TY - JOUR
T1 - Methodological Comparison of Mapping the Expanded Prostate Cancer Index Composite to EuroQoL-5D-3L Using Cross-Sectional and Longitudinal Data
T2 - Secondary Analysis of NRG/RTOG 0415
AU - Khairnar, Rahul
AU - DeMora, Lyudmila
AU - Sandler, Howard M.
AU - Lee, W. Robert
AU - Villalonga-Olives, Ester
AU - Mullins, C. Daniel
AU - Palumbo, Francis B.
AU - Bruner, Deborah W.
AU - Shaya, Fadia T.
AU - Bentzen, Soren M.
AU - Shah, Amit B.
AU - Malone, Shawn
AU - Michalski, Jeff M.
AU - Dayes, Ian S.
AU - Seaward, Samantha A.
AU - Albert, Michele
AU - Currey, Adam D.
AU - Pisansky, Thomas M.
AU - Chen, Yuhchyau
AU - Horwitz, Eric M.
AU - DeNittis, Albert S.
AU - Feng, Felix
AU - Mishra, Mark V.
PY - 2022/6/1
Y1 - 2022/6/1
N2 - PURPOSE: To compare the predictive ability of mapping algorithms derived using cross-sectional and longitudinal data. METHODS: This methodological assessment used data from a randomized controlled noninferiority trial of patients with low-risk prostate cancer, conducted by NRG Oncology (ClinicalTrials.gov identifier: NCT00331773), which examined the efficacy of conventional schedule versus hypofractionated radiation therapy (three-dimensional conformal external beam radiation therapy/IMRT). Health-related quality-of-life data were collected using the Expanded Prostate Cancer Index Composite (EPIC), and health utilities were obtained using EuroQOL-5D-3L (EQ-5D) at baseline and 6, 12, 24, and 60 months postintervention. Mapping algorithms were estimated using ordinary least squares regression models through five-fold cross-validation in baseline cross-sectional data and combined longitudinal data from all assessment periods; random effects specifications were also estimated in longitudinal data. Predictive performance was compared using root mean square error. Longitudinal predictive ability of models obtained using baseline data was examined using mean absolute differences in the reported and predicted utilities. RESULTS: A total of 267 (and 199) patients in the estimation sample had complete EQ-5D and EPIC domain (and subdomain) data at baseline and at all subsequent assessments. Ordinary least squares models using combined data showed better predictive ability (lowest root mean square error) in the validation phase for algorithms with EPIC domain/subdomain data alone, whereas models using baseline data outperformed other specifications in the validation phase when patient covariates were also modeled. The mean absolute differences were lower for models using EPIC subdomain data compared with EPIC domain data and generally decreased as the time of assessment increased. CONCLUSION: Overall, mapping algorithms obtained using baseline cross-sectional data showed the best predictive performance. Furthermore, these models demonstrated satisfactory longitudinal predictive ability.
AB - PURPOSE: To compare the predictive ability of mapping algorithms derived using cross-sectional and longitudinal data. METHODS: This methodological assessment used data from a randomized controlled noninferiority trial of patients with low-risk prostate cancer, conducted by NRG Oncology (ClinicalTrials.gov identifier: NCT00331773), which examined the efficacy of conventional schedule versus hypofractionated radiation therapy (three-dimensional conformal external beam radiation therapy/IMRT). Health-related quality-of-life data were collected using the Expanded Prostate Cancer Index Composite (EPIC), and health utilities were obtained using EuroQOL-5D-3L (EQ-5D) at baseline and 6, 12, 24, and 60 months postintervention. Mapping algorithms were estimated using ordinary least squares regression models through five-fold cross-validation in baseline cross-sectional data and combined longitudinal data from all assessment periods; random effects specifications were also estimated in longitudinal data. Predictive performance was compared using root mean square error. Longitudinal predictive ability of models obtained using baseline data was examined using mean absolute differences in the reported and predicted utilities. RESULTS: A total of 267 (and 199) patients in the estimation sample had complete EQ-5D and EPIC domain (and subdomain) data at baseline and at all subsequent assessments. Ordinary least squares models using combined data showed better predictive ability (lowest root mean square error) in the validation phase for algorithms with EPIC domain/subdomain data alone, whereas models using baseline data outperformed other specifications in the validation phase when patient covariates were also modeled. The mean absolute differences were lower for models using EPIC subdomain data compared with EPIC domain data and generally decreased as the time of assessment increased. CONCLUSION: Overall, mapping algorithms obtained using baseline cross-sectional data showed the best predictive performance. Furthermore, these models demonstrated satisfactory longitudinal predictive ability.
UR - http://www.scopus.com/inward/record.url?scp=85134083953&partnerID=8YFLogxK
U2 - 10.1200/CCI.21.00188
DO - 10.1200/CCI.21.00188
M3 - Article
C2 - 35776901
AN - SCOPUS:85134083953
SN - 2473-4276
VL - 6
SP - e2100188
JO - JCO Clinical Cancer Informatics
JF - JCO Clinical Cancer Informatics
ER -