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Using generalized estimating equations and extensions in randomized trials with missing longitudinal patient reported

Melanie L Bell1,2, Nicholas J Horton3, Haryana M Dhillon2,4

  • 1Department of Epidemiology and Biostatistics, Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Psycho-Oncology
|May 27, 2018
PubMed
Summary

Generalized estimating equations (GEE) offer robust statistical methods for handling missing patient-reported outcomes (PROs) in oncology research. These advanced techniques ensure more reliable results in longitudinal randomized trials, especially for non-continuous data.

Keywords:
cancerdropoutinverse probability weightingmissing datamultiple imputationoncologystatistical methods

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Area of Science:

  • Oncology research
  • Psycho-oncology
  • Biostatistics

Background:

  • Patient-reported outcomes (PROs) are crucial in oncology research but are susceptible to missing data, which can compromise study validity.
  • Robust statistical methods are needed to address missing data in PRO analyses, particularly in longitudinal studies.

Purpose of the Study:

  • To demonstrate the application of generalized estimating equations (GEEs) for analyzing PROs with missing data in randomized controlled trials (RCTs).
  • To compare GEE methods, including inverse probability weighted GEEs and multiple imputation with GEE, using a real-world example.

Main Methods:

  • Utilized data from an RCT evaluating a web-based brain training intervention for cancer survivors experiencing cognitive symptoms post-chemotherapy.
  • Applied GEE extensions, specifically inverse probability weighted GEEs and multiple imputation with GEE, to handle missing data in PROs.
  • Focused on a binary outcome of cognitive impairment for the primary analysis.

Main Results:

  • Analytical approach significantly influenced study estimates, with odds ratios for no cognitive impairment varying from 2.04 to 5.74.
  • Most estimates were statistically significant (P < 0.05), highlighting the impact of the chosen statistical method.

Conclusions:

  • Researchers utilizing PROs should employ statistical methods capable of producing unbiased estimates by effectively handling missing data.
  • GEE extensions provide valuable analytical options for managing dropouts in longitudinal RCTs, especially for non-continuous outcomes.