Mapping expanded prostate cancer index composite to EQ5D utilities to inform economic evaluations in prostate cancer: Secondary analysis of NRG/RTOG 0415

Mapping Studies to Estimate Health-State Utilities From Nonpreference-Based Outcome Measures: A Systematic Review on How Repeated Measurements are Taken Into Account

OBJECTIVES

Mapping algorithms are developed using data sets containing patient responses to a preference-based questionnaire and another health-related quality-of-life questionnaire. When data sets include repeated measurements from the same individuals over time, the assumption of observations' independence, required by standard models, is violated, and standard errors are underestimated. This review aimed to identify how studies deal with methodological challenges of repeated measurements, provide an overview of practice to date, and potential implications for future work.

METHODS

We conducted a systematic literature search of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, specialized databases, and previous systematic reviews. A data template was used to extract, among others, start and target instruments if the data set(s) used for estimation and validation had repeated measurements per patient, used regression techniques, and which (if any) adjustments were made for repeated measurements.

RESULTS

We identified 278 publications developing at least 1 mapping algorithm. Of the 278 publications, 121 used a data set with repeated measurements, among which 92 used multiple time points for estimation, and 39 selected specific time points to have 1 observation per participant. A total of 36 studies did not account for repeated measurements. An adjustment was conducted using cluster-robust standard errors (21), random-effects models (30), generalized estimating equations (7), and other methods (7).

CONCLUSIONS

The inconsistent use of methods to account for interdependent observations in the literature indicates that mapping guidelines should include recommendations on how to deal with repeated measurements, and journals should update their guidelines accordingly.

Cost-effectiveness of hypofractionated versus conventional radiotherapy in patients with intermediate-risk prostate cancer: An ancillary study of the PROstate fractionated irradiation trial - PROFIT

PURPOSE

To evaluate the cost-effectiveness of moderate Hypofractionated Radiotherapy (H-RT) compared to Conventional Radiotherapy (C-RT) for intermediate-risk prostate caner (PCa).

METHODS

A prospective randomized clinical trial including 222 patients from six French cancer centers was conducted as an ancillary study of the international PROstate Fractionated Irradiation Trial (PROFIT). We carried-out a cost-effectiveness analysis (CEA) from the payer's perspective, with a time horizon of 48 months. Patients assigned to the H-RT arm received 6000 cGy in 20 fractions over 4 weeks, or 7800 cGy in 39 fractions over 7 to 8 weeks in the C-RT arm. Patients completed quality of life (QoL) questionnaire: Expanded Prostate Cancer Index Composite (EPIC) at baseline, 24 and 48 months, which were mapped to obtain a EuroQol five-dimensional questionnaire (EQ-5D) equivalent to generate Quality Adjusted Life Years (QALY). We assessed differences in QALYs and costs between the two arms with Generalized Linear Models (GLMs). Costs, estimated in euro (€) 2020, were combined with QALYs to estimate the Incremental Cost-effectiveness ratio (ICER) with non-parametric bootstrap.

RESULTS

Total costs per patien were lower in the H-RT arm compared to the C-RT arm €3,062 (95 % CI: 2,368 to 3,754) versus €4,285 (95 % CI: 3,355 to 5,215), (p < 0.05). QALY were marginally higher in the H-RT arm, however this difference was not significant: 0.044 (95 % CI: - 0.016 to 0.099).

CONCLUSIONS

Treating localized prostate cancer with moderate H-RT could reduce national health insurance spending. Adopting such a treatment with an updated reimbursement tariff would result in improving resource allocation in RT management.

Methodological Comparison of Mapping the Expanded Prostate Cancer Index Composite to EuroQoL-5D-3L Using Cross-Sectional and Longitudinal Data: Secondary Analysis of NRG/RTOG 0415

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.