Predicting utility scores for prostate cancer: mapping the Prostate Cancer Index to the Patient-Oriented Prostate Utility Scale (PORPUS)

Resurrecting Multiattribute Utility Function: Developing a Value Set for Health Utility for Glaucoma

OBJECTIVES

This study aimed to develop a scoring function to calculate health utilities for health states described by the Health Utility for Glaucoma (HUG-5) based on the preferences of the general population in the United States.

METHODS

Preferences for HUG-5 health states were elicited using the standard gamble and visual analog scale through an online survey. Quota-based sampling was used to recruit a representative sample of the US general population in terms of age, sex, and race. A multiple attribute disutility function (MADUF) approach was adopted to derive scoring for the HUG-5. Model fit was assessed using mean absolute error associated with 5 HUG-5 marker health states that describe mild/moderate and severe glaucoma.

RESULTS

Of 634 respondents completing the tasks, 416 were included in the estimation of the MADUF; 260 respondents (63%) considered worst possible HUG-5 health state better than death. The preferred scoring function generates the utilities ranging from 0.05 (worst HUG-5 health state) to 1 (best HUG-5 health state). The correlation between mean elicited and estimated values for marker states was strong (R² = 0.97) with mean absolute error = 0.11.

CONCLUSIONS

The MADUF for HUG-5 is used to measure health utilities on the scale of perfect health and death, which can be used to estimate quality-adjusted life-years for economic evaluations of glaucoma interventions.

Cost-effectiveness analysis of primary treatments for localised prostate cancer: A population-based Markov analysis using real-world evidence

OBJECTIVE

We evaluate cost-effectiveness of primary treatments for localised prostate cancer by uniquely combining prospectively collected real-world outcomes and costs from UCSF Cancer of Prostate Strategic Urologic Research Endeavor (CaPSURE™).

METHODS

Markov models assessed cost-effectiveness of radical prostatectomy (RP), brachytherapy, electron beam radiation therapy (EBRT) and brachytherapy with EBRT by risk from US payers perspective over 8 years. Treatment costs included office visits, hospitalisation, procedures, medication and long-term care. Patients' surveyed HRQoL were mapped into utilities. Incremental cost-effectiveness ratios (ICERs) used cost per quality-adjusted life years (QALYs) and willingness-to-pay of $150,000/QALY.

RESULTS

Cost-effectiveness analysis (CEA) showed for low-risk prostate cancer, EBRT dominated the lowest cost brachytherapy, but RPns and brachytherapy plus EBRT were cost-effective compared to brachytherapy with ICERs of $18,926 and $41,662 per QALY. In medium-risk patients, RP, EBRT and brachytherapy plus EBRT all were cost-effective compared with brachytherapy, with ICERs of $30,604, $22,588 and $21,627/QALY. In high-risk, brachytherapy dominated all treatments. Procedure cost and utility are driving ICER, but probabilistic sensitivity analysis showed the model was robust across variables.

CONCLUSION

This first CEA combining prospective real-world evidence for HRQOL outcomes with costs shows cost-effectiveness of treatments vary by risk groups, providing new evidence for treatment decisions.

Mapping the Patient-Oriented Prostate Utility Scale From the Expanded Prostate Cancer Index Composite and the Short-Form Health Surveys

OBJECTIVES

This study aimed to develop mapping algorithms from the Expanded Prostate Cancer Index Composite (EPIC) and the Short-Form (SF) Health Surveys to the Patient-Oriented Prostate Utility Scale (PORPUS), an econometric instrument specifically developed for patients with prostate cancer.

METHODS

Data were drawn from 2 cohorts concurrently administering PORPUS, EPIC-50, and SF-36v2. The development cohort included patients who had received a diagnosis of localized or locally advanced prostate cancer from 2017 to 2019. The validation cohort included men who had received a diagnosis of localized prostate cancer from 2014 to 2016. Linear regression models were constructed with ln(1 - PORPUS utility) as the dependent variable and scores from the original and brief versions of the EPIC and SF as independent variables. The predictive capacity of mapping models constructed with all possible combinations of these 2 instruments was assessed through the proportion of variance explained (R²) and the agreement between predicted and observed values. Validation was based on the comparison between estimated and observed utility values in the validation cohort.

RESULTS

Models constructed with EPIC-50 with and without SF yielded the highest predictive capacity (R² = 0.884, 0.871, and 0.842) in comparison with models constructed with EPIC-26 (R² = 0.844, 0.827, and 0.776). The intraclass correlation coefficient was excellent in the 4 models (>0.9) with EPIC and SF. In the validation cohort, predicted PORPUS utilities were slightly higher than those observed, but differences were not statistically significant.

CONCLUSIONS

Mapping algorithms from both the original and the abbreviated versions of the EPIC and the SF Health Surveys allow estimating PORPUS utilities for economic evaluations with cost-utility analyses in patients with prostate cancer.

Benchmarking prognosis methods for survivability - A case study for patients with contingent primary cancers

BACKGROUND

There is an increasing number of patients with a first primary cancer who are diagnosed with a second primary cancer, but prognosis methods to predict the survivability of a patient with multiple primary cancers have not been fully benchmarked.

METHODS

This study investigated the five-year survivability prognosis performances of six machine learning approaches. These approaches are: artificial neural network, decision tree (DT), logistic regression, support vector machine, naïve Bayes (NB), and Bayesian network (BN). A synthetic minority over-sampling technique (SMOTE) was used to solve the imbalanced problem, and a nationwide cancer patient database containing 7,845 subjects in Taiwan was used as a sample source. Ten primary and secondary cancers and their key variables affecting the survivability of the patients were identified.

RESULTS

All the models using SMOTE improved sensitivity and specificity significantly. NB has the highest performance in terms of accuracy and specificity, whereas BN has the highest performance in terms of sensitivity. Further, the computational time and the power of knowledge representation of NB, BN, and DT outperformed the others.

CONCLUSIONS

Selecting the appropriate prognosis models to predict survivability of patients with two contingent primary cancers can aid precise prediction and can support appropriate treatment advice.

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

PURPOSE

The Expanded Prostate Cancer Index Composite (EPIC) is the most commonly used patient reported outcome (PRO) tool in prostate cancer (PC) clinical trials, but health utilities associated with the different health states assessed with this tool are unknown, limiting our ability to perform cost-utility analyses. This study aimed to map EPIC tool to EuroQoL-5D-3L (EQ5D) to generate EQ5D health utilities.

METHODS AND MATERIALS

This is a secondary analysis of a prospective, randomized non-inferiority clinical trial, conducted between 04/2006 and 12/2009 at cancer centers across the United States, Canada, and Switzerland. Eligible patients included men >18 years with a known diagnosis of low-risk PC. Patient HRQoL data were collected using EPIC and health utilities were obtained using EQ5D. Data were divided into an estimation sample (n = 765, 70%) and a validation sample (n = 327, 30%). The mapping algorithms that capture the relationship between the instruments were estimated using ordinary least squares (OLS), Tobit, and two-part models. Five-fold cross-validation (in-sample) was used to compare the predictive performance of the estimated models. Final models were selected based on root mean square error (RMSE).

RESULTS

A total of 565 patients in the estimation sample had complete information on both EPIC and EQ5D questionnaires at baseline. Mean observed EQ5D utility was 0.90±0.13 (range: 0.28-1) with 55% of patients in full health. OLS models outperformed their counterpart Tobit and two-part models for all pre-determined model specifications. The best model fit was: "EQ5D utility = 0.248541 + 0.000748*(Urinary Function) + 0.001134*(Urinary Bother) + 0.000968*(Hormonal Function) + 0.004404*(Hormonal Bother)- 0.376487*(Zubrod) + 0.003562*(Urinary Function*Zubrod)"; RMSE was 0.10462.

CONCLUSIONS

This is the first study to identify a comprehensive set of mapping algorithms to generate EQ5D utilities from EPIC domain/ sub-domain scores. The study results will help estimate quality-adjusted life-years in PC economic evaluations.

Combined cancer patient-reported symptom and health utility tool for routine clinical implementation: a real-world comparison of the ESAS and EQ-5D in multiple cancer sites

Background

We assessed whether the presence and severity of common cancer symptoms are associated with the health utility score (hus) generated from the EQ-5D (EuroQol Research Foundation, Rotterdam, Netherlands) in patients with cancer and evaluated whether it is possible pragmatically to integrate routine hus and symptom evaluation in our cancer population.

Methods

Adult outpatients at Princess Margaret Cancer Centre with any cancer were surveyed cross-sectionally using the Edmonton Symptom Assessment System (esas) and the EQ-5D-3L, and results were compared using Spearman correlation coefficients and regression analyses.

Results

Of 764 patients analyzed, 27% had incurable disease. We observed mild-to-moderate correlations between each esas symptom score and the hus (Spearman coefficients: -0.204 to -0.416; p < 0.0001 for each comparison), with the strongest associations being those for pain (R = -0.416), tiredness (R = -0.387), and depression (R =-0.354). Multivariable analyses identified pain and depression as highly associated (both p < 0.0001) and tiredness as associated (p = 0.03) with the hus. The ability of the esas to predict the hus was low, at 0.25. However, by mapping esas pain, anxiety, and depression scores to the corresponding EQ-5D questions, we could derive the hus using partial esas data, with Spearman correlations of 0.83-0.91 in comparisons with direct EQ-5D measurement of the hus.

Conclusions

The hus derived from the EQ-5D-3L is associated with all major cancer symptoms as captured by the esas. The esas scores alone could not predict EQ-5D scores with high accuracy. However, esas-derived questions assessing the same domains as the EQ-5D-3L questions could be mapped to their corresponding EQ-5D questions to generate the hus, with high correlation to the directly measured hus. That finding suggests a potential approach to integrating routine symptom and hus evaluations after confirmatory studies.

A Systematic Review and Meta-Analysis of Prostate Cancer Utility Values of Patients and Partners Between 2007 and 2016

Background. There is widespread agreement that both the length and quality of life matter when assessing new technologies and/or models of care in the treatment for cancer patients. Quality of life for partners/carers also matters, particularly for prostate cancer. Purpose. This systematic review aims to provide up-to-date utility values along the prostate cancer care continuum (i.e., from prescreening through to palliative care) for use where future trial-based or modelled economic evaluations cannot collect primary data from men and/or partners. Data Sources. A protocol was developed and registered on the international register of systematic reviews—PROSPERO. Databases searched included EBSCO Information Services (CINAHL, EconLit, Global Health, HEED, MEDLINE Complete, PsycINFO), Cochrane Database of Systematic Reviews, Web of Science, and Embase. Study Selection. Study selection terms included health-related quality of life, prostate cancer, and partners or carers. Data Extraction. The authors identified articles published between 2007 and 2016 that provided health state utility values, with statistical uncertainty, for men with or at risk of prostate cancer and/or their partner/carers. Data Synthesis and Results. Study quality and generalizability of utilities was evaluated and meta-analysis conducted against prespecified criteria. From 906 original articles, 29 recent primary studies met the inclusion/exclusion criteria. We tabulate all the utility values with uncertainty, along with considerable methodological detail and patient population characteristics. Limitations. Utility values pertaining to carers/partners were limited to one study. Conclusions. Studies varied in design, measurement instruments utilized, quality, and generalizability. There is sufficient qualitative and quantitative detail for the reported utility values to be readily incorporated into economic evaluations. More research is needed with carers/partners and with newly developing prostate cancer-specific quality of life tools.

Quality of life improvement in patients treated with degarelix versus leuprorelin for advanced prostate cancer

PURPOSE

We used responses to questionnaires included in the CS21 degarelix trial and published mapping algorithms to address the paucity of evidence for health related quality of life in patients with advanced hormone dependent prostate cancer treated with degarelix.

MATERIALS AND METHODS

We measured health related quality of life in 610 patients enrolled in the CS21 trial using SF-12® and EORTC QLQ-C30. Based on responses to these questionnaires we estimated patient utility using 4 published mapping algorithms. Utility was tested for relationships with aspects of the symptom and side effect burden that may be affected by degarelix treatment, that is prostate specific antigen progression and adverse events.

RESULTS

Average utility in patients without prostate specific antigen progression or an adverse event was 0.742, similar to previously published utilities for nonprogressed prostate cancer states. Prostate specific antigen progression was associated with a utility decrement of between 0.062 and 0.134 depending on the mapping algorithm used. Of adverse events considered in our analysis musculoskeletal events were associated with the greatest effects on patient utility with a decrement of between 0.029 and 0.086. The 4 mapping algorithms generated similar utility estimates, although values derived from SF-12 were consistently lower than those derived from EORTC QLQ-C30.

CONCLUSIONS

Prostate specific antigen progression status and the incidence of treatment and disease related adverse events result in significant decrements to patient health related quality of life. By slowing prostate specific antigen progression degarelix may improve patient utility and the health related quality of life burden.