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

  • Venediktos KapetanakisEmail author
  • Thibaud Prawitz
  • Michael Schlichting
  • K. Jack Ishak
  • Hemant Phatak
  • Mairead Kearney
  • John W. Stevens
  • Agnes Benedict
  • Murtuza Bharmal
Original Research Article

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.

Notes

Author Contributions

VK and JI conceived the method; MS, HP, MB and JWS contributed to the method inception; TP analysed the data and wrote the first draft of the manuscript; MB led the project team from inception to completion; VK, MS, JI, HP, MK, JWS, AB and MB contributed to the interpretation of results and revision of the manuscript. All authors have read and approved the final manuscript; VK is the guarantor of the manuscript.

Compliance with Ethical Standards

Funding

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Venediktos Kapetanakis
    • 1
    Email author
  • Thibaud Prawitz
    • 2
  • Michael Schlichting
    • 3
  • K. Jack Ishak
    • 4
  • Hemant Phatak
    • 5
  • Mairead Kearney
    • 6
  • John W. Stevens
    • 7
  • Agnes Benedict
    • 8
  • Murtuza Bharmal
    • 9
  1. 1.Evidence Synthesis, Modeling & Communication, EvideraLondonUK
  2. 2.Evidence Synthesis, Modeling & Communication, EvideraLondonUK
  3. 3.Global Biostatistics, Merck Healthcare KGaADarmstadtGermany
  4. 4.Evidence Synthesis, Modeling & Communication, EvideraMontrealCanada
  5. 5.US Health Economics and Outcomes Research, EMD SeronoRocklandUSA
  6. 6.Global Evidence and Value Development, Merck Healthcare KGaADarmstadtGermany
  7. 7.Health Economics and Decision Science (HEDS), University of SheffieldSheffieldUK
  8. 8.Evidence Synthesis, Modeling & Communication, EvideraBudapestHungary
  9. 9.Global Evidence and Value Development, EMD SeronoRocklandUSA

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