Literature DB >> 24436222

Methods for comparing center-specific survival outcomes using direct standardization.

Kevin He1, Douglas E Schaubel.   

Abstract

The evaluation of center-specific outcomes is often through survival analysis methods. Such evaluations must account for differences in the distribution of patient characteristics across centers. In the context of censored event times, it is also important that the measure chosen to evaluate centers not be influenced by imbalances in the center-specific censoring distributions. The practice of using center indicators in a hazard regression model is often invalid, inconvenient, or undesirable to carry out. We propose a semiparametric version of the standardized rate ratio (SRR) useful for the evaluation of centers with respect to a right-censored event time. The SRR for center j can be interpreted as the ratio of the expected number of deaths in the total population (if the total population were in fact subject to the center j mortality hazard) to the observed number of events. The proposed measure is not affected by differences in center-specific covariate or censoring distributions. Asymptotic properties of the proposed estimators are derived, with finite-sample properties examined through simulation studies. The proposed methods are applied to national kidney transplant data.
Copyright © 2014 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Cox regression; center effect; standardized rate ratio; stratification; survival analysis

Mesh:

Year:  2014        PMID: 24436222      PMCID: PMC4013227          DOI: 10.1002/sim.6089

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


  7 in total

1.  The method of expected number of deaths, 1786-1886-1986.

Authors:  N Keiding
Journal:  Int Stat Rev       Date:  1987-04       Impact factor: 2.217

2.  SRTR center-specific reporting tools: Posttransplant outcomes.

Authors:  D M Dickinson; T H Shearon; J O'Keefe; H-H Wong; C L Berg; J D Rosendale; F L Delmonico; R L Webb; R A Wolfe
Journal:  Am J Transplant       Date:  2006       Impact factor: 8.086

3.  The analysis of mortality by the subject-years method.

Authors:  G Berry
Journal:  Biometrics       Date:  1983-03       Impact factor: 2.571

Review 4.  The standardized mortality ratio revisited: improvements, innovations, and limitations.

Authors:  R A Wolfe
Journal:  Am J Kidney Dis       Date:  1994-08       Impact factor: 8.860

5.  Using USRDS generated mortality tables to compare local ESRD mortality rates to national rates.

Authors:  R A Wolfe; D S Gaylin; F K Port; P J Held; C L Wood
Journal:  Kidney Int       Date:  1992-10       Impact factor: 10.612

6.  Analyzing center specific outcomes in hematopoietic cell transplantation.

Authors:  Brent R Logan; Gene O Nelson; John P Klein
Journal:  Lifetime Data Anal       Date:  2008-10-03       Impact factor: 1.588

7.  Flexible random-effects models using Bayesian semi-parametric models: applications to institutional comparisons.

Authors:  D I Ohlssen; L D Sharples; D J Spiegelhalter
Journal:  Stat Med       Date:  2007-04-30       Impact factor: 2.373

  7 in total
  3 in total

1.  Evaluating center performance in the competing risks setting: Application to outcomes of wait-listed end-stage renal disease patients.

Authors:  Sai H Dharmarajan; Douglas E Schaubel; Rajiv Saran
Journal:  Biometrics       Date:  2017-07-06       Impact factor: 2.571

2.  Stratified Cox models with time-varying effects for national kidney transplant patients: A new blockwise steepest ascent method.

Authors:  Kevin He; Ji Zhu; Jian Kang; Yi Li
Journal:  Biometrics       Date:  2021-05-04       Impact factor: 1.701

3.  The Effect of Transplant Volume and Patient Case Mix on Center Variation in Kidney Transplantation Outcomes.

Authors:  Anne Tsampalieros; Dean Fergusson; Stephanie Dixon; Shane W English; Douglas Manuel; Carl Van Walraven; Monica Taljaard; Greg A Knoll
Journal:  Can J Kidney Health Dis       Date:  2019-09-20
  3 in total

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