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 journalArticlepeer-review

10 Citations (Scopus)


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
Issue number500
Publication statusPublished - 2012


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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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