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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.