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Purpose . Women with localized breast cancer face difficult decisions about adjuvant therapy. Several decision aids are available to help women choose between treatment options. Decision aids are known to affect treatment choices and may therefore affect patient survival. The authors aimed to model the effects of the Adjuvant! decision aid on expected survival in women with early stage breast cancer. Patients and Methods . Data were obtained from a randomized trial of Adjuvant! (n = 395). To calculate the effects of the decision aid on survival, the authors used the Adjuvant! survival predictions as a surrogate endpoint. Data from each arm were entered separately into statistical models to estimate change in survival associated with receiving the Adjuvant! decision aid. Results . Most women (∼ 85%) chose a treatment option that maximized predicted survival. The effects of the decision aid on outcome could not be modeled because a small number of women (n = 12, 3%) chose treatment options associated with a large (5%—14%) loss in survival. These women—most typically estrogen receptor positive but refusing hormonal therapy—were equally divided between Adjuvant! and control groups and were not distinguished by medical or demographic factors. Conclusions . Expected benefit from treatment is a key variable in understanding patient behavior. A small number of women refuse adjuvant treatment associated with large increases in predicted survival, even when they are explicitly informed about the degree of benefit they would forgo. Investigation of the effects of decision aids on cancer survival is unlikely to be fruitful due to power considerations.

Background. Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. Method. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the “decision curve.” The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Conclusion. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.

Purpose. To evaluate the effect of preference assessment method on treatment recommended by an individualized decision-analytic model for early prostate cancer. Methods. Health state preferences were elicited by time tradeoff, rating scale, and a power transformation of the rating scale from 63 men ages 55to75. The authors used these values in aMarkov model to determine whether radical prostatectomy or watchful waiting yielded the greater quality-adjusted life expectancy. Results. Time tradeoff and transformed rating scale recommendations differed widely. Time tradeoff and transformedratingscaleutilitiesdifferedintheirtreatmentrecommendation for 21% to 52% of men, and the mean difference in quality-adjusted life years varied from less than 0.5 to greater than 1.0. Conclusions. Treatment recommendations from the prostate cancer decision model were sensitive to the method of preference assessment. If decision analysis is used to counsel individual patients, careful consideration must be given to the method of preference elicitation. Key words: preference assessment method; decision-analytic model; prostate cancer; time tradeoff; rating scale; quality-adjusted life expectancy. (Med Decis Making 2004;24:504‐510)