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Covariate-adjusted measures of discrimination for survival data.

TitleCovariate-adjusted measures of discrimination for survival data.
Publication TypeJournal Article
Year of Publication2015
AuthorsWhite IR
Secondary AuthorsRapsomaniki E
Corporate AuthorsEmerging Risk Factors Collaboration
JournalBiom J
Volume57
Issue4
Pagination592-613
Date Published2015 Jul
ISSN1521-4036
KeywordsAnalysis of Variance, Biometry, Cardiovascular Diseases, Clinical Trials as Topic, Discriminant Analysis, Female, Humans, Male, Middle Aged, Risk Factors, Survival Analysis
Abstract

MOTIVATION: Discrimination statistics describe the ability of a survival model to assign higher risks to individuals who experience earlier events: examples are Harrell's C-index and Royston and Sauerbrei's D, which we call the D-index. Prognostic covariates whose distributions are controlled by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination statistics in censored survival data.

OBJECTIVE: To develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s).

METHOD: We define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration.

RESULTS: The proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were substantially smaller than unadjusted values. The study-specific standard deviation of baseline age was strongly associated with the unadjusted C-index and D-index but not significantly associated with the age-adjusted indices.

CONCLUSIONS: The proposed estimators improve meta-analysis comparisons, are easy to implement and give a more meaningful clinical interpretation.

DOI10.1002/bimj.201400061
Alternate JournalBiom J
PubMed ID25530064
PubMed Central IDPMC4666552
Grant ListG19/35 / / Medical Research Council / United Kingdom
G0100222 / / Medical Research Council / United Kingdom
RG/08/014/24067 / / British Heart Foundation / United Kingdom
G8802774 / / Medical Research Council / United Kingdom
G0902037 / / Medical Research Council / United Kingdom
MR/L003120/1 / / Medical Research Council / United Kingdom
UL1 TR000062 / TR / NCATS NIH HHS / United States
G1000616 / / Medical Research Council / United Kingdom
MR/K013351/1 / / Medical Research Council / United Kingdom
G0700463 / / Medical Research Council / United Kingdom
MC_UU_12013/5 / / Medical Research Council / United Kingdom
UL1 TR001450 / TR / NCATS NIH HHS / United States
RG/07/008/23674 / / British Heart Foundation / United Kingdom