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Empirical Estimates of the Lead Time Distribution for Prostate Cancer Based on Two Independent Representative Cohorts of Men Not Subject to Prostate-Specific Antigen Screening.

Savage, Caroline J; Lilja, Hans LU ; Cronin, Angel M; Ulmert, David LU and Vickers, Andrew J (2010) In Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology May 4. p.1201-1207
Abstract
BACKGROUND: Lead time, the estimated time by which screening advances the date of diagnosis, is used to calculate the risk of overdiagnosis. We sought to describe empirically the distribution of lead times between an elevated prostate-specific antigen (PSA) and subsequent prostate cancer diagnosis. METHODS: We linked the Swedish cancer registry to two independent cohorts: 60-year-olds sampled in 1981-1982 and 51- to 56-year-olds sampled in 1982-1985. We used univariate kernel density estimation to characterize the lead time distribution. Linear regression was used to model the lead time as a function of baseline PSA and logistic regression was used to test for an association between lead time and either stage or grade at diagnosis.... (More)
BACKGROUND: Lead time, the estimated time by which screening advances the date of diagnosis, is used to calculate the risk of overdiagnosis. We sought to describe empirically the distribution of lead times between an elevated prostate-specific antigen (PSA) and subsequent prostate cancer diagnosis. METHODS: We linked the Swedish cancer registry to two independent cohorts: 60-year-olds sampled in 1981-1982 and 51- to 56-year-olds sampled in 1982-1985. We used univariate kernel density estimation to characterize the lead time distribution. Linear regression was used to model the lead time as a function of baseline PSA and logistic regression was used to test for an association between lead time and either stage or grade at diagnosis. RESULTS: Of 1,167 older men, 132 were diagnosed with prostate cancer, of which 57 had PSA >/=3 ng/mL at baseline; 495 of 4,260 younger men were diagnosed with prostate cancer, of which 116 had PSA >/=3 ng/mL at baseline. The median lead time was slightly longer in the younger men (12.8 versus 11.8 years). In both cohorts, wide variation in lead times followed an approximately normal distribution. Longer lead times were significantly associated with a lower risk of high-grade disease in older and younger men [odds ratio, 0.82 (P = 0.023) and 0.77 (P < 0.001)]. CONCLUSION: Our findings suggest that early changes in the natural history of the disease are associated with high-grade cancer at diagnosis. Impact: The distinct differences between the observed distribution of lead times and those used in modeling studies illustrate the need to model overdiagnosis rates using empirical data. Cancer Epidemiol Biomarkers Prev; 19(5); OF1-7. (c)2010 AACR. (Less)
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author
organization
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type
Contribution to journal
publication status
published
subject
in
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
volume
May 4
pages
1201 - 1207
publisher
American Association for Cancer Research
external identifiers
  • WOS:000278489800008
  • PMID:20406957
  • Scopus:77952014775
ISSN
1538-7755
DOI
10.1158/1055-9965.EPI-09-1251
language
English
LU publication?
yes
id
c776390a-3d16-4e24-8d55-8512b614f4eb (old id 1595009)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/20406957?dopt=Abstract
date added to LUP
2010-05-04 21:28:30
date last changed
2016-11-01 09:54:58
@misc{c776390a-3d16-4e24-8d55-8512b614f4eb,
  abstract     = {BACKGROUND: Lead time, the estimated time by which screening advances the date of diagnosis, is used to calculate the risk of overdiagnosis. We sought to describe empirically the distribution of lead times between an elevated prostate-specific antigen (PSA) and subsequent prostate cancer diagnosis. METHODS: We linked the Swedish cancer registry to two independent cohorts: 60-year-olds sampled in 1981-1982 and 51- to 56-year-olds sampled in 1982-1985. We used univariate kernel density estimation to characterize the lead time distribution. Linear regression was used to model the lead time as a function of baseline PSA and logistic regression was used to test for an association between lead time and either stage or grade at diagnosis. RESULTS: Of 1,167 older men, 132 were diagnosed with prostate cancer, of which 57 had PSA &gt;/=3 ng/mL at baseline; 495 of 4,260 younger men were diagnosed with prostate cancer, of which 116 had PSA &gt;/=3 ng/mL at baseline. The median lead time was slightly longer in the younger men (12.8 versus 11.8 years). In both cohorts, wide variation in lead times followed an approximately normal distribution. Longer lead times were significantly associated with a lower risk of high-grade disease in older and younger men [odds ratio, 0.82 (P = 0.023) and 0.77 (P &lt; 0.001)]. CONCLUSION: Our findings suggest that early changes in the natural history of the disease are associated with high-grade cancer at diagnosis. Impact: The distinct differences between the observed distribution of lead times and those used in modeling studies illustrate the need to model overdiagnosis rates using empirical data. Cancer Epidemiol Biomarkers Prev; 19(5); OF1-7. (c)2010 AACR.},
  author       = {Savage, Caroline J and Lilja, Hans and Cronin, Angel M and Ulmert, David and Vickers, Andrew J},
  issn         = {1538-7755},
  language     = {eng},
  pages        = {1201--1207},
  publisher    = {ARRAY(0x9342038)},
  series       = {Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology},
  title        = {Empirical Estimates of the Lead Time Distribution for Prostate Cancer Based on Two Independent Representative Cohorts of Men Not Subject to Prostate-Specific Antigen Screening.},
  url          = {http://dx.doi.org/10.1158/1055-9965.EPI-09-1251},
  volume       = {May 4},
  year         = {2010},
}