Literature DB >> 30282119

The relationship between the C-statistic and the accuracy of program-specific evaluations.

Andrew Wey1, Nicholas Salkowski1, Bertram L Kasiske1,2, Melissa A Skeans1, Sally K Gustafson1, Ajay K Israni1,2,3, Jon J Snyder1,3.   

Abstract

The C-statistic of the risk-adjustment model is often used to judge the accuracy of program evaluations. However, the C-statistic depends on the variability in risk for individual transplants and may be inappropriate for determining the accuracy of program evaluations. A simulation study investigated the association of the C-statistic with several metrics of program evaluation accuracy, including categorizing programs into the 5-tier system and identifying programs for regulatory review. The simulation study used data from deceased donor kidney-alone transplants for adult recipients in the program-specific reports released January 2018. A range of C-statistics was generated by changing the variability in risk for individual transplants. The C-statistic had no association with any metric of program evaluation accuracy. Instead, the number of expected events at a program was the most important factor. For example, Spearman's rho, which is the correlation of ranks, was -0.27 and -0.72 between the true program-specific hazard ratios and assigned tiers for programs with, respectively, <3 and >10 expected events. Presence of unadjusted risk factors did not modify the associations, although the accuracy of program evaluations was systematically lower. Therefore, the C-statistic provides no information on the accuracy of program evaluations. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

Entities:  

Keywords:  Scientific Registry for Transplant Recipients (SRTR); clinical research/practice; organ transplantation in general; statistics

Mesh:

Year:  2018        PMID: 30282119      PMCID: PMC6836685          DOI: 10.1111/ajt.15132

Source DB:  PubMed          Journal:  Am J Transplant        ISSN: 1600-6135            Impact factor:   8.086


  19 in total

1.  Censored quantile regression with recursive partitioning-based weights.

Authors:  Andrew Wey; Lan Wang; Kyle Rudser
Journal:  Biostatistics       Date:  2013-08-23       Impact factor: 5.899

Review 2.  Scientific Registry of Transplant Recipients: collecting, analyzing, and reporting data on transplantation in the United States.

Authors:  Susan Leppke; Tabitha Leighton; David Zaun; Shu-Cheng Chen; Melissa Skeans; Ajay K Israni; Jon J Snyder; Bertram L Kasiske
Journal:  Transplant Rev (Orlando)       Date:  2013-03-06       Impact factor: 3.943

3.  Effect of provider volume on the accuracy of hospital report cards: a Monte Carlo study.

Authors:  Peter C Austin; Mathew J Reeves
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2014-03-11

4.  Potential Implications of Recent and Proposed Changes in the Regulatory Oversight of Solid Organ Transplantation in the United States.

Authors:  B L Kasiske; N Salkowski; A Wey; A K Israni; J J Snyder
Journal:  Am J Transplant       Date:  2016-07-28       Impact factor: 8.086

5.  Utilizing High-Risk Kidneys-Risks, Benefits, and Unintended Consequences?

Authors:  D A Axelrod; J J Friedewald
Journal:  Am J Transplant       Date:  2016-07-14       Impact factor: 8.086

6.  Minimizing Risk Associated With Older Liver Donors by Matching to Preferred Recipients: A National Registry and Validation Study.

Authors:  Christine E Haugen; Alvin G Thomas; Jacqueline Garonzik-Wang; Allan B Massie; Dorry L Segev
Journal:  Transplantation       Date:  2018-09       Impact factor: 4.939

Review 7.  Developing Statistical Models to Assess Transplant Outcomes Using National Registries: The Process in the United States.

Authors:  Jon J Snyder; Nicholas Salkowski; S Joseph Kim; David Zaun; Hui Xiong; Ajay K Israni; Bertram L Kasiske
Journal:  Transplantation       Date:  2016-02       Impact factor: 4.939

8.  Measuring transplant center performance: The goals are not controversial but the methods and consequences can be.

Authors:  Colleen Jay; Jesse D Schold
Journal:  Curr Transplant Rep       Date:  2017-02-08

9.  Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable.

Authors:  Peter C Austin; Ewout W Steyerberg
Journal:  BMC Med Res Methodol       Date:  2012-06-20       Impact factor: 4.615

10.  Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models.

Authors:  Andrew Wey; John Connett; Kyle Rudser
Journal:  Biostatistics       Date:  2015-02-05       Impact factor: 5.279

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  4 in total

1.  A donor risk index for graft loss in pediatric living donor kidney transplantation.

Authors:  Heather L Wasik; Cozumel S Pruette; Rebecca L Ruebner; Mara A McAdams-DeMarco; Sheng Zhou; Alicia M Neu; Dorry L Segev; Allan B Massie
Journal:  Am J Transplant       Date:  2019-04-09       Impact factor: 8.086

2.  A Composite End Point of Graft Status and eGFR at 1 Year to Improve the Scientific Registry of Transplant Recipients' Five-Tier Rating System.

Authors:  Kaicheng Wang; Yanhong Deng; Darren Stewart; Richard N Formica
Journal:  J Am Soc Nephrol       Date:  2022-05-10       Impact factor: 14.978

3.  Predictive Capacity of Risk Models in Liver Transplantation.

Authors:  Jacob D de Boer; Hein Putter; Joris J Blok; Ian P J Alwayn; Bart van Hoek; Andries E Braat
Journal:  Transplant Direct       Date:  2019-05-22

4.  Textbook Outcome as a Quality Metric in Living and Deceased Donor Kidney Transplantation.

Authors:  Austin D Schenk; April J Logan; Jeffrey M Sneddon; Daria Faulkner; Jing L Han; Guy N Brock; William K Washburn
Journal:  J Am Coll Surg       Date:  2022-06-17       Impact factor: 6.532

  4 in total

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