Tuesday, October 25, 2011: 10:30 AM
Grand Ballroom EF (Hyatt Regency Chicago)
(DEC) Decision Psychology and Shared Decision Making

Murray D. Krahn, MD, MSc, University of Toronto, Toronto, ON, Canada, Karen E. Bremner, BSc, University Health Network, Toronto, ON, Canada, Nicholas Mitsakakis, MSc, PHD, Toronto Health Economics and Technology Assessment Collaborative, Toronto, ON, Canada and Leslie S. Wilson, PhD, University of California San Francisco, San Francisco, CA

Purpose: The Patient Oriented Prostate Utility Scale (PORPUS-U) is a 10-item disease-specific multiattribute utility instrument with utility weights from prostate cancer patients. The Prostate Cancer Index (PCI) is a descriptive quality of life instrument producing function and bother scores ranging from 0 (poor outcome) to 100 (good outcome) for urinary, sexual, and bowel problems. The study objective was to develop a function to predict utility scores from PCI scores.

Method: We used patient-level data from previous studies in which the PCI and PORPUS were administered concurrently. Study 1 included 248 prostate cancer patients from an outpatient clinic interviewed on 3 occasions within 18 months. Study 2 included 676 community-dwelling prostate cancer patients who completed the questionnaires by mail. The derivation sample (Study 2) was used to fit three linear regression models, chosen based on previous work. Study 1 data were used to validate the models. PCI scores were divided by 100 to range from 0 to 1. One model used the original PORPUS-U scores, and two used log-transformed PORPUS-U scores, one with a hierarchy constraint and one without. Also, all models were run with and without patient age. Model selection was performed with PORPUS-U score as the dependent variable and PCI score as the covariate, using stepwise selection and 5-fold cross validation. The predictive ability of the models was assessed.

Result: The best-fitting model used the log-transformed PORPUS-U with no hierarchy constraint. Inclusion of age did not improve the model. Scores were untransformed for validation, and Dunn’s smearing estimator applied to correct potential bias in the estimate. The r-squared was 0.72. The RMSE ranged from 0.041 to 0.061 for the 3 validation datasets.    We compared the observed PORPUS-U scores to scores predicted from PCI responses. The mean predicted and observed scores were similar (eg., 0.966 vs 0.956). The mean predicted scores were also similar across quartiles of  observed scores but slightly overestimated the lowest 5% of observed PORPUS-U scores.

Conclusion: We developed an algorithm to predict PORPUS-U utility scores from PCI scores. This facilitates the estimation of patient-derived utilities for clinical and health economic studies from the many published studies using the PCI. This is also, to our knowledge, the only attempt to map a disease-specific quality of life instrument to a disease-specific utility measure.