Daniel O Erim1, Antonia V Bennett2,3, Bradley N Gaynes4, Ram Sankar Basak5, Deborah Usinger2, Ronald C Chen6. 1. Lineberger Comprehensive Cancer Center, School of Medicine, The University of North Carolina, Chapel Hill, NC, USA. erim.daniel@alumni.harvard.edu. 2. Lineberger Comprehensive Cancer Center, School of Medicine, The University of North Carolina, Chapel Hill, NC, USA. 3. Department of Health Policy and Management, The University of North Carolina At Chapel Hill, Chapel Hill, NC, USA. 4. Department of Psychiatry, The University of North Carolina, Chapel Hill, NC, USA. 5. Department of Radiation Oncology, The University of North Carolina, Chapel Hill, NC, USA. 6. Department of Radiation Oncology, The University of Kansas Cancer Center, Kansas City, KS, USA.
Abstract
PURPOSE: To create a crosswalk that predicts Short Form 6D (SF-6D) utilities from Memorial Anxiety Scale for Prostate Cancer (MAX-PC) scores. METHODS: The data come from prostate cancer patients enrolled in the North Carolina Prostate Cancer Comparative Effectiveness & Survivorship Study (NC ProCESS, N = 1016). Cross-sectional data from 12- to 24-month follow-up were used as estimation and validation datasets, respectively. Participants' SF-12 scores were used to generate SF-6D utilities in both datasets. Beta regression mixture models were used to evaluate SF-6D utilities as a function of MAX-PC scores, race, education, marital status, income, employment status, having health insurance, year of cancer diagnosis and clinically significant prostate cancer-related anxiety (PCRA) status in the estimation dataset. Models' predictive accuracies (using mean absolute error [MAE], root mean squared error [RMSE], Akaike information criterion [AIC] and Bayesian information criterion [BIC]) were examined in both datasets. The model with the highest prediction accuracy and the lowest prediction errors was selected as the crosswalk. RESULTS: The crosswalk had modest prediction accuracy (MAE = 0.092, RMSE = 0.114, AIC = - 2708 and BIC = - 2595.6), which are comparable to prediction accuracies of other SF-6D crosswalks in the literature. About 24% and 52% of predictions fell within ± 5% and ± 10% of observed SF-6D, respectively. The observed mean disutility associated with acquiring clinically significant PCRA is 0.168 (standard deviation = 0.179). CONCLUSION: This study provides a crosswalk that converts MAX-PC scores to SF-6D utilities for economic evaluation of clinically significant PCRA treatment options for prostate cancer survivors.
PURPOSE: To create a crosswalk that predicts Short Form 6D (SF-6D) utilities from Memorial Anxiety Scale for Prostate Cancer (MAX-PC) scores. METHODS: The data come from prostate cancerpatients enrolled in the North Carolina Prostate Cancer Comparative Effectiveness & Survivorship Study (NC ProCESS, N = 1016). Cross-sectional data from 12- to 24-month follow-up were used as estimation and validation datasets, respectively. Participants' SF-12 scores were used to generate SF-6D utilities in both datasets. Beta regression mixture models were used to evaluate SF-6D utilities as a function of MAX-PC scores, race, education, marital status, income, employment status, having health insurance, year of cancer diagnosis and clinically significant prostate cancer-related anxiety (PCRA) status in the estimation dataset. Models' predictive accuracies (using mean absolute error [MAE], root mean squared error [RMSE], Akaike information criterion [AIC] and Bayesian information criterion [BIC]) were examined in both datasets. The model with the highest prediction accuracy and the lowest prediction errors was selected as the crosswalk. RESULTS: The crosswalk had modest prediction accuracy (MAE = 0.092, RMSE = 0.114, AIC = - 2708 and BIC = - 2595.6), which are comparable to prediction accuracies of other SF-6D crosswalks in the literature. About 24% and 52% of predictions fell within ± 5% and ± 10% of observed SF-6D, respectively. The observed mean disutility associated with acquiring clinically significant PCRA is 0.168 (standard deviation = 0.179). CONCLUSION: This study provides a crosswalk that converts MAX-PC scores to SF-6D utilities for economic evaluation of clinically significant PCRA treatment options for prostate cancer survivors.
Authors: Christian J Nelson; Tatiana D Starr; Richard J Macchia; Llewellyn Hyacinthe; Steven Friedman; Andrew J Roth Journal: Support Care Cancer Date: 2016-02-04 Impact factor: 3.603