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Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network

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posted on 2022-05-18, 11:00 authored by Suzanne C. Freeman, Nicola J. Cooper, Alex J. Sutton, Michael J. Crowther, James R, Carpenter, Neil Hawkins
Background Synthesis of clinical effectiveness from multiple trials is a well-established component of decision-making. Time-to-event outcomes are often synthesised using the Cox proportional hazards model assuming a constant hazard ratio over time. However, with an increasing proportion of trials reporting treatment effects where hazard ratios vary over time and with differing lengths of follow-up across trials, alternative synthesis methods are needed. Objectives To compare and contrast five modelling approaches for synthesis of time-to-event outcomes and provide guidance on key considerations for choosing between the modelling approaches. Methods The Cox proportional hazards model and five other methods of estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption, were applied to a network of melanoma trials reporting overall survival: restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models. Results All models fitted the melanoma network acceptably well. However, there were important differences in extrapolations of the survival curve and interpretability of the modelling constraints demonstrating the potential for different conclusions from different modelling approaches. Conclusion The restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can accommodate non-proportional hazards and differing lengths of trial follow-up within a network meta-analysis of time-to-event outcomes. We recommend that model choice is informed using available and relevant prior knowledge, model transparency, graphically comparing survival curves alongside observed data to aid consideration of the reliability of the survival estimates, and consideration of how the treatment effect estimates can be incorporated within a decision model.

Funding

SCF is funded by a National Institute for Health Research (NIHR) Post-Doctoral Fellowship (PDF-2018-11-ST2-007) for this research project. SCF, NJC, AJS and NH are funded by the NIHR Complex Reviews Support Unit (project number 14/178/29). SCF, NJC, AJS and MJC are supported by the NIHR Applied Research Collaboration East Midlands (ARC EM). This paper presents independent research funded by the NIHR. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. MJC was part funded by a MRC New Investigator Research Grant (MR/P015433/1). JRC was supported by the UK Medical Research Council via core funding for the MRC Clinical Trials Unit at UCL and grant funding for the MRC London Hub for Trials Methodology Research (MC UU 12023/21).

History

Citation

Statistical Methods in Medical Research 31(5) 839-861

Author affiliation

Department of Health Sciences

Version

  • VoR (Version of Record)

Published in

Statistical Methods in Medical Research

Volume

31

Issue

5

Pagination

839-861

Publisher

SAGE Publications

issn

0962-2802

eissn

1477-0334

Copyright date

2022

Available date

2022-05-18

Language

en

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