Assessment-Schedule Matching in Unanchored Indirect Treatment Comparisons of Progression-Free Survival in Cancer Studies

Venediktos Kapetanakis (), Thibaud Prawitz, Michael Schlichting, K. Jack Ishak, Hemant Phatak, Mairead Kearney, John W. Stevens, Agnes Benedict and Murtuza Bharmal
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Venediktos Kapetanakis: Evidence Synthesis, Modeling & Communication, Evidera
Thibaud Prawitz: Evidence Synthesis, Modeling & Communication, Evidera
Michael Schlichting: Global Biostatistics, Merck Healthcare KGaA
K. Jack Ishak: Evidence Synthesis, Modeling & Communication, Evidera
Hemant Phatak: US Health Economics and Outcomes Research, EMD Serono
Mairead Kearney: Global Evidence and Value Development, Merck Healthcare KGaA
John W. Stevens: Health Economics and Decision Science (HEDS), University of Sheffield
Agnes Benedict: Evidence Synthesis, Modeling & Communication, Evidera
Murtuza Bharmal: Global Evidence and Value Development, EMD Serono

PharmacoEconomics, 2019, vol. 37, issue 12, No 10, 1537-1551

Abstract: Abstract Background 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. Objective 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. Methods 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). Results 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. Conclusions 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.

Date: 2019
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DOI: 10.1007/s40273-019-00831-3

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