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The sensitivity and specificity of markers for event times.

TitleThe sensitivity and specificity of markers for event times.
Publication TypeJournal Article
Year of Publication2006
AuthorsCai, T, Pepe, MSullivan, Zheng, Y, Lumley, T, Jenny, NSwords
JournalBiostatistics
Volume7
Issue2
Pagination182-97
Date Published2006 Apr
ISSN1465-4644
KeywordsAged, Aged, 80 and over, Biomarkers, Biometry, Cardiovascular Diseases, Female, Humans, Male, Models, Statistical, Risk Factors, ROC Curve, Sensitivity and Specificity, Time Factors
Abstract<p>The statistical literature on assessing the accuracy of risk factors or disease markers as diagnostic tests deals almost exclusively with settings where the test, Y, is measured concurrently with disease status D. In practice, however, disease status may vary over time and there is often a time lag between when the marker is measured and the occurrence of disease. One example concerns the Framingham risk score (FR-score) as a marker for the future risk of cardiovascular events, events that occur after the score is ascertained. To evaluate such a marker, one needs to take the time lag into account since the predictive accuracy may be higher when the marker is measured closer to the time of disease occurrence. We therefore consider inference for sensitivity and specificity functions that are defined as functions of time. Semiparametric regression models are proposed. Data from a cohort study are used to estimate model parameters. One issue that arises in practice is that event times may be censored. In this research, we extend in several respects the work by Leisenring et al. (1997) that dealt only with parametric models for binary tests and uncensored data. We propose semiparametric models that accommodate continuous tests and censoring. Asymptotic distribution theory for parameter estimates is developed and procedures for making statistical inference are evaluated with simulation studies. We illustrate our methods with data from the Cardiovascular Health Study, relating the FR-score measured at enrollment to subsequent risk of cardiovascular events.</p>
DOI10.1093/biostatistics/kxi047
Alternate JournalBiostatistics
PubMed ID16079162
Grant ListAI29168 / AI / NIAID NIH HHS / United States
GM-54438 / GM / NIGMS NIH HHS / United States
N01-HC-15103 / HC / NHLBI NIH HHS / United States
N01-HC-35129 / HC / NHLBI NIH HHS / United States
N01-HC-85079 / HC / NHLBI NIH HHS / United States
N01-HC-85086 / HC / NHLBI NIH HHS / United States