Decision tool for prostate cancer patients helps men customize treatment in anxious time

Study in special issue on health care from INFORMS journal Interfaces

An online decision tool created in part by a graduate student at the University of California Irvine helps men diagnosed with prostate cancer sort through an intimidating flurry of possible treatments and customize treatment plans of their own, according to a study in the current issue of Interfaces, a journal of the Institute for Operations Research and the Management Sciences (INFORMS®).

"Decision Making with Prostate Cancer: A Multiple-Objective Model with Uncertainty" is by Jay Simon, who received his doctorate this spring and is scheduled to join the faculty of the Naval Postgraduate School. The study appears in a special issue of Interfaces http://interfaces.journal.informs.org/ containing applications of operations research models to healthcare problems.

His research, says Simon, "gives a patient the opportunity to see all the available information in one place and personalize it, tailor it, and incorporate his own preferences."

The tool allows men to enter their own data, compare it with available research, score different options, and derive a "life score" that guides their decision.

Making decisions about prostate cancer treatment is unique, the author writes. Patients diagnosed with other diseases often face restricted options. By contrast, because prostate cancer in many cases progresses slowly, men with the disease choose from a variety of possible cares. These can be evaluated using multiattribute utility theory, which is used by operations researches and decision scientists to evaluate trade-offs.

"Two main factors are critical in making the treatment decision," the author writes.

"First, it is necessary to quantify as probabilities the uncertainties involving death and side effects over time. A decision analyst accomplishes this mainly by collecting and analyzing historical data reported in medical journals. Second, the decision analyst must elicit the patient's individual preferences and incorporate them into the analysis. Because the outcomes involve multiple attributes (e.g., length of life and side effects), it is necessary to know the relative importance that the patient places on each attribute."

The model considers five treatment options:

1. surgery
2. external radiation
3. seed radiation
4. dual radiation
5. no treatment

The model's tasks are specifying and evaluating the uncertainties, and evoking patient preferences. Among the uncertainties considered are patients' expected life span in the absence of cancer and probability of death following each of the treatment options.