Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker

ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a ti...

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Vydané v:Biometrics Ročník 56; číslo 2; s. 337 - 344
Hlavní autori: Heagerty, Patrick J., Lumley, Thomas, Pepe, Margaret S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford, UK Blackwell Publishing Ltd 01.06.2000
International Biometric Society
Predmet:
Accuracy
biometry
Breast cancer
breast neoplasms
Breast Neoplasms - diagnosis
Breast Neoplasms - mortality
Censored data
Censorship
Cytometry
death
Discrimination
Estimators
Female
flow cytometry
Health outcomes
Humans
Infections
Kaplan Meier estimator
Kernel smoothing
patients
prediction
Reliability functions
ROC Curve
Sample size
Sensitivity
Sensitivity and Specificity
Specificity
Survival Analysis
Survival Rate
Time Factors
Treatment Outcome
ISSN:0006-341X, 1541-0420
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Popis
Shrnutí:ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.
Bibliografia:istex:6FD158F501B633EC7C0521D83477464D0E76F19E
ark:/67375/WNG-5HHCMRGX-T
ArticleID:BIOM337
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0006-341X
1541-0420
DOI:10.1111/j.0006-341X.2000.00337.x