Literature DB >> 1485054

Analysis of a clinical trial involving a combined mortality and adherence dependent interval censored endpoint.

L A Moyé1, B R Davis, C M Hawkins.   

Abstract

Clinical trials often involve a variety of clinical and laboratory measures that are used as endpoints and sometimes two of these measures are combined in one endpoint. When the individual components of such a combined endpoint are 'time to event' measurements, the analysis is straightforward if each of the components is measured frequently and regularly over time. However, the analysis of the combined endpoint is more difficult when one component of the endpoint is right censored and the other is interval censored. This paper describes a statistic, based on a rank ordering of events for such a combined measure. The power of the test statistic is explored.

Mesh:

Year:  1992        PMID: 1485054     DOI: 10.1002/sim.4780111305

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


  9 in total

1.  Power and sample size calculations for the Wilcoxon-Mann-Whitney test in the presence of death-censored observations.

Authors:  Roland A Matsouaka; Rebecca A Betensky
Journal:  Stat Med       Date:  2014-11-13       Impact factor: 2.373

2.  An optimal Wilcoxon-Mann-Whitney test of mortality and a continuous outcome.

Authors:  Roland A Matsouaka; Aneesh B Singhal; Rebecca A Betensky
Journal:  Stat Methods Med Res       Date:  2016-12-29       Impact factor: 3.021

3.  Alive and Ventilator Free: A Hierarchical, Composite Outcome for Clinical Trials in the Acute Respiratory Distress Syndrome.

Authors:  Victor Novack; Jeremy R Beitler; Maayan Yitshak-Sade; B Taylor Thompson; David A Schoenfeld; Gordon Rubenfeld; Daniel Talmor; Samuel M Brown
Journal:  Crit Care Med       Date:  2020-02       Impact factor: 7.598

4.  Combining censored and uncensored data in a U-statistic: design and sample size implications for cell therapy research.

Authors:  Lemuel A Moyé; Dejian Lai; Kaiyan Jing; Mary Sarah Baraniuk; Minjung Kwak; Marc S Penn; Colon O Wu
Journal:  Int J Biostat       Date:  2011-07-22       Impact factor: 0.968

5.  A robust method for comparing two treatments in a confirmatory clinical trial via multivariate time-to-event methods that jointly incorporate information from longitudinal and time-to-event data.

Authors:  Benjamin R Saville; Amy H Herring; Gary G Koch
Journal:  Stat Med       Date:  2010-01-15       Impact factor: 2.373

6.  Use of alternative methodologies for evaluation of composite end points in trials of therapies for critical limb ischemia.

Authors:  Sumeet Subherwal; Kevin J Anstrom; William S Jones; Michael G Felker; Sanjay Misra; Michael S Conte; William R Hiatt; Manesh R Patel
Journal:  Am Heart J       Date:  2012-09       Impact factor: 4.749

7.  Global rank tests for multiple, possibly censored, outcomes.

Authors:  Ritesh Ramchandani; David A Schoenfeld; Dianne M Finkelstein
Journal:  Biometrics       Date:  2016-01-26       Impact factor: 2.571

Review 8.  Statistical Methods for Cardiovascular Researchers.

Authors:  Lem Moyé
Journal:  Circ Res       Date:  2016-02-05       Impact factor: 17.367

9.  Applying a Risk-benefit Analysis to Outcomes in Tuberculosis Clinical Trials.

Authors:  Sachiko Miyahara; Ritesh Ramchandani; Soyeon Kim; Scott R Evans; Amita Gupta; Susan Swindells; Richard E Chaisson; Grace Montepiedra
Journal:  Clin Infect Dis       Date:  2020-02-03       Impact factor: 9.079

  9 in total

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