Simulation optimization of PSA-threshold based prostate cancer screening policies

Daniel J. Underwood; Jingyu Zhang; Brian T. Denton; Nilay D. Shah; Brant A. Inman

doi:10.1007/s10729-012-9195-x

Simulation optimization of PSA-threshold based prostate cancer screening policies

Daniel J. Underwood, Jingyu Zhang, Brian T. Denton, Nilay D. Shah, Brant A. Inman

Quantitative Health Sciences

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

We describe a simulation optimization method to design PSA screening policies based on expected quality adjusted life years (QALYs). Our method integrates a simulation model in a genetic algorithm which uses a probabilistic method for selection of the best policy. We present computational results about the efficiency of our algorithm. The best policy generated by our algorithm is compared to previously recommended screening policies. Using the policies determined by our model, we present evidence that patients should be screened more aggressively but for a shorter length of time than previously published guidelines recommend.

Original language	English (US)
Pages (from-to)	293-309
Number of pages	17
Journal	Health Care Management Science
Volume	15
Issue number	4
DOIs	https://doi.org/10.1007/s10729-012-9195-x
State	Published - Dec 2012

Keywords

Genetic algorithm
Prostate cancer screening
Ranking and selection
Simulation optimization

ASJC Scopus subject areas

Medicine (miscellaneous)
General Health Professions

Access to Document

10.1007/s10729-012-9195-x

Cite this

@article{d943aea849b24a5785346d6c54c09517,

title = "Simulation optimization of PSA-threshold based prostate cancer screening policies",

abstract = "We describe a simulation optimization method to design PSA screening policies based on expected quality adjusted life years (QALYs). Our method integrates a simulation model in a genetic algorithm which uses a probabilistic method for selection of the best policy. We present computational results about the efficiency of our algorithm. The best policy generated by our algorithm is compared to previously recommended screening policies. Using the policies determined by our model, we present evidence that patients should be screened more aggressively but for a shorter length of time than previously published guidelines recommend.",

keywords = "Genetic algorithm, Prostate cancer screening, Ranking and selection, Simulation optimization",

author = "Underwood, {Daniel J.} and Jingyu Zhang and Denton, {Brian T.} and Shah, {Nilay D.} and Inman, {Brant A.}",

note = "Funding Information: Acknowledgements This material is based in part upon work supported by the National Science Foundation under Grant Number CMMI 0844511. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This study was also made possible by the Rochester Epidemiology Project (Grant #R01 - AG034676-47 from the National Institute of Aging). We wish to thank two anonymous reviewers for their constructive comments, which were instrumental in improving this manuscript.",

year = "2012",

month = dec,

doi = "10.1007/s10729-012-9195-x",

language = "English (US)",

volume = "15",

pages = "293--309",

journal = "Health Care Management Science",

issn = "1386-9620",

publisher = "Kluwer Academic Publishers",

number = "4",

}

TY - JOUR

T1 - Simulation optimization of PSA-threshold based prostate cancer screening policies

AU - Underwood, Daniel J.

AU - Zhang, Jingyu

AU - Denton, Brian T.

AU - Shah, Nilay D.

AU - Inman, Brant A.

N1 - Funding Information: Acknowledgements This material is based in part upon work supported by the National Science Foundation under Grant Number CMMI 0844511. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This study was also made possible by the Rochester Epidemiology Project (Grant #R01 - AG034676-47 from the National Institute of Aging). We wish to thank two anonymous reviewers for their constructive comments, which were instrumental in improving this manuscript.

PY - 2012/12

Y1 - 2012/12

N2 - We describe a simulation optimization method to design PSA screening policies based on expected quality adjusted life years (QALYs). Our method integrates a simulation model in a genetic algorithm which uses a probabilistic method for selection of the best policy. We present computational results about the efficiency of our algorithm. The best policy generated by our algorithm is compared to previously recommended screening policies. Using the policies determined by our model, we present evidence that patients should be screened more aggressively but for a shorter length of time than previously published guidelines recommend.

AB - We describe a simulation optimization method to design PSA screening policies based on expected quality adjusted life years (QALYs). Our method integrates a simulation model in a genetic algorithm which uses a probabilistic method for selection of the best policy. We present computational results about the efficiency of our algorithm. The best policy generated by our algorithm is compared to previously recommended screening policies. Using the policies determined by our model, we present evidence that patients should be screened more aggressively but for a shorter length of time than previously published guidelines recommend.

KW - Genetic algorithm

KW - Prostate cancer screening

KW - Ranking and selection

KW - Simulation optimization

UR - http://www.scopus.com/inward/record.url?scp=84867555222&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84867555222&partnerID=8YFLogxK

U2 - 10.1007/s10729-012-9195-x

DO - 10.1007/s10729-012-9195-x

M3 - Article

C2 - 22302420

AN - SCOPUS:84867555222

SN - 1386-9620

VL - 15

SP - 293

EP - 309

JO - Health Care Management Science

JF - Health Care Management Science

IS - 4

ER -

Simulation optimization of PSA-threshold based prostate cancer screening policies

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this