Back to Journals » Patient Related Outcome Measures » Volume 10

Analytical approaches and estimands to take account of missing patient-reported data in longitudinal studies

Authors Bell ML, Floden L, Rabe BA, Hudgens S, Dhillon HM, Bray VJ, Vardy JL

Received 5 February 2019

Accepted for publication 14 March 2019

Published 16 April 2019 Volume 2019:10 Pages 129—140

DOI https://doi.org/10.2147/PROM.S178963

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Justinn Cochran

Peer reviewer comments 2

Editor who approved publication: Professor Lynne Nemeth


Melanie L Bell,1,2 Lysbeth Floden,1,3 Brooke A Rabe,1 Stacie Hudgens,3 Haryana M Dhillon,2,4 Victoria J Bray,5 Janette L Vardy6

1Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA; 2Psycho-Oncology Co-operative Research Group, School of Psychology, University of Sydney, Sydney, NSW, Australia; 3Clinical Outcomes Solutions, Tucson, AZ 85718, USA; 4Centre for Medical Psychology & Evidence-Based Decision-Making, School of Psychology, University of Sydney, Sydney, NSW, Australia; 5Department of Medical Oncology, Liverpool Hospital and University of Sydney, Sydney, NSW, Australia; 6Concord Cancer Centre and Sydney Medical School, University of Sydney, Sydney, NSW, Australia

Abstract: Patient-reported outcomes, such as quality of life, functioning, and symptoms, are used widely in therapeutic and behavioral trials and are increasingly used in drug development to represent the patient voice. Missing patient reported data is common and can undermine the validity of results reporting by reducing power, biasing estimates, and ultimately reducing confidence in the results. In this paper, we review statistically principled approaches for handling missing patient-reported outcome data and introduce the idea of estimands in the context of behavioral trials. Specifically, we outline a plan that considers missing data at each stage of research: design, data collection, analysis, and reporting. The design stage includes processes to prevent missing data, define the estimand, and specify primary and sensitivity analyses. The analytic strategy considering missing data depends on the estimand. Reviewed approaches include maximum likelihood-based models, multiple imputation, generalized estimating equations, and responder analysis. We outline sensitivity analyses to assess the robustness of the primary analysis results when data are missing. We also describe ad-hoc methods, including approaches to avoid. Last, we demonstrate methods using data from a behavioral intervention, where the primary outcome was self-reported cognition.

Keywords: estimands, sensitivity analysis, missing data, imputation, patient-reported outcomes
 

Creative Commons License This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

Download Article [PDF]