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Estimating time-dependent ROC curves using data under prevalent sampling.

Shanshan Li1

  • 1Department of Biostatistics, Indiana University Fairbanks School of Public Health, Indianapolis, 46202, IN, U.S.A.

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Summary
This summary is machine-generated.

Prevalent sampling in time-to-event studies is biased. This research introduces new methods to correct this bias for accurate survival analysis and receiver operating characteristic curve estimation.

Keywords:
false positive rateleft truncationnonparametric estimatorprevalent samplingproportional odds modeltrue positive rate

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Prevalent sampling is a common, economical method for collecting time-to-event data in disease natural history studies.
  • This sampling method is inherently biased, favoring individuals with longer survival times.
  • Accurate estimation of diagnostic test performance over time is crucial in clinical research.

Purpose of the Study:

  • To develop statistical methods for estimating time-dependent receiver operating characteristic (ROC) curves using prevalent sampling data.
  • To address and correct for the inherent bias introduced by prevalent sampling in survival data.
  • To evaluate the performance of novel estimators compared to standard methods when dealing with biased sampling.

Main Methods:

  • Development of nonparametric and semiparametric estimators.
  • Utilizing extended risk sets and inverse probability weighting techniques to correct for sampling bias.
  • Application of the developed methods to analyze ovarian cancer data.

Main Results:

  • The proposed estimators are shown to be consistent and converge to Gaussian processes.
  • Significant bias can occur if standard estimators for right-censored data are applied to prevalent samples.
  • The method was illustrated by estimating ROC curves for composite markers in an ovarian cancer cohort.

Conclusions:

  • The developed estimators provide a statistically sound approach for analyzing time-to-event data collected via prevalent sampling.
  • Correcting for prevalent sampling bias is essential for reliable estimation of time-dependent ROC curves.
  • The findings have implications for improving accuracy in prognostic marker evaluation in various disease studies.