Azin Nahvijou1, Hossein Safari2, Mahmood Yousefi3, Marziyeh Rajabi4, Morteza Arab-Zozani5, Hosein Ameri6. 1. Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran. 2. Health Promotion Research Center, Iran University of Medical Sciences, Tehran, Iran. 3. School of Management and Medical Informatics, Health Economics Department, Iranian Center of Excellence in Health Management, Tabriz University of Medical Sciences, Tabriz, Iran. 4. Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. 5. Social Determinants of Health Research Centre, Birjand University of Medical Sciences, Birjand, Iran. 6. Health Policy and Management Research Center, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. hamery7@yahoo.com.
Abstract
INTRODUCTION: The health-related quality of life (HRQoL) data extracted from cancer-specific questionnaires are often non-preference based, while patient preference-based utility data are required for health economic evaluation. This study aimed to map Functional Assessment of Cancer Therapy-Breast (FACT-B) subscales onto the Short Form six Dimension as an independent instrument (SF-6Dv2ind-6) using the data gathered from patients with breast cancer. METHODS: Data for 420 inpatient and outpatient patients with breast cancer were gathered from the largest academic center for cancer patients in Iran. The OLS and Tobit models were used to predict the values of the SF-6Dv2ind-6 with regard to the FACT-B subscales. Prediction accuracy of the models was determined by calculating the root mean square error (RMSE) and mean absolute error (MAE). The relationship between the fitted and observed SF-6Dv2ind-6 values was examined using the Intraclass Correlation Coefficients (ICC). Goodness of fit of models was assessed using the predicted R2 (Pred R2) and adjusted R2 (Adj R2). A tenfold cross-validation method was used for validation of models. RESULTS: Data of 416 patients with breast cancer were entered into final analysis. The model included main effects of FACT-B subscales, and statistically significant clinical and demographic variables were the best predictor for SF-6Dv2ind-6 (Model S3 of OLS with Adj R2 = 61.02%, Pred R2 = 59.25%, MAE = 0.0465, RMSE = 0.0621, ICC = 0.678, AIC = -831.324, BIC = -815.871). CONCLUSION: The best algorithm developed for SF-6Dv2ind-6 enables researchers to convert cancer-specific instruments scores into preference-based scores when the data are gathered using cancer-specific instruments.
INTRODUCTION: The health-related quality of life (HRQoL) data extracted from cancer-specific questionnaires are often non-preference based, while patient preference-based utility data are required for health economic evaluation. This study aimed to map Functional Assessment of Cancer Therapy-Breast (FACT-B) subscales onto the Short Form six Dimension as an independent instrument (SF-6Dv2ind-6) using the data gathered from patients with breast cancer. METHODS: Data for 420 inpatient and outpatientpatients with breast cancer were gathered from the largest academic center for cancerpatients in Iran. The OLS and Tobit models were used to predict the values of the SF-6Dv2ind-6 with regard to the FACT-B subscales. Prediction accuracy of the models was determined by calculating the root mean square error (RMSE) and mean absolute error (MAE). The relationship between the fitted and observed SF-6Dv2ind-6 values was examined using the Intraclass Correlation Coefficients (ICC). Goodness of fit of models was assessed using the predicted R2 (Pred R2) and adjusted R2 (Adj R2). A tenfold cross-validation method was used for validation of models. RESULTS: Data of 416 patients with breast cancer were entered into final analysis. The model included main effects of FACT-B subscales, and statistically significant clinical and demographic variables were the best predictor for SF-6Dv2ind-6 (Model S3 of OLS with Adj R2 = 61.02%, Pred R2 = 59.25%, MAE = 0.0465, RMSE = 0.0621, ICC = 0.678, AIC = -831.324, BIC = -815.871). CONCLUSION: The best algorithm developed for SF-6Dv2ind-6 enables researchers to convert cancer-specific instruments scores into preference-based scores when the data are gathered using cancer-specific instruments.
Entities:
Keywords:
Breast cancer; FACT-B; Quality of life; SF-6Dv2
Authors: Robert L Askew; Richard J Swartz; Yan Xing; Scott B Cantor; Merrick I Ross; Jeffrey E Gershenwald; J Lynn Palmer; Jeffrey E Lee; Janice N Cormier Journal: Value Health Date: 2011-06-24 Impact factor: 5.725
Authors: Freddie Bray; Jacques Ferlay; Isabelle Soerjomataram; Rebecca L Siegel; Lindsey A Torre; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2018-09-12 Impact factor: 508.702