Patient Heterogeneity in Health Economic Decision Models for Chronic Obstructive Pulmonary Disease: Are Current Models Suitable to Evaluate Personalized Medicine?

A scoping review on patient heterogeneity in economic evaluations of precision medicine based on basket trials

INTRODUCTION

Considerable challenges in the economic evaluation of precision medicines have been mentioned in previous studies. However, they have not addressed how an economic assessment would be conducted based on basket trials (novel studies for evaluation of precision medicine effects) in which the included populations have specific biomarkers and various cancers. Since basket trial populations have remarkable heterogeneity, this study aims to investigate the concept of heterogeneity and specific method(s) for considering it in economic evaluations through guidelines and studies that could be applicable in economic evaluation based on basket trials.

AREA COVERED

We searched PubMed, Web of Science, Scopus, Google Scholar, and Google to find studies and pharmacoeconomics guidelines. The inclusion criteria included subjects of patient heterogeneity and suggested explicit method(s). Thirty-nine guidelines and 43 studies were included and evaluated. None of these materials mentioned disease types in a target population as a factor causing heterogeneity. Moreover, in economic evaluations, patient heterogeneity has been considered with four general approaches subgroup analysis, individual-based models, sensitivity analysis, and regression models.

EXPERT OPINION

Type of disease is not considered a contributing factor in population heterogeneity, and the probable appropriate method for this issue could be individual-based models.

Addressing Challenges of Economic Evaluation in Precision Medicine Using Dynamic Simulation Modeling

OBJECTIVES

The objective of this article is to describe the unique challenges and present potential solutions and approaches for economic evaluations of precision medicine (PM) interventions using simulation modeling methods.

METHODS

Given the large and growing number of PM interventions and applications, methods are needed for economic evaluation of PM that can handle the complexity of cascading decisions and patient-specific heterogeneity reflected in the myriad testing and treatment pathways. Traditional approaches (eg, Markov models) have limitations, and other modeling techniques may be required to overcome these challenges. Dynamic simulation models, such as discrete event simulation and agent-based models, are used to design and develop mathematical representations of complex systems and intervention scenarios to evaluate the consequence of interventions over time from a systems perspective.

RESULTS

Some of the methodological challenges of modeling PM can be addressed using dynamic simulation models. For example, issues regarding companion diagnostics, combining and sequencing of tests, and diagnostic performance of tests can be addressed by capturing patient-specific pathways in the context of care delivery. Issues regarding patient heterogeneity can be addressed by using patient-level simulation models.

CONCLUSION

The economic evaluation of PM interventions poses unique methodological challenges that might require new solutions. Simulation models are well suited for economic evaluation in PM because they enable patient-level analyses and can capture the dynamics of interventions in complex systems specific to the context of healthcare service delivery.

Broadening the Perspective of Cost-Effectiveness Modeling in Chronic Obstructive Pulmonary Disease: A New Patient-Level Simulation Model Suitable to Evaluate Stratified Medicine

OBJECTIVES

To develop a health economic model that included a great diversity of patient characteristics and outcomes for chronic obstructive pulmonary disease (COPD), which can be used to inform decisions about stratified medicine in COPD.

METHODS

The choice of patient characteristics and outcomes to include in the model was based on 3 literature reviews on multidimensional prognostic COPD indices, COPD phenotypes, and treatment effects in subgroups. A conceptual model was constructed including 14 patient characteristics, 7 intermediate outcomes (lung function, physical activity, exercise capacity, symptoms, disease-specific quality of life, exacerbations, and pneumonias), and 3 final outcomes (mortality, quality-adjusted life-years [QALYs], and costs). Regression equations describing the statistical associations between the patient characteristics and intermediate and final outcomes were estimated using the longitudinal data of 5 large COPD trials (19,378 patients). A patient-level simulation model was developed in which individual patients from the baseline population of the 5 trials are sampled and their outcomes over lifetime are predicted based on the regression equations.

RESULTS

The base-case analysis (single-arm simulation representing treatment with tiotropium) showed that patients had a mean lung function decline of 43 mL/year, 0.62 exacerbations/year, a worsening of their physical activity and quality of life with 1.48 and 1.10 points/year, a life expectancy of 11.2 years, 7.25 QALYs, and total lifetime costs of £24,891. Results for a selection of treatment scenarios and subgroups were shown to demonstrate the potential of the model.

CONCLUSIONS

We developed a unique patient-level simulation model that can be used to evaluate COPD treatment options for a variety of subgroups.

Development and Validation of the Evaluation Platform in COPD (EPIC): A Population-Based Outcomes Model of COPD for Canada

BACKGROUND

We report the development, validation, and implementation of an open-source population-based outcomes model of chronic obstructive pulmonary disease (COPD) for Canada.

METHODS

Evaluation Platform in COPD (EPIC) is a discrete-event simulation model of Canadians 40 years of age or older. Three core features of EPIC are its open-population design (incorporating projections of future population growth, aging, and smoking trends), its incorporation of heterogeneity in lung function decline and burden of exacerbations, and its modeling of the natural history of COPD from inception. Multiple original data analyses, as well as values reported in the literature, were used to populate the model. Extensive face validity and internal and external validity evaluations were performed.

RESULTS

The model was internally validated on demographic projections, mortality rates, lung function trajectories, COPD exacerbations, costs and health state utility values, and stability of COPD prevalence over time within strata of risk factors. In external validation, it moderately overestimated the rate of overall exacerbations in 2 independent trials but generated consistent estimates of rate of severe exacerbations and mortality.

LIMITATIONS

In its current version, EPIC does not consider uncertainty in the evidence. Several components such as additional (e.g., environmental and occupational) risk factors, treatment, symptoms, and comorbidity will have to be added in future iterations. Predictive validity of EPIC needs to be examined prospectively against future empirical studies.

CONCLUSIONS

EPIC is the first multipurpose, open-source, outcome- and policy-focused model of COPD for Canada. Platforms of this type have the capacity to be iteratively updated to incorporate the latest evidence and to project the outcomes of many different scenarios within a consistent framework.

The Future of Precision Medicine: Potential Impacts for Health Technology Assessment

OBJECTIVE

Precision medicine allows healthcare interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information, or treatment response. We analysed what developments are expected in precision medicine over the next decade and considered the implications for health technology assessment (HTA) agencies.

METHODS

We performed a pragmatic literature search to account for the large size and wide scope of the precision medicine literature. We refined and enriched these results with a series of expert interviews up to 1 h in length, including representatives from HTA agencies, research councils and researchers designed to cover a wide spectrum of precision medicine applications and research.

RESULTS

We identified 31 relevant papers and interviewed 13 experts. We found that three types of precision medicine are expected to emerge in clinical practice: complex algorithms, digital health applications and 'omics'-based tests. These are expected to impact upon each stage of the HTA process, from scoping and modelling through to decision-making and review. The complex and uncertain treatment pathways associated with patient stratification and fast-paced technological innovation are central to these effects.

DISCUSSION

Innovation in precision medicine promises substantial benefits but will change the way in which some health services are delivered and evaluated. The shelf life of guidance may decrease, structural uncertainty may increase and new equity considerations will emerge. As biomarker discovery accelerates and artificial intelligence-based technologies emerge, refinements to the methods and processes of evidence assessments will help to adapt and maintain the objective of investing in healthcare that is value for money.

The contribution of symptoms and comorbidities to the economic impact of COPD: an analysis of the German COSYCONET cohort

Background

Although patients with COPD often have various comorbidities and symptoms, limited data are available on the contribution of these aspects to health care costs. This study analyzes the association of frequent comorbidities and common symptoms with the annual direct and indirect costs of patients with COPD.

Methods

Self-reported information on 33 potential comorbidities and symptoms (dyspnea, cough, and sputum) of 2,139 participants from the baseline examination of the German COPD cohort COSYCONET was used. Direct and indirect costs were calculated based on self-reported health care utilization, work absence, and retirement. The association of comorbidities, symptoms, and COPD stage with annual direct/indirect costs was assessed by generalized linear regression models. Additional models analyzed possible interactions between COPD stage, the number of comorbidities, and dyspnea.

Results

Unadjusted mean annual direct costs were €7,263 per patient. Other than COPD stage, a high level of dyspnea showed the strongest driving effect on direct costs (+33%). Among the comorbidities, osteoporosis (+38%), psychiatric disorders (+36%), heart disease (+25%), cancer (+24%), and sleep apnea (+21%) were associated with the largest increase in direct costs (p<0.01). A sub-additive interaction between advanced COPD stage and a high number of comorbidities reduced the independent cost-driving effects of these factors. For indirect costs, besides dyspnea (+34%), only psychiatric disorders (+32%) and age (+62% per 10 years) were identified as significant drivers of costs (p<0.04). In the subsequent interaction analysis, a high number of comorbidities was found to be a more crucial factor for increased indirect costs than single comorbidities.

Conclusion

Detailed knowledge about comorbidities in COPD is useful not only for clinical purposes but also to identify relevant cost factors and their interactions and to establish a ranking of major cost drivers. This could help in focusing therapeutic efforts on both clinically and economically important comorbidities in COPD.

Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources

BACKGROUND AND OBJECTIVES

Exacerbations are important outcomes in COPD both from a clinical and an economic perspective. Most studies investigating predictors of exacerbations were performed in COPD patients participating in pharmacological clinical trials who usually have moderate to severe airflow obstruction. This study was aimed to investigate whether predictors of COPD exacerbations depend on the COPD population studied.

METHODS

A network of COPD health economic modelers used data from five COPD data sources - two population-based studies (COPDGene® and The Obstructive Lung Disease in Norrbotten), one primary care study (RECODE), and two studies in secondary care (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoint and UPLIFT) - to estimate and validate several prediction models for total and severe exacerbations (= hospitalization). The models differed in terms of predictors (depending on availability) and type of model.

RESULTS

FEV1% predicted and previous exacerbations were significant predictors of total exacerbations in all five data sources. Disease-specific quality of life and gender were predictors in four out of four and three out of five data sources, respectively. Age was significant only in the two studies including secondary care patients. Other significant predictors of total exacerbations available in one database were: presence of cough and wheeze, pack-years, 6-min walking distance, inhaled corticosteroid use, and oxygen saturation. Predictors of severe exacerbations were in general the same as for total exacerbations, but in addition low body mass index, cardiovascular disease, and emphysema were significant predictors of hospitalization for an exacerbation in secondary care patients.

CONCLUSIONS

FEV1% predicted, previous exacerbations, and disease-specific quality of life were predictors of exacerbations in patients regardless of their COPD severity, while age, low body mass index, cardiovascular disease, and emphysema seem to be predictors in secondary care patients only.

External Validation of Health Economic Decision Models for Chronic Obstructive Pulmonary Disease (COPD): Report of the Third COPD Modeling Meeting

OBJECTIVES

To validate outcomes of presently available chronic obstructive pulmonary disease (COPD) cost-effectiveness models against results of two large COPD trials-the 3-year TOwards a Revolution in COPD Health (TORCH) trial and the 4-year Understanding Potential Long-term Impacts on Function with Tiotropium (UPLIFT) trial.

METHODS

Participating COPD modeling groups simulated the outcomes for the placebo-treated groups of the TORCH and UPLIFT trials using baseline characteristics of the trial populations as input. Groups then simulated treatment effectiveness by using relative reductions in annual decline in lung function and exacerbation frequency observed in the most intensively treated group compared with placebo as input for the models. Main outcomes were (change in) total/severe exacerbations and mortality. Furthermore, the absolute differences in total exacerbations and quality-adjusted life-years (QALYs) were used to approximate the cost per exacerbation avoided and the cost per QALY gained.

RESULT

Of the six participating models, three models reported higher total exacerbation rates than observed in the TORCH trial (1.13/patient-year) (models: 1.22-1.48). Four models reported higher rates than observed in the UPLIFT trial (0.85/patient-year) (models: 1.13-1.52). Two models reported higher mortality rates than in the TORCH trial (15.2%) (models: 20.0% and 30.6%) and the UPLIFT trial (16.3%) (models: 24.8% and 36.0%), whereas one model reported lower rates (9.8% and 12.1%, respectively). Simulation of treatment effectiveness showed that the absolute reduction in total exacerbations, the gain in QALYs, and the cost-effectiveness ratios did not differ from the trials, except for one model.

CONCLUSIONS

Although most of the participating COPD cost-effectiveness models reported higher total exacerbation rates than observed in the trials, estimates of the absolute treatment effect and cost-effectiveness ratios do not seem different from the trials in most models.