Simulation optimization of PSA-threshold based prostate cancer screening policies.
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 recommended.
Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC 27695, USA. daniel.underwood@ncsu.edu
Daniel J
DJ
Underwood
Jingyu
J
Zhang
Brian T
BT
Denton
Nilay D
ND
Shah
Brant A
BA
Inman
D000367:Age Factors; D000465:Algorithms; D003198:Computer Simulation; D055088:Early Detection of Cancer / Q000379:methods* / Q000706:statistics & numerical data*; D005189:False Positive Reactions; D006291:Health Policy; D006801:Humans; D008297:Male; D017410:Practice Guidelines as Topic; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D019057:Quality-Adjusted Life Years
D017430:Prostate-Specific Antigen