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Table 1 Kappa statistics for different imputation strategies when missingness is completely at random

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 0.954 0.913     
  Propensity score 0.953 0.910 0.865 0.820 0.730 0.549
  MCMC1 0.954 0.913 0.869 0.825 0.737 0.561
Across cluster Propensity score 0.954 0.912 0.868 0.828 0.738 0.556
  Random-effects logistic regression 0.955 0.914 0.871 0.830 0.741 0.562
  Fixed-effects logistic regression 0.956 0.911 0.866 0.821 0.732 0.554
Ignore cluster Logistic regression 0.954 0.907 0.861 0.814 0.722 0.537
  Propensity score 0.952 0.902 0.854 0.804 0.707 0.512
  MCMC1 0.953 0.906 0.859 0.811 0.717 0.530
  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.