Treatment selection for cancer patients: application of statistical decision theory to the treatment of advanced ovarian cancer.

Optimal treatment selection for patients with chronic disease, especially advanced cancer, requires careful consideration in weighing risks and benefits of each therapy. The application of statistical decision theory to such problems provides an explicit and systematic means of combining information on risks and benefits with individual patient preferences on quality-of-life issues. This paper evaluates the strengths and weaknesses of this methodology by using, as an example, treatment selection in advanced ovarian cancer. Possible treatment options and the major consequences of each are first outlined on a decision tree. The probability of various outcomes is estimated from the literature and methods for assessing the relative value or utility of each outcome are illustrated by interviews with 9 volunteers. Based on decision analysis, the recommended treatment for advanced ovarian cancer is found to be highly dependent on survival estimates but far less dependent on other probability estimates or the method of obtaining utilities. Individual preferences are also found to influence the treatment choice. The analysis illustrates that an important strength in using decision theory is its ability to identify key factors in the decision through sensitivity analysis. This may help both the physician selecting treatment and the investigator planning clinical trials which compare these therapies. In addition, this method can help in planning a trial's sample size by determining what survival difference between therapeutic strategies is worth detecting. Some problems identified with this methodology include the need for several simplifying assumptions and the difficulties in assessing individual preferences. On balance, we believe decision theory in this setting can play a useful role in complementing the physician's clinical judgement.