Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/124633
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Type: Journal article
Title: Adjusting for treatment switching in oncology trials: a systematic review and rRecommendations for reporting
Author: Sullivan, T.
Latimer, N.
Gray, J.
Sorich, M.
Salter, A.
Karnon, J.
Citation: Value in Health, 2020; 23(3):388-396
Publisher: Elsevier
Issue Date: 2020
ISSN: 1098-3015
1524-4733
Statement of
Responsibility: 
Thomas R. Sullivan, Nicholas R. Latimer, Jodi Gray, Michael J. Sorich, Amy B. Salter, Jonathan Karnon
Abstract: Objectives: To systematically review the quality of reporting on the application of switching adjustment approaches in published oncology trials and industry submissions to the National Institute for Health and Care Excellence Although methods such as the rank preserving structural failure time model (RPSFTM) and inverse probability of censoring weights (IPCW) have been developed to address treatment switching, the approaches are not widely accepted within health technology assessment. This limited acceptance may partly be a consequence of poor reporting on their application. Methods: Published trials and industry submissions were obtained from searches of PubMed and nice.org.uk, respectively. The quality of reporting in these studies was judged against a checklist of reporting recommendations, which was developed by the authors based on detailed considerations of the methods. Results: Thirteen published trials and 8 submissions to nice.org.uk satisfied inclusion criteria. The quality of reporting around the implementation of the RPSFTM and IPCW methods was generally poor. Few studies stated whether the adjustment approach was prespecified, more than a third failed to provide any justification for the chosen method, and nearly half neglected to perform sensitivity analyses. Further, it was often unclear how the RPSFTM and IPCW methods were implemented. Conclusions: Inadequate reporting on the application of switching adjustment methods increases uncertainty around results, which may contribute to the limited acceptance of these methods by decision makers. The proposed reporting recommendations aim to support the improved interpretation of analyses undertaken to adjust for treatment switching.
Keywords: Economic evaluation; statistical methods; survival analysis; technology assessment; treatment switching
Rights: © 2019, ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc.
RMID: 1000014126
DOI: 10.1016/j.jval.2019.10.015
Grant ID: http://purl.org/au-research/grants/nhmrc/1128332
Appears in Collections:Public Health publications

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