A stochastic model for survival of early prostate cancer with adjustments for leadtime, length bias, and over-detection

Grace Hui Min Wu, Anssi Auvinen, Amy Ming Fang Yen, Matti Hakama, Stephen D. Walter, Hsiu Hsi Chen

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

To compare the survival between screen-detected and clinically detected cancers, we applied a series of non-homogeneous stochastic processes to deal with leadtime, length bias, and over-detection by using full information on detection modes obtained from the Finnish randomized controlled trial for prostate cancer screening. The results show after 9-year follow-up the hazard ratio of prostate cancer death for screen-detected cases against clinically detected cases increased from 0.24 (95% CI: 0.16-0.35) without correction for these biases, to 0.76 after correction for leadtime and length biases, and finally to 1.03 (95% CI: 0.79-1.33) for a further adjustment for over-detection. Adjustment for leadtime and length bias but no over-detection led to a 24% reduction in prostate cancer death as a result of prostate-specific antigen test. The further calibration of over-detection indicates no gain in survival of screen-detected prostate cancers (excluding over-detected case as stayer considered in the mover-stayer model) as compared with the control group in the absence of screening that is considered as the mover. However, whether the model assumption on over-detection is robust should be validated with other data sets and longer follow-up.

Original languageEnglish
Pages (from-to)20-44
Number of pages25
JournalBiometrical Journal
Volume54
Issue number1
DOIs
Publication statusPublished - Jan 2012

Fingerprint

Prostate Cancer
Stochastic Model
Prostatic Neoplasms
Adjustment
Stochastic Processes
Screening
Prostate-Specific Antigen
Early Detection of Cancer
Calibration
Randomized Controlled Trial
Randomized Controlled Trials
Hazard
Control Groups
Prostate cancer
Stochastic model
Lead time
Cancer
Neoplasms
Series
Model

Keywords

  • Leadtime and length bias
  • Mass screening
  • Prostate neoplasms
  • Prostate-specific antigen
  • Stochastic processes

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Statistics, Probability and Uncertainty

Cite this

A stochastic model for survival of early prostate cancer with adjustments for leadtime, length bias, and over-detection. / Wu, Grace Hui Min; Auvinen, Anssi; Yen, Amy Ming Fang; Hakama, Matti; Walter, Stephen D.; Chen, Hsiu Hsi.

In: Biometrical Journal, Vol. 54, No. 1, 01.2012, p. 20-44.

Research output: Contribution to journalArticle

Wu, Grace Hui Min ; Auvinen, Anssi ; Yen, Amy Ming Fang ; Hakama, Matti ; Walter, Stephen D. ; Chen, Hsiu Hsi. / A stochastic model for survival of early prostate cancer with adjustments for leadtime, length bias, and over-detection. In: Biometrical Journal. 2012 ; Vol. 54, No. 1. pp. 20-44.
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