Literature DB >> 15900586

Statistical inference for cancer trials with treatment switching.

Jun Shao1, Mark Chang, Shein-Chung Chow.   

Abstract

In cancer clinical trials, it is not uncommon that some patients switched their treatments due to lack of efficacy and/or disease progression under ethical consideration. This treatment switch makes it difficult for the evaluation of the efficacy of the treatment under investigation. The current existing methods consider random treatment switch and do not take into consideration of prognosis and/or investigator's assessment that leads to patients' treatment switch. In this paper, we model patients' treatment switching effect in a latent event times model under parametric setting or a latent hazard rate model under the semi-parametric proportional hazard model. Statistical inference procedures under both models are provided. A simulation study is performed to investigate the performance of the proposed methods. Copyright 2005 John Wiley & Sons, Ltd.

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Year:  2005        PMID: 15900586     DOI: 10.1002/sim.2128

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  9 in total

1.  The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design.

Authors:  Munyaradzi Dimairo; Philip Pallmann; James Wason; Susan Todd; Thomas Jaki; Steven A Julious; Adrian P Mander; Christopher J Weir; Franz Koenig; Marc K Walton; Jon P Nicholl; Elizabeth Coates; Katie Biggs; Toshimitsu Hamasaki; Michael A Proschan; John A Scott; Yuki Ando; Daniel Hind; Douglas G Altman
Journal:  BMJ       Date:  2020-06-17

2.  Estimating treatment effects with treatment switching via semicompeting risks models: an application to a colorectal cancer study.

Authors:  Donglin Zeng; Qingxia Chen; Ming-Hui Chen; Joseph G Ibrahim
Journal:  Biometrika       Date:  2011-12-29       Impact factor: 2.445

3.  Longer-term outcomes of letrozole versus placebo after 5 years of tamoxifen in the NCIC CTG MA.17 trial: analyses adjusting for treatment crossover.

Authors:  Huan Jin; Dongsheng Tu; Naiqing Zhao; Lois E Shepherd; Paul E Goss
Journal:  J Clin Oncol       Date:  2011-10-31       Impact factor: 44.544

4.  Estimating time-varying effects for overdispersed recurrent events data with treatment switching.

Authors:  Qingxia Chen; Donglin Zeng; Joseph G Ibrahim; Mouna Akacha; Heinz Schmidli
Journal:  Biometrika       Date:  2013       Impact factor: 2.445

5.  Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study.

Authors:  James P Morden; Paul C Lambert; Nicholas Latimer; Keith R Abrams; Allan J Wailoo
Journal:  BMC Med Res Methodol       Date:  2011-01-11       Impact factor: 4.615

6.  Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study.

Authors:  Qingxia Chen; Fan Zhang; Ming-Hui Chen; Xiuyu Julie Cong
Journal:  Lifetime Data Anal       Date:  2020-03-03       Impact factor: 1.429

7.  Treatment crossovers in time-to-event non-inferiority randomised trials of radiotherapy in patients with breast cancer.

Authors:  Sameer Parpia; Jim A Julian; Lehana Thabane; Chushu Gu; Timothy J Whelan; Mark N Levine
Journal:  BMJ Open       Date:  2014-10-24       Impact factor: 2.692

8.  The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design.

Authors:  Munyaradzi Dimairo; Philip Pallmann; James Wason; Susan Todd; Thomas Jaki; Steven A Julious; Adrian P Mander; Christopher J Weir; Franz Koenig; Marc K Walton; Jon P Nicholl; Elizabeth Coates; Katie Biggs; Toshimitsu Hamasaki; Michael A Proschan; John A Scott; Yuki Ando; Daniel Hind; Douglas G Altman
Journal:  Trials       Date:  2020-06-17       Impact factor: 2.279

Review 9.  Status of adjuvant endocrine therapy for breast cancer.

Authors:  Gaia Schiavon; Ian E Smith
Journal:  Breast Cancer Res       Date:  2014       Impact factor: 6.466

  9 in total

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