Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • معلومة اضافية
    • Author-Supplied Keywords:
      Research Article
    • Abstract:
      Background: Randomized Controlled Trials almost invariably utilize the hazard ratio calculated with a Cox proportional hazard model as a treatment efficacy measure. Despite the widespread adoption of HRs, these provide a limited understanding of the treatment effect and may even provide a biased estimate when the assumption of proportional hazards in the Cox model is not verified by the trial data. Additional treatment effect measures on the survival probability or the time scale may be used to supplement HRs but a framework for the simultaneous generation of these measures is lacking. Methods: By splitting follow-up time at the nodes of a Gauss Lobatto numerical quadrature rule, techniques for Poisson Generalized Additive Models (PGAM) can be adopted for flexible hazard modeling. Straightforward simulation post-estimation transforms PGAM estimates for the log hazard into estimates of the survival function. These in turn were used to calculate relative and absolute risks or even differences in restricted mean survival time between treatment arms. We illustrate our approach with extensive simulations and in two trials: IPASS (in which the proportionality of hazards was violated) and HEMO a long duration study conducted under evolving standards of care on a heterogeneous patient population. Findings: PGAM can generate estimates of the survival function and the hazard ratio that are essentially identical to those obtained by Kaplan Meier curve analysis and the Cox model. PGAMs can simultaneously provide multiple measures of treatment efficacy after a single data pass. Furthermore, supported unadjusted (overall treatment effect) but also subgroup and adjusted analyses, while incorporating multiple time scales and accounting for non-proportional hazards in survival data. Conclusions: By augmenting the HR conventionally reported, PGAMs have the potential to support the inferential goals of multiple stakeholders involved in the evaluation and appraisal of clinical trial results under proportional and non-proportional hazards. [ABSTRACT FROM AUTHOR]
    • Abstract:
      Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
    • Author Affiliations:
      1Department of Internal Medicine, Division of Nephrology, University of New Mexico, Albuquerque, New Mexico, United States of America
    • ISSN:
      1932-6203
    • Accession Number:
      10.1371/journal.pone.0123784
    • Accession Number:
      102400727
  • Citations
    • ABNT:
      ARGYROPOULOS, C.; UNRUH, M. L. Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models. PLoS ONE, [s. l.], v. 10, n. 4, p. 1–33, 2015. DOI 10.1371/journal.pone.0123784. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=102400727&custid=s8280428. Acesso em: 13 ago. 2020.
    • AMA:
      Argyropoulos C, Unruh ML. Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models. PLoS ONE. 2015;10(4):1-33. doi:10.1371/journal.pone.0123784
    • APA:
      Argyropoulos, C., & Unruh, M. L. (2015). Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models. PLoS ONE, 10(4), 1–33. https://doi.org/10.1371/journal.pone.0123784
    • Chicago/Turabian: Author-Date:
      Argyropoulos, Christos, and Mark L. Unruh. 2015. “Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models.” PLoS ONE 10 (4): 1–33. doi:10.1371/journal.pone.0123784.
    • Harvard:
      Argyropoulos, C. and Unruh, M. L. (2015) ‘Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models’, PLoS ONE, 10(4), pp. 1–33. doi: 10.1371/journal.pone.0123784.
    • Harvard: Australian:
      Argyropoulos, C & Unruh, ML 2015, ‘Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models’, PLoS ONE, vol. 10, no. 4, pp. 1–33, viewed 13 August 2020, .
    • MLA:
      Argyropoulos, Christos, and Mark L. Unruh. “Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models.” PLoS ONE, vol. 10, no. 4, Apr. 2015, pp. 1–33. EBSCOhost, doi:10.1371/journal.pone.0123784.
    • Chicago/Turabian: Humanities:
      Argyropoulos, Christos, and Mark L. Unruh. “Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models.” PLoS ONE 10, no. 4 (April 2015): 1–33. doi:10.1371/journal.pone.0123784.
    • Vancouver/ICMJE:
      Argyropoulos C, Unruh ML. Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models. PLoS ONE [Internet]. 2015 Apr [cited 2020 Aug 13];10(4):1–33. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=102400727&custid=s8280428