To compare the predictive ability of mapping algorithms derived using cross-sectional and longitudinal data.
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.
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.
Overall, mapping algorithms obtained using baseline cross-sectional data showed the best predictive performance. Furthermore, these models demonstrated satisfactory longitudinal predictive ability.
JCO clinical cancer informatics. 2022 Jun [Epub]
Rahul Khairnar, Lyudmila DeMora, Howard M Sandler, W Robert Lee, Ester Villalonga-Olives, C Daniel Mullins, Francis B Palumbo, Deborah W Bruner, Fadia T Shaya, Soren M Bentzen, Amit B Shah, Shawn Malone, Jeff M Michalski, Ian S Dayes, Samantha A Seaward, Michele Albert, Adam D Currey, Thomas M Pisansky, Yuhchyau Chen, Eric M Horwitz, Albert S DeNittis, Felix Feng, Mark V Mishra
Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy, Baltimore, MD., NRG Oncology Statistics and Data Management Center, Philadelphia, PA., Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA., Department of Radiation Oncology, Duke University, Durham, NC., Department of Radiation Oncology, Emory University, Atlanta, GA., Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD., WellSpan Health-York Cancer Center, York, PA., Ottawa Hospital and Cancer Center, Ottawa, Ontario, Canada., Department of Radiation Oncology, Washington University, St Louis, MO., Juravinski Cancer Center at Hamilton Health Sciences, Hamilton, Ontario, Canada., Kaiser Permanente Northern California, Oakland, CA., Saint Anne's Hospital, Fall River, MA., Zablocki VAMC and the Medical College of Wisconsin, Milwaukee, WI., Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, MN., Department of Radiation Oncology, University of Rochester, Rochester, NY., Department of Radiation Oncology, Fox Chase Cancer Center, Philadelphia, PA., Department of Radiation Oncology, Main Line Health, Wynnewood, PA., Department of Radiation Oncology, University of California San Francisco, San Francisco, CA., Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD.