A pattern-mixture model approach for handling missing continuous outcome data in longitudinal cluster randomized trials

Mallorie H. Fiero, Chiu Hsieh Hsu, Melanie L. Bell

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

We extend the pattern-mixture approach to handle missing continuous outcome data in longitudinal cluster randomized trials, which randomize groups of individuals to treatment arms, rather than the individuals themselves. Individuals who drop out at the same time point are grouped into the same dropout pattern. We approach extrapolation of the pattern-mixture model by applying multilevel multiple imputation, which imputes missing values while appropriately accounting for the hierarchical data structure found in cluster randomized trials. To assess parameters of interest under various missing data assumptions, imputed values are multiplied by a sensitivity parameter, k, which increases or decreases imputed values. Using simulated data, we show that estimates of parameters of interest can vary widely under differing missing data assumptions. We conduct a sensitivity analysis using real data from a cluster randomized trial by increasing k until the treatment effect inference changes. By performing a sensitivity analysis for missing data, researchers can assess whether certain missing data assumptions are reasonable for their cluster randomized trial.

Original languageEnglish (US)
Pages (from-to)4094-4105
Number of pages12
JournalStatistics in Medicine
Volume36
Issue number26
DOIs
StatePublished - Nov 20 2017

Keywords

  • cluster randomized trials
  • missing data
  • multiple imputation
  • pattern-mixture model

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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