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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.