A stochastic model for calibrating the survival benefit of screen-detected cancers

Hsiu Hsi Chen, Amy Ming Fang Yen, Laszlo Tabár

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Comparison of the survival of clinically detected and screen-detected cancer cases from either population-based service screening programs or opportunistic screening is often distorted by both lead-time and length biases. Both are correlated with each other and are also affected by measurement errors and tumor attributes such as regional lymph node spread. We propose a general stochastic approach to calibrate the survival benefit of screen-detected cancers related to both biases, measurement errors, and tumor attributes. We apply our proposed method to breast cancer screening data from one arm of the Swedish Two-County trial in the trial period together with the subsequent service screening for the same cohort. When there is no calibration, the results-assuming a constant (exponentially distributed) post-lead-time hazard rate (i. e., a homogeneous stochastic process)-show a 57% reduction in breast cancer death over 25 years. After correction, the reduction was 30%, with approximately 12% of the overestimation being due to lead-time bias and 15% due to length bias. The additional impacts of measurement errors (sensitivity and specificity) depend on the type of the proposed model and follow-up time. The corresponding analysis when the Weibull distribution was applied-relaxing the assumption of a constant hazard rate-yielded similar findings and lacked statistical significance compared with the exponential model. The proposed calibration approach allows the benefit of a service cancer screening program to be fairly evaluated. This article has supplementary materials online.

Original languageEnglish
Pages (from-to)1339-1359
Number of pages21
JournalJournal of the American Statistical Association
Volume107
Issue number500
DOIs
Publication statusPublished - 2012

Fingerprint

Screening
Stochastic Model
Cancer
Measurement Error
Hazard Rate
Breast Cancer
Tumor
Calibration
Attribute
Exponential Model
Statistical Significance
Weibull Distribution
Specificity
Stochastic Processes
Stochastic model
Lead time
Measurement error
Vertex of a graph
Breast cancer
Cancer screening

Keywords

  • Calibration
  • Lead-time bias
  • Length bias
  • Screening
  • Survival

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

A stochastic model for calibrating the survival benefit of screen-detected cancers. / Chen, Hsiu Hsi; Yen, Amy Ming Fang; Tabár, Laszlo.

In: Journal of the American Statistical Association, Vol. 107, No. 500, 2012, p. 1339-1359.

Research output: Contribution to journalArticle

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