Gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching

Gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching

In randomised controlled trials of treatments for late‐stage cancer, it is common for control arm patients to receive the experimental treatment around the point of disease progression. This treatment switching can dilute the estimated treatment effect on overall survival and impact the assessment o...

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Main Authors: Bowden, Jack, Seaman, Shaun, Huang, Xin, White, Ian R
Format: Online
Language:English
Published: John Wiley and Sons Inc. 2015
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4871231/
id pubmed-4871231
recordtype oai_dc
spelling pubmed-48712312016-05-18 Gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching Bowden, Jack Seaman, Shaun Huang, Xin White, Ian R Research Articles In randomised controlled trials of treatments for late‐stage cancer, it is common for control arm patients to receive the experimental treatment around the point of disease progression. This treatment switching can dilute the estimated treatment effect on overall survival and impact the assessment of a treatment's benefit on health economic evaluations. The rank‐preserving structural failure time model of Robins and Tsiatis (Comm. Stat., 20:2609–2631) offers a potential solution to this problem and is typically implemented using the logrank test. However, in the presence of substantial switching, this test can have low power because the hazard ratio is not constant over time. Schoenfeld (Biometrika, 68:316–319) showed that when the hazard ratio is not constant, weighted versions of the logrank test become optimal. We present a weighted logrank test statistic for the late stage cancer trial context given the treatment switching pattern and working assumptions about the underlying hazard function in the population. Simulations suggest that the weighted approach can lead to large efficiency gains in either an intention‐to‐treat or a causal rank‐preserving structural failure time model analysis compared with the unweighted approach. Furthermore, violation of the working assumptions used in the derivation of the weights only affects the efficiency of the estimates and does not induce bias or inflate the type I error rate. The weighted logrank test statistic should therefore be considered for use as part of a careful secondary, exploratory analysis of trial data affected by substantial treatment switching. ©©2015 The Authors. Statistics inMedicine Published by John Wiley & Sons Ltd. John Wiley and Sons Inc. 2015-11-17 2016-04-30 /pmc/articles/PMC4871231/ /pubmed/26576494 http://dx.doi.org/10.1002/sim.6801 Text en ©2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Bowden, Jack
Seaman, Shaun
Huang, Xin
White, Ian R
spellingShingle Bowden, Jack
Seaman, Shaun
Huang, Xin
White, Ian R
Gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching
author_facet Bowden, Jack
Seaman, Shaun
Huang, Xin
White, Ian R
author_sort Bowden, Jack
title Gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching
title_short Gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching
title_full Gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching
title_fullStr Gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching
title_full_unstemmed Gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching
title_sort gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching
description In randomised controlled trials of treatments for late‐stage cancer, it is common for control arm patients to receive the experimental treatment around the point of disease progression. This treatment switching can dilute the estimated treatment effect on overall survival and impact the assessment of a treatment's benefit on health economic evaluations. The rank‐preserving structural failure time model of Robins and Tsiatis (Comm. Stat., 20:2609–2631) offers a potential solution to this problem and is typically implemented using the logrank test. However, in the presence of substantial switching, this test can have low power because the hazard ratio is not constant over time. Schoenfeld (Biometrika, 68:316–319) showed that when the hazard ratio is not constant, weighted versions of the logrank test become optimal. We present a weighted logrank test statistic for the late stage cancer trial context given the treatment switching pattern and working assumptions about the underlying hazard function in the population. Simulations suggest that the weighted approach can lead to large efficiency gains in either an intention‐to‐treat or a causal rank‐preserving structural failure time model analysis compared with the unweighted approach. Furthermore, violation of the working assumptions used in the derivation of the weights only affects the efficiency of the estimates and does not induce bias or inflate the type I error rate. The weighted logrank test statistic should therefore be considered for use as part of a careful secondary, exploratory analysis of trial data affected by substantial treatment switching. ©©2015 The Authors. Statistics inMedicine Published by John Wiley & Sons Ltd.
publisher John Wiley and Sons Inc.
publishDate 2015
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4871231/
_version_ 1613580879022522368

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