Literature DB >> 20183467

Increasing power in randomized trials with right censored outcomes through covariate adjustment.

K L Moore1, M J van der Laan.   

Abstract

Targeted maximum likelihood methodology is applied to provide a test that makes use of the covariate data that are commonly collected in randomized trials, and does not require assumptions beyond those of the logrank test when censoring is uninformative. Under informative censoring, the logrank test is biased, whereas the test provided in this article is consistent under consistent estimation of the censoring mechanism or the conditional hazard for survival. Two approaches based on this methodology are provided: (1) a substitution-based approach that targets treatment and time-specific survival from which the logrank parameter is estimated, and (2) directly targeting the logrank parameter.

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Year:  2009        PMID: 20183467      PMCID: PMC2895464          DOI: 10.1080/10543400903243017

Source DB:  PubMed          Journal:  J Biopharm Stat        ISSN: 1054-3406            Impact factor:   1.051


  10 in total

1.  Nonparametric analysis of covariance for hypothesis testing with logrank and Wilcoxon scores and survival-rate estimation in a randomized clinical trial.

Authors:  C M Tangen; G G Koch
Journal:  J Biopharm Stat       Date:  1999-05       Impact factor: 1.051

Review 2.  Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals.

Authors:  Angela M Wood; Ian R White; Simon G Thompson
Journal:  Clin Trials       Date:  2004       Impact factor: 2.486

3.  Randomized controlled trials with time-to-event outcomes: how much does prespecified covariate adjustment increase power?

Authors:  Adrián V Hernández; Marinus J C Eijkemans; Ewout W Steyerberg
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4.  The type I error and power of non-parametric logrank and Wilcoxon tests with adjustment for covariates--a simulation study.

Authors:  Honghua Jiang; James Symanowski; Sofia Paul; Yongming Qu; Anthony Zagar; Shengyan Hong
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Review 5.  Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addressing them.

Authors:  G G Koch; C M Tangen; J W Jung; I A Amara
Journal:  Stat Med       Date:  1998 Aug 15-30       Impact factor: 2.373

6.  Power of logrank test and Cox regression model in clinical trials with heterogeneous samples.

Authors:  K Akazawa; T Nakamura; Y Palesch
Journal:  Stat Med       Date:  1997-03-15       Impact factor: 2.373

7.  Covariate adjustment in randomized trials with binary outcomes: targeted maximum likelihood estimation.

Authors:  K L Moore; M J van der Laan
Journal:  Stat Med       Date:  2009-01-15       Impact factor: 2.373

8.  Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems.

Authors:  Stuart J Pocock; Susan E Assmann; Laura E Enos; Linda E Kasten
Journal:  Stat Med       Date:  2002-10-15       Impact factor: 2.373

9.  Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: a principled yet flexible approach.

Authors:  Anastasios A Tsiatis; Marie Davidian; Min Zhang; Xiaomin Lu
Journal:  Stat Med       Date:  2008-10-15       Impact factor: 2.373

10.  Improving efficiency of inferences in randomized clinical trials using auxiliary covariates.

Authors:  Min Zhang; Anastasios A Tsiatis; Marie Davidian
Journal:  Biometrics       Date:  2008-01-11       Impact factor: 1.701

  10 in total
  7 in total

1.  A targeted maximum likelihood estimator for two-stage designs.

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2.  Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards.

Authors:  Iván Díaz; Elizabeth Colantuoni; Daniel F Hanley; Michael Rosenblum
Journal:  Lifetime Data Anal       Date:  2018-02-28       Impact factor: 1.588

3.  Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes.

Authors:  Melanie Prague; Rui Wang; Alisa Stephens; Eric Tchetgen Tchetgen; Victor DeGruttola
Journal:  Biometrics       Date:  2016-04-08       Impact factor: 2.571

4.  Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes.

Authors:  David Benkeser; Iván Díaz; Alex Luedtke; Jodi Segal; Daniel Scharfstein; Michael Rosenblum
Journal:  medRxiv       Date:  2020-06-11

5.  Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes.

Authors:  David Benkeser; Iván Díaz; Alex Luedtke; Jodi Segal; Daniel Scharfstein; Michael Rosenblum
Journal:  Biometrics       Date:  2020-10-11       Impact factor: 1.701

6.  Optimising precision and power by machine learning in randomised trials with ordinal and time-to-event outcomes with an application to COVID-19.

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Journal:  J R Stat Soc Ser A Stat Soc       Date:  2022-09-23       Impact factor: 2.175

7.  Using Mobile Integrated Health and telehealth to support transitions of care among patients with heart failure (MIGHTy-Heart): protocol for a pragmatic randomised controlled trial.

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Journal:  BMJ Open       Date:  2022-03-10       Impact factor: 2.692

  7 in total

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