Literature DB >> 10458357

Serial prostate specific antigen screening for prostate cancer: a computer model evaluates competing strategies.

R Etzioni1, R Cha, M E Cowen.   

Abstract

PURPOSE: We compare prostate specific antigen (PSA) screening strategies in terms of expected years of life saved with screening, number of screens, number of false-positive screens and rates of over diagnosis, defined as detection by PSA screening of patients who would never have been diagnosed without screening.
MATERIALS AND METHODS: A computer model of disease progression, clinical diagnosis, PSA growth and PSA screening was used. Under baseline conditions, when screening is not considered, the model replicates clinical diagnosis and disease mortality rates recorded by the Surveillance, Epidemiology and End Results Program of the National Cancer Institute in the mid 1980s.
RESULTS: Biannual screening with PSA greater than 4.0 ng./ml. was projected to reduce the number of screens and false-positive tests by almost 50% relative to annual screening while retaining 93% of years of life saved. With annual screening use of an age specific bound for PSA to consider a test positive instead of the standard 4.0 ng./ml. was projected to reduce false-positive screens by 27% and over diagnosis by a third while retaining almost 95% of years of life saved. Sensitivity analyses did not change the relative efficacy of biannual screening.
CONCLUSIONS: Under the model assumptions biannual PSA screening is a cost-effective alternative to annual PSA screening for prostate cancer. With annual screening use of an age specific bound for PSA positivity appears to reduce false-positive results and over diagnosis rates sharply relative to a bound of 4 ng./ml. while retaining most of the survival benefits.

Entities:  

Mesh:

Substances:

Year:  1999        PMID: 10458357     DOI: 10.1097/00005392-199909010-00032

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  12 in total

1.  Optimization of PSA screening policies: a comparison of the patient and societal perspectives.

Authors:  Jingyu Zhang; Brian T Denton; Hari Balasubramanian; Nilay D Shah; Brant A Inman
Journal:  Med Decis Making       Date:  2011-09-20       Impact factor: 2.583

2.  Simulation optimization of PSA-threshold based prostate cancer screening policies.

Authors:  Daniel J Underwood; Jingyu Zhang; Brian T Denton; Nilay D Shah; Brant A Inman
Journal:  Health Care Manag Sci       Date:  2012-12

Review 3.  Calibration methods used in cancer simulation models and suggested reporting guidelines.

Authors:  Natasha K Stout; Amy B Knudsen; Chung Yin Kong; Pamela M McMahon; G Scott Gazelle
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

4.  Modeling Disease Progression with Longitudinal Markers.

Authors:  Lurdes Y T Inoue; Ruth Etzioni; Christopher Morrell; Peter Müller
Journal:  J Am Stat Assoc       Date:  2008       Impact factor: 5.033

Review 5.  Risk stratification in prostate cancer screening.

Authors:  Monique J Roobol; Sigrid V Carlsson
Journal:  Nat Rev Urol       Date:  2012-12-18       Impact factor: 14.432

6.  Quantifying the role of PSA screening in the US prostate cancer mortality decline.

Authors:  Ruth Etzioni; Alex Tsodikov; Angela Mariotto; Aniko Szabo; Seth Falcon; Jake Wegelin; Dante DiTommaso; Kent Karnofski; Roman Gulati; David F Penson; Eric Feuer
Journal:  Cancer Causes Control       Date:  2007-11-20       Impact factor: 2.506

7.  Variation in Prostate-Specific Antigen Testing Rates and Prostate Cancer Treatments and Outcomes in a National 20-Year Cohort.

Authors:  Oskar Bergengren; Marcus Westerberg; Lars Holmberg; Pär Stattin; Anna Bill-Axelson; Hans Garmo
Journal:  JAMA Netw Open       Date:  2021-05-03

8.  Modeling screening, prevention, and delaying of Alzheimer's disease: an early-stage decision analytic model.

Authors:  Nicolas M Furiak; Robert W Klein; Kristin Kahle-Wrobleski; Eric R Siemers; Eric Sarpong; Timothy M Klein
Journal:  BMC Med Inform Decis Mak       Date:  2010-04-30       Impact factor: 2.796

9.  Benefits and harms of prostate cancer screening - predictions of the ONCOTYROL prostate cancer outcome and policy model.

Authors:  Nikolai Mühlberger; Kristijan Boskovic; Murray D Krahn; Karen E Bremner; Willi Oberaigner; Helmut Klocker; Wolfgang Horninger; Gaby Sroczynski; Uwe Siebert
Journal:  BMC Public Health       Date:  2017-06-26       Impact factor: 3.295

Review 10.  Screening Coverage Needed to Reduce Mortality from Prostate Cancer: A Living Systematic Review.

Authors:  Ahmad K Rahal; Robert G Badgett; Richard M Hoffman
Journal:  PLoS One       Date:  2016-04-12       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.