Heterogeneity in action: the role of passive personalization in comparative effectiveness research

Evaluating Policies of Expanding Versus Restricting First-Line Treatment Choices: A Cost-Effectiveness Analysis Framework

OBJECTIVES

Healthcare payers often implement coverage policies that restrict the utilization of costly new first-line treatments. Cost-effectiveness analysis can be conducted to inform these decisions by comparing the new treatment with an existing one. However, this approach may overlook important factors such as treatment effect heterogeneity and endogenous treatment selection, policy implementation costs, and diverse patient preferences across multiple treatment options. We aimed to develop a cost-effectiveness analysis framework that considers these real-world factors, facilitating the evaluation of alternative policies related to expanding or restricting first-line treatment choices.

METHODS

We introduced a metric of incremental cost-effectiveness ratio (ICER) that compares an expanded choice set (CS) including the new first-line treatment with a restricted CS excluding the new treatment. ICER(CS) accounts for treatment selection influenced by heterogeneous treatment effects and policy implementation costs. We examined a basic scenario with 2 standard first-line treatment choices and a more realistic scenario involving diverse preferences toward multiple choices. To illustrate the framework, we conducted a retrospective evaluation of including versus excluding abiraterone acetate plus prednisone (AAP) (androgen deprivation therapy [ADT] + AAP) as a first-line treatment for metastatic hormone-sensitive prostate cancer.

RESULTS

The traditional ICERs for ADT + AAP versus ADT alone and ADT+ docetaxel were $104 269 and $206 324/quality-adjusted life-year, respectively. The ICER(CS) for comparing an expanded CS with ADT + AAP with a restricted CS without ADT + AAP was $123 179/quality-adjusted life-year.

CONCLUSIONS

The proposed framework provides decision makers with policy-relevant tools, enabling them to assess the cost-effectiveness of alternative policies of expanding versus restricting patients' and physicians' first-line treatment choices.

Understanding differences between what alternate propensity score methods estimate

BACKGROUND: Many approaches to propensity score methods are used in the applied health economics and outcomes research literature. Often this creates confusion when different approaches produce different results for the same data. OBJECTIVE: To present a conceptual overview based on a potential outcomes framework to demonstrate how more than 1 mean treatment effect parameter can be estimated using the propensity score methods and how the selection of appropriate methods should align with the scientific questions. METHODS: We highlight that more than 1 mean treatment effect parameter can be estimated using the propensity score methods. Using the potential outcomes framework and alternate data-generating processes, we discuss under what assumptions different mean treatment effect parameter estimates are supposed to vary. We tie these discussions with propensity score methods to show that different approaches may estimate different parameters. We illustrate these methods using a case study of the comparative effectiveness of apixaban vs warfarin on the likelihood of stroke among patients with a prior diagnosis of atrial fibrillation. RESULTS: Different mean treatment effect parameters take on different values when treatment effects are heterogeneous. We show that traditional propensity score approaches, such as blocking, weighting, matching, or doubly robust, can estimate different mean treatment effect parameters. Therefore, they may not produce the same results even when applied to the same data using the same covariates. We found significant differences in our case study estimates of mean treatment effect parameters. Still, once a mean treatment effect parameter is targeted, estimates across different methods are not different. This highlights the importance of first selecting the target parameter for analysis by aligning the interpretation of the target parameter with the scientific questions and then selecting the specific method to estimate this target parameter. CONCLUSIONS: We present a conceptual overview of propensity score methods in health economics and outcomes research from a potential outcomes framework. We hope these discussions will help applied researchers choose appropriate propensity score approaches for their analysis. DISCLOSURES: Dr Unuigbe's time was supported through an unrestricted postdoctoral fellowship from Pfizer to the University of Washington, Seattle.

Incentivizing Prescription Drug Switching to Reduce Patient and Health Plan Spending: A Microsimulation Model

OBJECTIVES

Most spending for prescription drugs is on branded drugs that do not have direct generic equivalents but many of these drugs do have therapeutic alternatives within class. We estimate the potential savings from providing patients a financial incentive to switch from a higher cost drug to a lower cost, therapeutic alternative drug.

METHODS

We used individual state-transition microsimulations to model medication use and spending with and without financial incentives over a 12-month time horizon with a healthcare sector perspective. Costs and utilization inputs were from individuals on branded insulins or multiple sclerosis drugs enrolled in a regional mixed-model health maintenance organization. Base-case model used a one-time financial incentive of $83 and $250 offered to patients on higher cost insulin and multiple sclerosis treatments, respectively, to switch to lower cost drugs within class.

RESULTS

Savings per individual offered an incentive in the insulin and multiple sclerosis classes were, respectively, $84 (95% CI $46-$122) and $2,127 (95% CI $267-$3,987). Varying the incentive size and switch probability resulted in maximum savings of $712 at elasticity of 0.2 and incentive size $250 for the insulin drug class. For the multiple sclerosis drug class, maximum savings of $5945 was at elasticity of 0.2 and incentive size of $1000. Short time horizon makes our savings estimates conservative.

CONCLUSIONS

If programs such as financial incentives could encourage even a small proportion of patients to switch among drugs within therapeutic class, then substantial savings could be generated.

The heterogeneous causal effects of neonatal care: a model of endogenous demand for multiple treatment options based on geographical access to care

Neonatal units in the UK are organised into three levels, from highest Neonatal Intensive Care Unit (NICU), to Local Neonatal Unit (LNU) to lowest Special Care Unit (SCU). We model the endogenous treatment selection of neonatal care unit of birth to estimate the average and marginal treatment effects of different neonatal designations on infant mortality, length of stay and hospital costs. We use prognostic factors, survival and hospital care use data on all preterm births in England for 2014-2015, supplemented by national reimbursement tariffs and instrumental variables of travel time from a geographic information system. The data were consistent with a model of demand for preterm birth care driven by physical access. In-hospital mortality of infants born before 32 weeks was 8.5% overall, and 1.2 (95% CI: -0.7, 3.2) percentage points lower for live births in hospitals with NICU or SCU compared to those with an LNU according to instrumental variable estimates. We find imprecise differences in average total hospital costs by unit designation, with positive unobserved selection of those with higher unexplained absolute and incremental costs into NICU. Our results suggest a limited scope for improvement in infant mortality by increasing in-utero transfers based on unit designation alone.

Adherence and economic impact of paliperidone palmitate versus oral atypical antipsychotics in a Medicare population

Aim: To compare adherence, healthcare utilization and costs among real world, Medicare-eligible patients with schizophrenia using long-acting injectable paliperidone palmitate (PP) versus oral atypical antipsychotics. Patients & methods: Historical cohort study used Medicare Advantage claims data. Inverse probability of treatment weighting was applied to adjust for baseline differences. 12-month adherence, healthcare utilization and costs were compared. Results: Patients using PP were more adherent (proportion of days covered ≥0.8; 48.1 vs 32.6%; p < 0.001), had lower odds of hospitalization (odds ratio [OR]: 0.81; 95% CI: 0.68-0.96) and lower medical costs ($11,095; 95% CI: $10,374-11,867 vs $15,551; 95% CI: $14,584-16,583), but higher pharmacy costs ($14,787; 95% CI: $14,117-15,488 vs $5781; 95% CI: $5530-6043). Conclusion: Compared with patients using oral atypical antipsychotics, PP had lower hospitalizations and medical costs with greater medication adherence accompanied by higher pharmacy costs.

Assessing Variation in the Cost of Palivizumab for Respiratory Syncytial Virus Prevention in Preterm Infants

BACKGROUND

The variability in cost of palivizumab treatment, indicated for prevention of respiratory syncytial virus (RSV) infections in high-risk infants, has not been robustly estimated in prior studies. This study aimed to determine the cost variations of palivizumab from a US payer perspective for otherwise healthy preterm infants born 29-35 weeks gestational age (wGA) using infant characteristics and applied dosing regimens.

METHODS

Fenton Growth Charts were merged with World Health Organization Child Growth Standards to estimate preterm infant growth patterns. The merged growth chart was applied to infants who received palivizumab from a prospective, observational registry to determine future body weight using each infant's wGA and birth weight. Using quarter 3 (Q3) 2016-Q2 2017 vial cost, treatment costs at monthly dosing intervals were estimated using expected weights and averaged by age to derive expected mean 2016-2017 RSV seasonal costs per infant under various dosing scenarios.

RESULTS

Given different dosing scenarios (two to five doses), birth month, and growth patterns for preterm infants 29-35 wGA, the estimated average 2016-2017 seasonal cost of palivizumab treatment ranged from $3221 to $12,568. Outpatient-only cost (excluding first dose at hospital discharge) ranged from $1733 to $11,862. The main drivers of costs were dosing regimen (74% of variance), dosing interacted with birth month (17%), and wGA (6%).

CONCLUSION

The considerable variability in the average cost of palivizumab treatment for preterm infants is driven by choice of dosing regimen, wGA, and birth month. Therefore, when estimating the cost of palivizumab, it is important to consider both infant characteristics at each dose and potential dosing regimens.

New Metrics for Economic Evaluation in the Presence of Heterogeneity: Focusing on Evaluating Policy Alternatives Rather than Treatment Alternatives

BACKGROUND

Cost-effectiveness analysis (CEA) methods fail to acknowledge that where cost-effectiveness differs across subgroups, there may be differential adoption of technology. Also, current CEA methods are not amenable to incorporating the impact of policy alternatives that potentially influence the adoption behavior. Unless CEA methods are extended to allow for a comparison of policies rather than simply treatments, their usefulness to decision makers may be limited.

METHODS

We conceptualize new metrics, which estimate the realized value of technology from policy alternatives, through introducing subgroup-specific adoption parameters into existing metrics, incremental cost-effectiveness ratios (ICERs) and Incremental Net Monetary Benefits (NMBs). We also provide the Loss with respect to Efficient Diffusion (LED) metrics, which link with existing value of information metrics but take a policy evaluation perspective. We illustrate these metrics using policies on treatment with combination therapy with a statin plus a fibrate v. statin monotherapy for patients with diabetes and mixed dyslipidemia.

RESULTS

Under the traditional approach, the population-level ICER of combination v. monotherapy was $46,000/QALY. However, after accounting for differential rates of adoption of the combination therapy (7.2% among males and 4.3% among females), the modified ICER was $41,733/QALY, due to the higher rate of adoption in the more cost-effective subgroup (male). The LED metrics showed that an education program to increase the uptake of combination therapy among males would provide the largest economic returns due to the significant underutilization of the combination therapy among males under the current policy.

CONCLUSION

This framework may have the potential to improve the decision-making process by producing metrics that are better aligned with the specific policy decisions under consideration for a specific technology.

A Framework for Prioritizing Research Investments in Precision Medicine

INTRODUCTION

The adoption of precision medicine (PM) has been limited in practice to date, and yet its promise has attracted research investments. Developing foundational economic approaches for directing proper use of PM and stimulating growth in this area from multiple perspectives is thus quite timely.

METHODS

Building on our previously developed expected value of individualized care (EVIC) framework, we conceptualize new decision-relevant metrics to better understand and forecast the expected value of PM. Several aspects of behavior at the patient, physician, and payer levels are considered that can inform the rate and manner in which PM innovations diffuse throughout the relevant population. We illustrate this framework and the methods using a retrospective evaluation of the use of OncotypeDx genomic test among breast cancer patients.

RESULTS

The enriched metrics can help inform many facets of PM decision making, such as evaluating alternative reimbursement levels for PM tests, implementation and education programs for physicians and patients, and decisions around research investments by manufacturers and public entities. We replicated prior published results on evaluation of OncotypeDx among breast cancer patients but also illustrated that those results are based on assumptions that are often not met in practice. Instead, we show how incorporating more practical aspects of behavior around PM could lead to drastically different estimates of value.

CONCLUSION

We believe that the framework and the methods presented can provide decision makers with more decision-relevant tools to explore the value of PM. There is a growing recognition that data on adoption is important to decision makers. More research is needed to develop prediction models for potential diffusion of PM technologies.

Co-care: Producing better health outcome through interactions between patients, care providers and information and communication technology

The demands on healthcare are shifting, from caring for patients with acute conditions managed in a single-care episode to caring for patients with chronic and often complex conditions. With this shift comes a recognition that healthcare requires an interaction between patients and care providers, and of the interdependencies between these actors for achieving a positive outcome – that the results are co-produced. This paper introduces co-care, which stresses that the role of healthcare providers is to complement people’s own resources for managing their health so that patients’ and healthcare providers’ resources combined leads to the best possible outcome. This is done using tools and artifacts such as information and communication technology that enable knowledge to be created, shaped, shared and applied across the actors. Thus, in co-care, knowledge is not attributed to a single entity but distributed between them in line with the theory of distributed cognition. To put co-care into practice, several challenges must be addressed. This includes moving from profession-centeredness to patient-centeredness and from approaching care as a transformation of input to products to viewing care as linking needs and knowledge, as well as a substantial attitude and behavior change across healthcare stakeholders.

What Contributes Most to High Health Care Costs? Health Care Spending in High Resource Patients

BACKGROUND

U.S. health care spending nearly doubled in the decade from 2000-2010. Although the pace of increase has moderated recently, the rate of growth of health care costs is expected to be higher than the growth in the economy for the near future. Previous studies have estimated that 5% of patients account for half of all health care costs, while the top 1% of spenders account for over 27% of costs. The distribution of health care expenditures by type of service and the prevalence of particular health conditions for these patients is not clear, and is likely to differ from the overall population.

OBJECTIVE

To examine health care spending patterns and what contributes to costs for the top 5% of managed health care users based on total expenditures.

METHODS

This retrospective observational study employed a large administrative claims database analysis of health care claims of managed care enrollees across the full age and care spectrum. Direct health care expenditures were compared during calendar year 2011 by place of service (outpatient, inpatient, and pharmacy), payer type (commercially insured, Medicare Advantage, and Medicaid managed care), and therapy area between the full population and high resource patients (HRP).

RESULTS

The mean total expenditure per HRP during calendar year 2011 was $43,104 versus $3,955 per patient for the full population. Treatment of back disorders and osteoarthritis contributed the largest share of expenditures in both HRP and the full study population, while chronic renal failure, heart disease, and some oncology treatments accounted for disproportionately higher expenditures in HRP. The share of overall expenditures attributed to inpatient services was significantly higher for HRP (40.0%) compared with the full population (24.6%), while the share of expenditures attributed to pharmacy (HRP = 18.1%, full = 21.4%) and outpatient services (HRP = 41.9%, full = 54.1%) was reduced. This pattern was observed across payer type. While the use of physician-administered pharmaceuticals was slightly higher in HRP, their use did not alter this spending pattern.

CONCLUSIONS

Overall, expenditures in the HRP population are more than 10-fold higher compared with the full population. Managed care pharmacy can benefit from understanding what contributes to these higher costs, and managed care directors should consider an appropriately balanced assessment of the share of total spend by service and therapeutic category in HRP when devising drug usage and related cost-management strategies.

ESTIMATING PERSON-CENTERED TREATMENT (PeT) EFFECTS USING INSTRUMENTAL VARIABLES: AN APPLICATION TO EVALUATING PROSTATE CANCER TREATMENTS

This paper builds on the methods of local instrumental variables developed by Heckman and Vytlacil (1999, 2001, 2005) to estimate person-centered treatment (PeT) effects that are conditioned on the person's observed characteristics and averaged over the potential conditional distribution of unobserved characteristics that lead them to their observed treatment choices. PeT effects are more individualized than conditional treatment effects from a randomized setting with the same observed characteristics. PeT effects can be easily aggregated to construct any of the mean treatment effect parameters and, more importantly, are well suited to comprehend individual-level treatment effect heterogeneity. The paper presents the theory behind PeT effects, and applies it to study the variation in individual-level comparative effects of prostate cancer treatments on overall survival and costs.

Concepts of 'personalization' in personalized medicine: implications for economic evaluation

CONTEXT

This study assesses if, and how, existing methods for economic evaluation are applicable to the evaluation of personalized medicine (PM) and, if not, where extension to methods may be required.

METHODS

A structured workshop was held with a predefined group of experts (n = 47), and was run using a modified nominal group technique. Workshop findings were recorded using extensive note taking, and summarized using thematic data analysis. The workshop was complemented by structured literature searches.

RESULTS

The key finding emerging from the workshop, using an economic perspective, was that two distinct, but linked, interpretations of the concept of PM exist (personalization by 'physiology' or 'preferences'). These interpretations involve specific challenges for the design and conduct of economic evaluations. Existing evaluative (extra-welfarist) frameworks were generally considered appropriate for evaluating PM. When 'personalization' is viewed as using physiological biomarkers, challenges include representing complex care pathways; representing spillover effects; meeting data requirements such as evidence on heterogeneity; and choosing appropriate time horizons for the value of further research in uncertainty analysis. When viewed as tailoring medicine to patient preferences, further work is needed regarding revealed preferences, e.g. treatment (non)adherence; stated preferences, e.g. risk interpretation and attitude; consideration of heterogeneity in preferences; and the appropriate framework (welfarism vs. extra-welfarism) to incorporate non-health benefits.

CONCLUSIONS

Ideally, economic evaluations should take account of both interpretations of PM and consider physiology and preferences. It is important for decision makers to be cognizant of the issues involved with the economic evaluation of PM to appropriately interpret the evidence and target future research funding.

Are Elderly Patients With Clinically Localized Prostate Cancer Overtreated? Exploring Heterogeneity in Survival Effects

BACKGROUND

Clinical trial evidence shows minimal survival gains and higher complication rates from radical prostatectomy (RP) versus watchful waiting (WW) for elderly men with localized prostate cancer (PCa). It is believed that these patients are overtreated. The current analyses aim to explore patient-level heterogeneity in survival effects, examine matching of patients to treatments in practice, and identify patient characteristics driving heterogenous effects, in order to present more comprehensive evidence about the concerns of overtreatment.

METHODS

Eleven-year all-cause and PCa-specific survival among SEER-Medicare patients diagnosed during 1996-2002 were analyzed using local instrumental variable approaches.

RESULTS

A total of 8462 (77%) of 11,036 patients received RP. The average effects of RP over WW on 11-year overall and cancer-specific survival were 1.1 months (95%CI, -25, 28; P=0.94) and 1.7 months (95%CI, -25, 29; P=0.90) respectively; effects did not differ significantly according to age, race, grade, and stage. Fewer than 1% of patients had significant cancer-specific survival benefit from RP at the 10% level; 6% were expected to gain over 15 months from RP. However, patients with larger expected survival gains from RP were much more likely to receive RP in practice. Such positive self-selection was driven by PCa-specific survival than overall survival. Several comorbidities may play a critical role in predicting who could benefit from RP.

CONCLUSIONS

Our analyses corroborate concerns about PCa overtreatment. A small fraction of screen-detected PCa patients derive survival benefits from RP. Prediction tools should account for patient comorbidities to accurately predict survival benefits of RP over WW.