Currey, Adam D.

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.

60 Background: Mapping algorithms informing economic evaluations are often derived using baseline data from clinical trials. It is unclear if these algorithms can predict health utilities accurately in post-intervention data. Thus, this study examines the longitudinal predictive ability of mapping algorithms derived from baseline trial data and explores the factors associated with prediction errors. Methods: This methodological study utilized data from an international, multicenter, randomized controlled trial of patients with low-risk prostate cancer (PC), conducted by NRG Oncology (NCT00331773). In addition to patient demographic and clinical data, this study utilized PRO data collected at baseline and 6, 12 and 24 months post-intervention. The Expanded Prostate Cancer Index Composite (EPIC) questionnaire measures health-related quality-of-life (HRQoL) and has four domains (urinary, sexual, hormonal, and bowel) and two subdomains per domain (function and bother); EuroQOL-5D-3L (EQ5D) captures health utilities. Ordinary Least Squares (OLS) regression models were used to map EPIC scores to EQ5D utilities in the baseline data through 5-fold cross-validation. Predictive performance was tested in the post-intervention data; predicted and reported utilities were compared using t-tests, and the absolute prediction error was modeled using fixed effects, as a function of baseline demographic and clinical covariates, as well as observed and predicted EQ5D utilities. Results: A total of 267 (199) patients had complete EQ5D and EPIC domain (or subdomain) data at baseline and all subsequent assessments. In the EPIC domain sample, mean ± standard deviation observed EQ5D utility was 0.90±0.13 at baseline, 0.92±0.11 at 6 months, 0.90±0.13 at 12 months and 0.89±0.14 at 24 months. Mean absolute differences (MDs) between reported and predicted were lower for models using EPIC subdomain data compared to EPIC domain data, and generally decreased as the time of assessment increased. The mapping functions over-predicted utilities for patients in perfect health while the prediction errors were increasingly negative for lower reported EQ5D scores. According to the fixed effects model for EPIC domain data, lower observed and predicted baseline EQ5D scores, and time of assessment were significant predictors of the absolute prediction error; for EPIC subdomain data, lower observed and predicted baseline EQ5D scores, hormonal bother and function, and bowel function significantly predicted the absolute prediction error. Conclusions: This study is the first to demonstrate the longitudinal validity of EPIC questionnaire, and builds upon existing research on longitudinal validity of mapping functions. The low MDs in prediction errors in post-intervention data indicate that the mapping functions are sensitive to treatment effect, thereby increasing confidence in their use in economic evaluations in PC.

55 Background: To compare the predictive ability of health utility mapping algorithms derived using cross-sectional and longitudinal data specific to the Expanded Prostate Cancer Index Composite (EPIC). Methods: This mapping study utilized data from an international, multicenter, randomized controlled trial of patients with low-risk prostate cancer conducted by NRG Oncology (NCT00331773). Health-related quality-of-life (HRQoL) data were collected using EPIC, and health utilities were obtained using EuroQOL-5D (EQ5D) at baseline and 6, 12 and 24 months post-intervention. Data were split into an estimation sample (70%) and a validation sample (30%). Ordinary Least Squares (OLS) regression models were estimated using baseline cross-sectional data as well as pooled data from all assessment periods. Random effects (RE) specifications that explicitly model the longitudinal nature of the data were also estimated. Candidate models were selected based on root mean square error (RMSE). Results: A total of 196 (147) patients in the estimation sample had complete EQ5D and EPIC domain (subdomain) data at all time points. OLS models using combined data outperformed the counter-part RE models as well as OLS models using baseline data in the five-fold cross-validation. Addition of covariates to the models resulted in improved predictive ability. In the external validation, when only EPIC domain/ subdomain data are available, the OLS model using combined data predicted EQ5D utilities better than the counterpart RE model and OLS model using baseline data (RMSE=0.121108 & 0.111345). OLS model using baseline data outperformed other model types for algorithms with EPIC domains and demographics (RMSE=0.121757), while RE models outperformed the other two model types for algorithms with EPIC subdomains and demographic data, (0.112782) and for algorithms with EPIC domains/ subdomains, demographics, and clinical covariates (RMSE=0.123589 & 0.163093). Conclusions: While algorithms using pooled data outperformed other model types in internal validation, RE models showed better predictive ability in external validation for algorithms with covariates. Clinical trial information: NCT00331773.

Background In prostate cancer, end points that reliably portend prognosis and treatment benefit (surrogate end points) can accelerate therapy development. Although surrogate end point candidates have been evaluated in the context of radiotherapy and short-term androgen deprivation (AD), potential surrogates under long-term (24 month) AD, a proven therapy in high-risk localized disease, have not been investigated. Materials and Methods In the NRG/RTOG 9202 randomized trial (N = 1,520) of short-term AD (4 months) versus long-term AD (LTAD; 28 months), the time interval free of biochemical failure (IBF) was evaluated in relation to clinical end points of prostate cancer–specific survival (PCSS) and overall survival (OS). Survival modeling and landmark analysis methods were applied to evaluate LTAD benefit on IBF and clinical end points, association between IBF and clinical end points, and the mediating effect of IBF on LTAD clinical end point benefits. Results LTAD was superior to short-term AD for both biochemical failure (BF) and the clinical end points. Men remaining free of BF for 3 years had relative risk reductions of 39% for OS and 73% for PCSS. Accounting for 3-year IBF status reduced the LTAD OS benefit from 12% (hazard ratio [HR], 0.88; 95% CI, 0.79 to 0.98) to 6% (HR, 0.94; 95% CI, 0.83 to 1.07). For PCSS, the LTAD benefit was reduced from 30% (HR, 0.70; 95% CI, 0.52 to 0.82) to 6% (HR, 0.94; 95% CI, 0.72 to 1.22). Among men with BF, by 3 years, 50% of subsequent deaths were attributed to prostate cancer, compared with 19% among men free of BF through 3 years. Conclusion The IBF satisfied surrogacy criteria and identified the benefit of LTAD on disease-specific survival and OS. The IBF may serve as a valid end point in clinical trials and may also aid in risk monitoring after initial treatment.