CI = Confidence interval