Literature DB >> 24014891

Evaluating Joint Effects of Induction-Salvage Treatment Regimes on Overall Survival in Acute Leukemia.

Abdus S Wahed1, Peter F Thall.   

Abstract

Typical oncology practice often includes not only an initial, frontline treatment, but also subsequent treatments given if the initial treatment fails. The physician chooses a treatment at each stage based on the patient's baseline covariates and history of previous treatments and outcomes. Such sequentially adaptive medical decision-making processes are known as dynamic treatment regimes, treatment policies, or multi-stage adaptive treatment strategies. Conventional analyses in terms of frontline treatments that ignore subsequent treatments may be misleading, because they actually are an evaluation of more than front-line treatment effects on outcome. We are motivated by data from a randomized trial of four combination chemotherapies given as frontline treatments to patients with acute leukemia. Most patients in the trial also received a second-line treatment, chosen adaptively and subjectively rather than by randomization, either because the initial treatment was ineffective or the patient's cancer later recurred. We evaluate effects on overall survival time of the 16 two-stage strategies that actually were used. Our methods include a likelihood-based regression approach in which the transition times of all possible multi-stage outcome paths are modeled, and estimating equations with inverse probability of treatment weighting to correct for bias. While the two approaches give different numerical estimates of mean survival time, they lead to the same substantive conclusions when comparing the two-stage regimes.

Entities:  

Keywords:  Causal inference; Clinical trial; Dynamic treatment regime; Treatment policy

Year:  2013        PMID: 24014891      PMCID: PMC3762505          DOI: 10.1111/j.1467-9876.2012.01048.x

Source DB:  PubMed          Journal:  J R Stat Soc Ser C Appl Stat        ISSN: 0035-9254            Impact factor:   1.864


  14 in total

1.  Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials.

Authors:  Jared K Lunceford; Marie Davidian; Anastasios A Tsiatis
Journal:  Biometrics       Date:  2002-03       Impact factor: 2.571

2.  Optimal estimator for the survival distribution and related quantities for treatment policies in two-stage randomization designs in clinical trials.

Authors:  Abdus S Wahed; Anastasios A Tsiatis
Journal:  Biometrics       Date:  2004-03       Impact factor: 2.571

3.  An experimental design for the development of adaptive treatment strategies.

Authors:  S A Murphy
Journal:  Stat Med       Date:  2005-05-30       Impact factor: 2.373

4.  Dynamic treatment regimes: practical design considerations.

Authors:  Philip W Lavori; Ree Dawson
Journal:  Clin Trials       Date:  2004-02       Impact factor: 2.486

5.  Estimation and extrapolation of optimal treatment and testing strategies.

Authors:  James Robins; Liliana Orellana; Andrea Rotnitzky
Journal:  Stat Med       Date:  2008-10-15       Impact factor: 2.373

6.  Marginal Mean Models for Dynamic Regimes.

Authors:  S A Murphy; M J van der Laan; J M Robins
Journal:  J Am Stat Assoc       Date:  2001-12-01       Impact factor: 5.033

7.  Reinforcement learning design for cancer clinical trials.

Authors:  Yufan Zhao; Michael R Kosorok; Donglin Zeng
Journal:  Stat Med       Date:  2009-11-20       Impact factor: 2.373

8.  Randomized phase II study of fludarabine + cytosine arabinoside + idarubicin +/- all-trans retinoic acid +/- granulocyte colony-stimulating factor in poor prognosis newly diagnosed acute myeloid leukemia and myelodysplastic syndrome.

Authors:  E H Estey; P F Thall; S Pierce; J Cortes; M Beran; H Kantarjian; M J Keating; M Andreeff; E Freireich
Journal:  Blood       Date:  1999-04-15       Impact factor: 22.113

9.  Evaluating multiple treatment courses in clinical trials.

Authors:  P F Thall; R E Millikan; H G Sung
Journal:  Stat Med       Date:  2000-04-30       Impact factor: 2.373

10.  Adaptive therapy for androgen-independent prostate cancer: a randomized selection trial of four regimens.

Authors:  Peter F Thall; Christopher Logothetis; Lance C Pagliaro; Sijin Wen; Melissa A Brown; Dallas Williams; Randall E Millikan
Journal:  J Natl Cancer Inst       Date:  2007-10-30       Impact factor: 13.506

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  11 in total

1.  Comment.

Authors:  Qian Guan; Eric B Laber; Brian J Reich
Journal:  J Am Stat Assoc       Date:  2016-10-18       Impact factor: 5.033

2.  Direct estimation for adaptive treatment length policies: Methods and application to evaluating the effect of delayed PEG insertion.

Authors:  Xin Lu; Brent A Johnson
Journal:  Biometrics       Date:  2016-12-23       Impact factor: 2.571

3.  Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.

Authors:  Yanxun Xu; Peter Müller; Abdus S Wahed; Peter F Thall
Journal:  J Am Stat Assoc       Date:  2016-10-18       Impact factor: 5.033

4.  Optimization of multi-stage dynamic treatment regimes utilizing accumulated data.

Authors:  Xuelin Huang; Sangbum Choi; Lu Wang; Peter F Thall
Journal:  Stat Med       Date:  2015-06-21       Impact factor: 2.373

5.  A cure-rate model for Q-learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients.

Authors:  Erica E M Moodie; David A Stephens; Shomoita Alam; Mei-Jie Zhang; Brent Logan; Mukta Arora; Stephen Spellman; Elizabeth F Krakow
Journal:  Biom J       Date:  2018-05-16       Impact factor: 2.207

6.  Optimization of individualized dynamic treatment regimes for recurrent diseases.

Authors:  Xuelin Huang; Jing Ning; Abdus S Wahed
Journal:  Stat Med       Date:  2014-02-09       Impact factor: 2.373

7.  SMART designs in cancer research: Past, present, and future.

Authors:  Kelley M Kidwell
Journal:  Clin Trials       Date:  2014-04-14       Impact factor: 2.486

8.  Bayesian nonparametric statistics: A new toolkit for discovery in cancer research.

Authors:  Peter F Thall; Peter Mueller; Yanxun Xu; Michele Guindani
Journal:  Pharm Stat       Date:  2017-07-04       Impact factor: 1.894

9.  A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome.

Authors:  Jason Roy; Kirsten J Lum; Michael J Daniels
Journal:  Biostatistics       Date:  2016-06-26       Impact factor: 5.279

10.  Application of causal inference methods in the analyses of randomised controlled trials: a systematic review.

Authors:  Ruth E Farmer; Daphne Kounali; A Sarah Walker; Jelena Savović; Alison Richards; Margaret T May; Deborah Ford
Journal:  Trials       Date:  2018-01-10       Impact factor: 2.279

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