Literature DB >> 31729633

Group sequential tests for treatment effect on survival and cumulative incidence at a fixed time point.

Michael J Martens1, Brent R Logan2.   

Abstract

Medical research frequently involves comparing an event time of interest between treatment groups. Rather than comparing the entire survival or cumulative incidence curves, it is sometimes preferable to evaluate these probabilities at a fixed point in time. Performing a covariate adjusted analysis can improve efficiency, even in randomized clinical trials, but no currently available group sequential test for fixed point analysis provides this adjustment. This paper introduces covariate adjusted group sequential pointwise comparisons of survival and cumulative incidence probabilities. Their test statistics have an asymptotic distribution with independent increments, permitting use of common stopping boundary specification methods. These tests are demonstrated through a redesign of BMT CTN 0402, a clinical trial that evaluated a prophylactic treatment for adverse outcomes following blood and marrow transplantation. A simulation study demonstrates that these tests maintain the type I error rate and power at nominal levels under a variety of settings involving influential covariates.

Entities:  

Keywords:  Competing risks; Direct binomial regression; Graft versus host disease; Group sequential design; Hematopoietic cell transplantation; Survival analysis

Year:  2019        PMID: 31729633      PMCID: PMC7365590          DOI: 10.1007/s10985-019-09491-z

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  12 in total

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Authors:  John P Klein; Per Kragh Andersen
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2.  A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model.

Authors:  Xu Zhang; Fausto R Loberiza; John P Klein; Mei-Jie Zhang
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Review 3.  Should we adjust for covariates in nonlinear regression analyses of randomized trials?

Authors:  W W Hauck; S Anderson; S M Marcus
Journal:  Control Clin Trials       Date:  1998-06

4.  Group sequential designs for monitoring survival probabilities.

Authors:  D Y Lin; L Shen; Z Ying; N E Breslow
Journal:  Biometrics       Date:  1996-09       Impact factor: 2.571

5.  Continuous covariate imbalance and conditional power for clinical trial interim analyses.

Authors:  Jody D Ciolino; Renee' H Martin; Wenle Zhao; Edward C Jauch; Michael D Hill; Yuko Y Palesch
Journal:  Contemp Clin Trials       Date:  2014-03-07       Impact factor: 2.226

6.  Measuring continuous baseline covariate imbalances in clinical trial data.

Authors:  Jody D Ciolino; Reneé H Martin; Wenle Zhao; Michael D Hill; Edward C Jauch; Yuko Y Palesch
Journal:  Stat Methods Med Res       Date:  2011-08-24       Impact factor: 3.021

7.  Marginal models for clustered time-to-event data with competing risks using pseudovalues.

Authors:  Brent R Logan; Mei-Jie Zhang; John P Klein
Journal:  Biometrics       Date:  2011-03       Impact factor: 2.571

8.  A group sequential test for treatment effect based on the Fine-Gray model.

Authors:  Michael J Martens; Brent R Logan
Journal:  Biometrics       Date:  2018-03-13       Impact factor: 2.571

9.  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

10.  The use of group sequential designs with common competing risks tests.

Authors:  Brent R Logan; Mei-Jie Zhang
Journal:  Stat Med       Date:  2012-09-04       Impact factor: 2.373

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