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Publication Date:
December 2006
ISSN:
1557-4679
DOI:
10.2202/1557-4679.1036

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Ed. by Hubbard, Alan E. / van der Laan, Mark J.

1 Issue per year

IMPACT FACTOR 2011: 1.284

Modeling the Effect of a Preventive Intervention on the Natural History of Cancer: Application to the Prostate Cancer Prevention Trial

Paul Pinsky / Ruth Etzioni / Nadia Howlader / Phyllis Goodman / Ian M Thompson

1National Cancer Institute

1Fred Hutchinson Cancer Research Center

1National Cancer Institute

1Fred Hutchinson Cancer Research Center

1University of Texas Health Sciences Center at San Antonio

Citation Information: The International Journal of Biostatistics. Volume 2, Issue 1, Pages –, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1036, December 2006

Publication History:
Published Online:
2006-12-28

The Prostate Cancer Prevention Trial (PCPT) recently demonstrated a significant reduction in prostate cancer incidence of about 25% among men taking finasteride compared to men taking placebo. However, the effect of finasteride on the natural history of prostate cancer is not well understood. We adapted a convolution model developed by Pinsky (2001) to characterize the natural history of prostate cancer in the presence and absence of finasteride. The model was applied to data from 10,995 men in PCPT who had disease status determined by interim diagnosis of prostate cancer or end-of-study biopsy. Prostate cancer cases were either screen-detected by Prostate-Specific Antigen (PSA), biopsy-detected at the end of the study, or clinically detected, that is, detected by methods other than PSA screening. The hazard ratio (HR) for the incidence of preclinical disease on finasteride versus placebo was 0.42 (95% CI: 0.20-0.58). The progression from preclinical to clinical disease was relatively unaffected by finasteride, with mean sojourn time being 16 years for placebo cases and 18.5 years for finasteride cases (p-value for difference = 0.2). We conclude that finasteride appears to affect prostate cancer primarily by preventing the emergence of new, preclinical tumors with little impact on established, latent disease.

Keywords: convolution model; lead time; sojourn time; chemoprevention

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