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Table 3 Kappa statistics for different imputation strategies when missingness is covariate dependent

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

Imputation Level Imputation strategies Percentage of missingness
   5% 10% 15% 20% 30% 50%
Within cluster Logistic regression 0.949 0.902     
  Propensity score 0.947 0.899 0.850 0.801 0.706 0.524
  MCMC1 0.948 0.901 0.854 0.806 0.714 0.535
Across cluster Propensity score 0.949 0.903 0.853 0.805 0.713 0.529
  Random-effects logistic regression 0.951 0.908 0.859 0.808 0.717 0.538
  Fixed-effects logistic regression 0.949 0.899 0.850 0.801 0.707 0.528
Ignore cluster Logistic regression 0.947 0.895 0.844 0.793 0.695 0.508
  Propensity score 0.945 0.891 0.839 0.787 0.688 0.495
  MCMC1 0.946 0.893 0.841 0.790 0.691 0.501
  1. Note:
  2. 1 MCMC = Markov chain Monte Carlo. For the MCMC methods, we round the imputed values to 1 if it is equal or greater than 0.5 and to 0 otherwise.