Literature DB >> 26363892

Comparing treatment policies with assistance from the structural nested mean model.

Xi Lu1, Kevin G Lynch2, David W Oslin2, Susan Murphy1.   

Abstract

Treatment policies, also known as dynamic treatment regimes, are sequences of decision rules that link the observed patient history with treatment recommendations. Multiple, plausible, treatment policies are frequently constructed by researchers using expert opinion, theories, and reviews of the literature. Often these different policies represent competing approaches to managing an illness. Here, we develop an "assisted estimator" that can be used to compare the mean outcome of competing treatment policies. The term "assisted" refers to the fact estimators from the Structural Nested Mean Model, a parametric model for the causal effect of treatment at each time point, are used in the process of estimating the mean outcome. This work is motivated by our work on comparing the mean outcome of two competing treatment policies using data from the ExTENd study in alcohol dependence.
© 2015, The International Biometric Society.

Entities:  

Keywords:  Adaptive intervention; Dynamic treatment regime; Semiparametric model; Sequential multiple assignment randomized trial

Mesh:

Substances:

Year:  2015        PMID: 26363892      PMCID: PMC4789134          DOI: 10.1111/biom.12391

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  13 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

Review 2.  Flexible treatment strategies in chronic disease: clinical and research implications.

Authors:  P W Lavori; R Dawson; A J Rush
Journal:  Biol Psychiatry       Date:  2000-09-15       Impact factor: 13.382

3.  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

4.  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

5.  Dynamic treatment regimes: practical design considerations.

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

6.  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

7.  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

8.  Experimental design and primary data analysis methods for comparing adaptive interventions.

Authors:  Inbal Nahum-Shani; Min Qian; Daniel Almirall; William E Pelham; Beth Gnagy; Gregory A Fabiano; James G Waxmonsky; Jihnhee Yu; Susan A Murphy
Journal:  Psychol Methods       Date:  2012-10-01

9.  Reinforcement-based treatment improves the maternal treatment and neonatal outcomes of pregnant patients enrolled in comprehensive care treatment.

Authors:  Hendrée E Jones; Kevin E O'Grady; Michelle Tuten
Journal:  Am J Addict       Date:  2011-03-08

10.  Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions.

Authors:  Baqun Zhang; Anastasios A Tsiatis; Eric B Laber; Marie Davidian
Journal:  Biometrika       Date:  2013       Impact factor: 2.445

View more
  2 in total

1.  Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

Authors:  Timothy NeCamp; Amy Kilbourne; Daniel Almirall
Journal:  Stat Methods Med Res       Date:  2017-06-19       Impact factor: 3.021

2.  Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects.

Authors:  Tianjiao Dai; Sanjay Shete
Journal:  BMC Med Res Methodol       Date:  2016-08-30       Impact factor: 4.615

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.