TITLE: Optimal Design of Prostate Cancer Screening
Policies

SPEAKER: Brian Denton

ABSTRACT:

Prostate
cancer is the most common solid tumor that affects American men. Screening
typically involves the use of prostate specific antigen (PSA) tests. However,
the imperfect nature of PSA tests, and the potential for subsequent harm from
unnecessary biopsies and treatment, has raised debate about whether and when to
screen. In this talk I will provide some background on prostate cancer, current
screening guidelines, and a summary of the recent controversy over PSA testing.
Next, I will discuss a partially observable Markov decision process (POMDP)
model to investigate the optimal design of screening policies. Screening policies are defined by the patient’s probability of having
prostate cancer which is estimated from their history of PSA tests results
using Bayesian updating. The core states are the patients’ prostate cancer
related health states. Transition probabilities among health states are
estimated using a large longitudinal dataset from Olmsted County, the Mayo
Clinic Radical Prostatectomy Registry (MCRPR) and the medical literature.  Reward functions that are considered include
quality adjusted survival (patient perspective) and costs (third party payer
perspective).

Some
theoretical properties that define the optimal policy will be discussed, and a
new approximation method suited to solving finite horizon non-stationary POMDPs
will be presented.  The
results of computational experiments will be used to
illustrate the use of the model for making screening decisions, such as if and
when to recommend a patient for a PSA test, and when to refer patients for
biopsy and subsequent treatment.  Sensitivity
analysis will be presented to demonstrate the relative importance of factors
that define patient specific preferences and risk factors. Finally, future
research directions in the area will be discussed.