Using generalized estimating equations and extensions in randomized trials with missing longitudinal patient reported outcome data. Issue 9 (8th June 2018)

• Record Type: Journal Article
• Authors: Bell, Melanie L.; Horton, Nicholas J.; Dhillon, Haryana M.; Bray, Victoria J.; Vardy, Janette
• Is Part Of: Psycho-oncology. Volume 27:Issue 9(2018)
• Page Start: 2125
• Page End: 2131
• Publication Date: 2018-06-08
• Subjects: cancer -- dropout -- inverse probability weighting -- missing data -- multiple imputation -- oncology -- statistical methods; Cancer -- Psychological aspects -- Periodicals; Cancer -- Social aspects -- Periodicals; Neoplasms -- psychology -- Periodicals; 616.9940019
• DOI: 10.1002/pon.4777
• Languages: English
• ISSNs: 1057-9249

Abstract

Abstract: Objective: Patient reported outcomes (PROs) are important in oncology research; however, missing data can pose a threat to the validity of results. Psycho‐oncology researchers should be aware of the statistical options for handling missing data robustly. One rarely used set of methods, which includes extensions for handling missing data, is generalized estimating equations (GEEs). Our objective was to demonstrate use of GEEs to analyze PROs with missing data in randomized trials with assessments at fixed time points. Methods: We introduce GEEs and show, with a worked example, how to use GEEs that account for missing data: inverse probability weighted GEEs and multiple imputation with GEE. We use data from an RCT evaluating a web‐based brain training for cancer survivors reporting cognitive symptoms after chemotherapy treatment. The primary outcome for this demonstration is the binary outcome of cognitive impairment. Several methods are used, and results are compared. Results: We demonstrate that estimates can vary depending on the choice of analytical approach, with odds ratios for no cognitive impairment ranging from 2.04 to 5.74. While most of these estimates were statistically significant ( P < 0.05), a few were not. Conclusions: Researchers using PROs should use statistical methods that handle missing data in a way as to result in unbiased estimates. GEE extensions are analytic options for handling dropouts in longitudinal RCTs, particularly if the outcome is not continuous.