Literature DB >> 7997708

Analysing survival in the presence of an auxiliary variable.

D M Finkelstein1, D A Schoenfeld.   

Abstract

A primary endpoint of AIDS trials is the length of survival. Often there is auxiliary information available on measures of disease progression, which significantly alter the risk of mortality. This paper explores the use of this information in obtaining a refined estimate of survival, and a test based on this estimate. The methods are applied to an AIDS clinical trial, and results of simulations are provided which compare the approach to standard methods for survival analysis.

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Year:  1994        PMID: 7997708     DOI: 10.1002/sim.4780131706

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


  9 in total

1.  Nonparametric tests for stratum effects in the Cox model.

Authors:  J Sun; I Yang
Journal:  Lifetime Data Anal       Date:  2000-12       Impact factor: 1.588

2.  Using auxiliary time-dependent covariates to recover information in nonparametric testing with censored data.

Authors:  S Murray; A A Tsiatis
Journal:  Lifetime Data Anal       Date:  2001-06       Impact factor: 1.588

3.  Assessing survival benefit when treatment delays disease progression.

Authors:  David A Schoenfeld; Dianne M Finkelstein
Journal:  Clin Trials       Date:  2016-02-22       Impact factor: 2.486

4.  Surrogate endpoint analysis: an exercise in extrapolation.

Authors:  Stuart G Baker; Barnett S Kramer
Journal:  J Natl Cancer Inst       Date:  2012-12-21       Impact factor: 13.506

5.  A model-informed rank test for right-censored data with intermediate states.

Authors:  Ritesh Ramchandani; Dianne M Finkelstein; David A Schoenfeld
Journal:  Stat Med       Date:  2015-01-13       Impact factor: 2.373

6.  Improving efficiency in clinical trials using auxiliary information: Application of a multi-state cure model.

Authors:  A S C Conlon; J M G Taylor; D J Sargent
Journal:  Biometrics       Date:  2015-01-13       Impact factor: 2.571

7.  Comparing the survival of two groups with an intermediate clinical event.

Authors:  C M Nam; M Zelen
Journal:  Lifetime Data Anal       Date:  2001-03       Impact factor: 1.588

8.  Landmark estimation of survival and treatment effects in observational studies.

Authors:  Layla Parast; Beth Ann Griffin
Journal:  Lifetime Data Anal       Date:  2016-02-15       Impact factor: 1.588

9.  Survival methods, including those using competing risk analysis, are not appropriate for intensive care unit outcome studies.

Authors:  David Schoenfeld
Journal:  Crit Care       Date:  2006-02       Impact factor: 9.097

  9 in total

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