A comparison of imputation strategies in cluster randomized trials with missing binary outcomes

Stat Methods Med Res. 2016 Dec;25(6):2650-2669. doi: 10.1177/0962280214530030. Epub 2014 Apr 7.

Abstract

In cluster randomized trials, clusters of subjects are randomized rather than subjects themselves, and missing outcomes are a concern as in individual randomized trials. We assessed strategies for handling missing data when analysing cluster randomized trials with a binary outcome; strategies included complete case, adjusted complete case, and simple and multiple imputation approaches. We performed a simulation study to assess bias and coverage rate of the population-averaged intervention-effect estimate. Both multiple imputation with a random-effects logistic regression model or classical logistic regression provided unbiased estimates of the intervention effect. Both strategies also showed good coverage properties, even slightly better for multiple imputation with a random-effects logistic regression approach. Finally, this latter approach led to a slightly negatively biased intracluster correlation coefficient estimate but less than that with a classical logistic regression model strategy. We applied these strategies to a real trial randomizing households and comparing ivermectin and malathion to treat head lice.

Keywords: cluster randomized trial; missing data; multiple imputation; outcome.

Publication types

  • Comparative Study

MeSH terms

  • Animals
  • Bias
  • Data Interpretation, Statistical
  • Female
  • Hair / anatomy & histology
  • Humans
  • Ivermectin / therapeutic use
  • Lice Infestations / drug therapy
  • Logistic Models
  • Male
  • Pediculus / drug effects
  • Randomized Controlled Trials as Topic / methods*

Substances

  • Ivermectin