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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


Checked for plagiarism Yes

Review by Single-blind

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

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