AI Article Synopsis
- The study aimed to compare the effectiveness of mapping algorithms derived from cross-sectional and longitudinal data to predict health outcomes in low-risk prostate cancer patients receiving different radiation therapies.
- Using data from a randomized controlled trial, researchers employed various regression models to estimate mapping algorithms based on health-related quality-of-life measures collected over time.
- Results indicated that models utilizing combined longitudinal data provided better predictive accuracy, particularly when accounting for patient characteristics, with improved performance noted over time for specific health measure domains.
Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9276114 | PMC |
| http://dx.doi.org/10.1200/CCI.21.00188 | DOI Listing |
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