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

Abstract

Objective: To estimate the benefit of PSA-based screening for prostate cancer from the patient and societal perspectives.

Method: A partially observable Markov decision process model was used to optimize PSA screening decisions. Age-specific prostate cancer incidence rates and the mortality rates from prostate cancer and competing causes were considered. The model trades off the potential benefit of early detection with the cost of screening and loss of patient quality of life due to screening and treatment. PSA testing and biopsy decisions are made based on the patient's probability of having prostate cancer. Probabilities are inferred based on the patient's complete PSA history using Bayesian updating.

Data sources: The results of all PSA tests and biopsies done in Olmsted County, Minnesota, from 1993 to 2005 (11,872 men and 50,589 PSA test results).

Outcome measures: Patients' perspective: to maximize expected quality-adjusted life years (QALYs); societal perspective: to maximize the expected monetary value based on societal willingness to pay for QALYs and the cost of PSA testing, prostate biopsies, and treatment.

Results: From the patient perspective, the optimal policy recommends stopping PSA testing and biopsy at age 76. From the societal perspective, the stopping age is 71. The expected incremental benefit of optimal screening over the traditional guideline of annual PSA screening with threshold 4.0 ng/mL for biopsy is estimated to be 0.165 QALYs per person from the patient perspective and 0.161 QALYs per person from the societal perspective. PSA screening based on traditional guidelines is found to be worse than no screening at all.

Conclusions: PSA testing done with traditional guidelines underperforms and therefore underestimates the potential benefit of screening. Optimal screening guidelines differ significantly depending on the perspective of the decision maker.

Figure 1

Recurring screening decision process for prostate cancer screening, in which decision epoches (t, t+1, ⋯) occur annually. Squares represent decisions; circles represent test observations; the triangle denotes the expected outcome upon detection of prostate cancer.

Figure 2

Transitions among health states of the prostate cancer Markov model. Note that death from other causes is possible for any health state in our model, but omitted for simplicity.

Figure 3

The detailed classification inside state T, where CD means the time at which cancer is detected; parameters in this figure are from the Mayo Clinic Radical Prostatectomy Registry and SEER (23).

Figure 4

Optimal prostate cancer screening policies from the patient and societal perspectives. Lines denote thresholds for PSA testing and biopsy. If the patient’s age and probability of having prostate cancer are in area B they are referred for biopsy; DB means defer biopsy referral until obtaining the PSA test result in the next decision epoch; DP means defer biopsy referral and the PSA test in the next decision epoch.