The timing of efficacy-related clinical events recorded at scheduled study visits in clinical trials are interval censored, with the interval duration pre-determined by the study protocol. Events may happen any time during that interval but can only be detected during a planned or unplanned visit. Disease progression in oncology is a notable example where the time to an event is affected by the schedule of visits within a study. This can become a source of bias when studies with varying assessment schedules are used in unanchored comparisons using methods such as matching-adjusted indirect comparisons.
We illustrate assessment-time bias (ATB) in a simulation study based on data from a recent study in second-line treatment for locally advanced or metastatic urothelial carcinoma, and present a method to adjust for differences in assessment schedule when comparing progression-free survival (PFS) against a competing treatment.
A multi-state model for death and progression was used to generate simulated death and progression times, from which PFS times were derived. PFS data were also generated for a hypothetical comparator treatment by applying a constant hazard ratio (HR) to the baseline treatment. Simulated PFS times for the two treatments were then aligned to different assessment schedules so that progression events were only observed at set visit times, and the data were analysed to assess the bias and standard error of estimates of HRs between two treatments with and without assessment-schedule matching (ASM).
ATB is highly affected by the rate of the event at the first assessment time; in our examples, the bias ranged from 3 to 11% as the event rate increased. The proposed method relies on individual-level data from a study and attempts to adjust the timing of progression events to the comparator’s schedule by shifting them forward or backward without altering the patients’ actual follow-up time. The method removed the bias almost completely in all scenarios without affecting the precision of estimates of comparative effectiveness.
Considering the increasing use of unanchored comparative analyses for novel cancer treatments based on single-arm studies, the proposed method offers a relatively simple means of improving the accuracy of relative benefits of treatments on progression times.
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Data Availability Statement
The software code for the generation of the data analysed during this study and the implementation of the assessment-schedule matching method is included in the Electronic Supplementary Material files of this published article.
Zeng L, Cook RJ, Wen L, Boruvka A. Bias in progression-free survival analysis due to intermittent assessment of progression. Stat Med. 2015;34(24):3181–93.
Panageas KS, Ben-Porat L, Dickler MN, Chapman PB, Schrag D. When you look matters: the effect of assessment schedule on progression-free survival. J Natl Cancer Inst. 2007;99(6):428–32.
Qi Y, Allen Ziegler KL, Hillman SL, et al. Impact of disease progression date determination on progression-free survival estimates in advanced lung cancer. Cancer. 2012;118(21):5358–65.
Hatswell AJ, Baio G, Berlin JA, Irs A, Freemantle N. Regulatory approval of pharmaceuticals without a randomised controlled study: analysis of EMA and FDA approvals 1999–2014. BMJ Open. 2016;6(6):e011666.
Alexiou D, Chatzitheofilou I, Pi Blanque A. A review of NICE technology appraisals in oncology using single arm trials (SAT) evidence [abstract no. PRM85]. Value Health. 2018;21:S224.
Phillippo DM, Ades T, Dias S, Palmer S, Abrams KR, Welton NJ. NICE DSU technical support document 18: methods for population-adjusted indirect comparisons in submissions to NICE. Sheffield: NICE Decision Support Unit; 2016.
Ishak KJ, Proskorovsky I, Benedict A. Simulation and matching-based approaches for indirect comparison of treatments. Pharmacoeconomics. 2015;33(6):537–49.
Signorovitch JE, Wu EQ, Betts KA, Parikh K, Kantor E, Guo A, et al. Comparative efficacy of nilotinib and dasatinib in newly diagnosed chronic myeloid leukemia: a matching-adjusted indirect comparison of randomized trials. Curr Med Res Opin. 2011;27(6):1263–71.
NICE Single technology appraisal: atezolizumab for treating metastatic urothelial bladder cancer after platinum-based chemotherapy [ID1327]. London: NICE; 2017.
Fuchs CS, Doi T, Jang RW, Muro K, Satoh T, Machado M, et al. Safety and efficacy of pembrolizumab monotherapy in patients with previously treated advanced gastric and gastroesophageal junction cancer: phase 2 clinical KEYNOTE-059 trial. JAMA Oncol. 2018;4(5):e180013.
Kang YK, Boku N, Satoh T, Ryu MH, Chao Y, Kato K, et al. Nivolumab in patients with advanced gastric or gastro-oesophageal junction cancer refractory to, or intolerant of, at least two previous chemotherapy regimens (ONO-4538-12, ATTRACTION-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2017;390(10111):2461–71.
Powles T, O’Donnell PH, Massard C, Arkenau HT, Friedlander TW, Hoimes CJ, et al. Efficacy and safety of durvalumab in locally advanced or metastatic urothelial carcinoma: updated results from a phase 1/2 open-label study. JAMA Oncol. 2017;3(9):e172411.
Apolo AB, Ellerton J, Infante JR, Agrawal M, Gordon MS, Aljumaily R, et al. Avelumab treatment of metastatic urothelial carcinoma (mUC) in the phase 1b JAVELIN Solid Tumor study: updated analysis with ≥ 12 months of follow-up in all patients [poster no. 856P]. 42nd ESMO Annual Congress; 8–12 Sep 2017; Madrid.
Champiat S, Dercle L, Ammari S, Massard C, Hollebecque A, Postel-Vinay S, et al. Hyperprogressive disease is a new pattern of progression in cancer patients treated by anti-PD-1/PD-L1. Clin Cancer Res. 2017;23(8):1920–8.
Tanase T, Hamada C, Yoshino T, Ohtsu A. A proposal for progression-free survival assessment in patients with early progressive cancer. Anticancer Res. 2017;37(10):5851–5.
Heller G. Proportional hazards regression with interval censored data using an inverse probability weight. Lifetime Data Anal. 2011;17(3):373–85.
This research was funded by Merck KGaA, Darmstadt, Germany, and is part of an alliance between Merck KGaA and Pfizer Inc., New York, NY, USA.
Conflict of interest
Venediktos Kapetanakis, Thibaud Prawitz, Jack Ishak and Agnes Benedict are employees of Evidera, which was hired by the sponsor, Merck Healthcare KGaA, to conduct this research. John W. Stevens served as a consultant to Evidera. Michael Schlichting and Mairead Kearney are employees of the sponsor, Merck Healthcare KGaA. Hemant Phatak and Murtuza Bharmal are employees of EMD Serono, a business of Merck KGaA, Darmstadt, Germany.
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Kapetanakis, V., Prawitz, T., Schlichting, M. et al. Assessment-Schedule Matching in Unanchored Indirect Treatment Comparisons of Progression-Free Survival in Cancer Studies. PharmacoEconomics 37, 1537–1551 (2019). https://doi.org/10.1007/s40273-019-00831-3