Thomas R Sullivan1, Nicholas R Latimer2, Jodi Gray3, Michael J Sorich4, Amy B Salter5, Jonathan Karnon4. 1. SAHMRI Women & Kids, South Australian Health & Medical Research Institute, Adelaide, Australia; School of Public Health, The University of Adelaide, Adelaide, Australia. Electronic address: thomas.sullivan@sahmri.com. 2. School of Health and Related Research, The University of Sheffield, Sheffield, England, UK. 3. Australian Institute of Health Innovation, Macquarie University, Sydney, Australia; College of Medicine and Public Health, Flinders University, Adelaide, Australia. 4. College of Medicine and Public Health, Flinders University, Adelaide, Australia. 5. School of Public Health, The University of Adelaide, Adelaide, Australia.
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.
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.
Authors: Sabine E Grimm; Willem Witlox; Robert Wolff; Annette Chalker; Mickael Hiligsmann; Ben Wijnen; Charlotte Ahmadu; Steve Ryder; Nigel Armstrong; Steven Duffy; Isabel Syndikus; Jos Kleijnen; Manuela A Joore Journal: Pharmacoeconomics Date: 2021-10-19 Impact factor: 4.558
Authors: Rachel Evans; Neil Hawkins; Pascale Dequen-O'Byrne; Charles McCrea; Dominic Muston; Christopher Gresty; Sameer R Ghate; Lin Fan; Robert Hettle; Keith R Abrams; Johann de Bono; Maha Hussain; Neeraj Agarwal Journal: Target Oncol Date: 2021-09-03 Impact factor: 4.493