Validation of a model predicting enrollment status in a chemoprevention trial for breast cancer.

We evaluated the performance of a regression model in predicting enrollment status in a chemoprevention trial for breast cancer using a population independent of that from which the model was derived. In years 1 and 2 of recruitment, questionnaires were completed by eligible participants following attendance at informational meetings about the Breast Cancer Prevention Trial. The variables in the original model, based on women recruited in year 1, included not being able to take estrogen replacement therapy (ERT), concern about the side effects of tamoxifen, the possibility of getting a placebo, the out-of-pocket expenses associated with the trial, and disagreement with the statement "significant others would be reassured if the respondent was taking tamoxifen." These variables were used to predict enrollment status of women newly recruited to the trial in year 2. Among the 89 women in the study population who responded to the questionnaire, 66% did not enroll in the trial. By applying the original logistic regression model, enrollment status in the trial was correctly predicted for 72% of year 2 questionnaire respondents. Age and risk scores, as binary variables, were used in a derived logistic model to determine whether they provided additional predictive information on enrollment status. The resulting four-factor model, which predicted nonenrollment, included: age of > or = 50 years, not being able to take ERT, expressed concern that significant others would not be reassured if the respondent was taking tamoxifen, and concern about out-of-pocket expenses associated with the trial. This model correctly classified 76% of the respondents. The logistic regression models performed reasonably well in predicting enrollment status. Not being able to take ERT remained the strongest factor predicting nonenrollment. More research is needed to evaluate factors that motivate persons to seek participation in primary chemoprevention trials in culturally diverse populations.