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

Authors

  • Grace Hui-Min Wu,

    1. Tampere School of Public Health, University of Tampere, Tampere, Finland
    2. Division of Biostatistics, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
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  • Anssi Auvinen,

    1. Tampere School of Public Health, University of Tampere, Tampere, Finland
    2. Finnish Cancer Institute, Helsinki, Finland
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  • Amy Ming-Fang Yen,

    1. Division of Biostatistics, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
    2. School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
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  • Matti Hakama,

    1. Tampere School of Public Health, University of Tampere, Tampere, Finland
    2. Finnish Cancer Institute, Helsinki, Finland
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  • Stephen D. Walter,

    1. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada
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  • Hsiu-Hsi Chen

    Corresponding author
    1. Division of Biostatistics, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
    • Phone: +886-2-33668033, Fax: +886-2-23587707

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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.

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