Prasun Subedi

From concepts, theory, and evidence of heterogeneity of treatment effects to methodological approaches: a primer

Implicit in the growing interest in patient-centered outcomes research is a growing need for better evidence regarding how responses to a given intervention or treatment may vary across patients, referred to as heterogeneity of treatment effect (HTE). A variety of methods are available for exploring HTE, each associated with unique strengths and limitations. This paper reviews a selected set of methodological approaches to understanding HTE, focusing largely but not exclusively on their uses with randomized trial data. It is oriented for the “intermediate” outcomes researcher, who may already be familiar with some methods, but would value a systematic overview of both more and less familiar methods with attention to when and why they may be used. Drawing from the biomedical, statistical, epidemiological and econometrics literature, we describe the steps involved in choosing an HTE approach, focusing on whether the intent of the analysis is for exploratory, initial testing, or confirmatory testing purposes. We also map HTE methodological approaches to data considerations as well as the strengths and limitations of each approach. Methods reviewed include formal subgroup analysis, meta-analysis and meta-regression, various types of predictive risk modeling including classification and regression tree analysis, series of n-of-1 trials, latent growth and growth mixture models, quantile regression, and selected non-parametric methods. In addition to an overview of each HTE method, examples and references are provided for further reading.By guiding the selection of the methods and analysis, this review is meant to better enable outcomes researchers to understand and explore aspects of HTE in the context of patient-centered outcomes research.

Treatment Dynamics of Newly Marketed Drugs and Implications for Comparative Effectiveness Research

OBJECTIVES Clinicians and payers require rapid comparative effectiveness (CE) evidence generation to inform decisions for new drugs. We empirically assessed treatment dynamics of newly marked drugs and their implications for conducting CE research. METHODS We used claims data to evaluate five drug-outcome pairs: 1) raloxifene (vs. alendronate) and fracture; 2) risedronate (vs. alendronate) and fracture; 3) simvastatin plus ezetimibe fixed-dose combination (simvastatin + ezetimibe) (vs. simvastatin alone) and cardiovascular events; 4) rofecoxib (vs. nonselective nonsteroidal anti-inflammatory drugs [ns-NSAIDs]) and myocardial infarction; and 5) rofecoxib (vs. ns-NSAIDS) and gastrointestinal bleed. We examined utilization dynamics in the early marketing period, including evolving utilization patterns, outcome risk among those treated with new versus established drugs, and prior treatment patterns that may indicate treatment resistance or intolerance. We addressed these challenges by replicating active CE monitoring with sequential matched cohort analysis. RESULTS Patients initiating new drugs were more likely to have used other drugs for the same indication in the past, but the majority of patients in all new drug cohorts were treatment naive (82.0% overall). Patients initiating rofecoxib had higher predicted baseline risk of gastrointestinal bleed than did patients initiating ns-NSAIDs. Patients initiating risedronate and alendronate had similar predicted baseline risks of fracture, while those initiating raloxifene and simvastatin + ezetimibe had lower risks of outcomes of interest relative to their comparators. Prospective monitoring yielded results consistent with expectation for each example. CONCLUSIONS Many challenges to assessing the CE of new drugs are borne out in empirical data. Attention to these challenges can yield valid CE results.

Survival among patients with advanced renal cell carcinoma in the pretargeted versus targeted therapy eras

Between December 2005 and October 2009, FDA approved six targeted therapies shown to significantly extend survival for advanced renal cell carcinoma (RCC) patients in clinical trials. This study aimed to examine changes in survival between the pretargeted and targeted therapy periods in advanced RCC patients in a real‐world setting. Utilizing the 2000–2010 SEER Research files, a pre–post study design with a contemporaneous comparison group was employed to examine differences in survival outcomes for patients diagnosed with advanced RCC (study group) or advanced prostate cancer (comparison group, for whom no significant treatment innovations happened during this period) across the pretargeted therapy era (2000–2005) and the targeted therapy era (2006–2010). RCC patients diagnosed in the targeted therapy era (N = 6439) showed improved survival compared to those diagnosed in the pretargeted therapy era (N = 7231, hazard ratio (HR) for all‐cause death: 0.86, P < 0.01), while the change between the pre–post periods was not significant for advanced prostate cancer patients (HR: 0.97, P = 0.08). Advanced RCC patients had significantly larger improvements in overall survival compared to advanced prostate cancer patients (z = 4.31; P < 0.01). More detailed year‐to‐year analysis revealed greater survival improvements for RCC in the later years of the posttargeted period. Similar results were seen for cause‐specific survival. Subgroup analyses by nephrectomy status, age, and gender showed consistent findings. Patients diagnosed with advanced RCC during the targeted therapy era had better survival outcomes than those diagnosed during the pretargeted therapy era. Future studies should examine the real‐world survival improvements directly associated with targeted therapies.

Optimizing the Leveraging of Real-World Data to Improve the Development and Use of Medicines

Health research, including health outcomes and comparative effectiveness research, is on the cusp of a golden era of access to digitized real-world data, catalyzed by the adoption of electronic health records and the integration of clinical and biological information with other data. This era promises more robust insights into what works in health care. Several barriers, however, will need to be addressed if the full potential of these new data are fully realized; these will involve both policy solutions and stakeholder cooperation. Although a number of these issues have been widely discussed, we focus on the one we believe is the most important-the facilitation of greater openness among public and private stakeholders to collaboration, connecting information and data sharing, with the goal of making robust and complete data accessible to all researchers. In this way, we can better understand the consequences of health care delivery, improve the effectiveness and efficiency of health care systems, and develop advancements in health technologies. Early real-world data initiatives illustrate both potential and the need for future progress, as well as the essential role of collaboration and data sharing. Health policies critical to progress will include those that promote open source data standards, expand access to the data, increase data capture and connectivity, and facilitate communication of findings.

Financing a Cure for Diabetes in a Multipayer Environment

BACKGROUND Financing medical breakthroughs or cures is becoming increasingly challenging in the current fiscal environment. OBJECTIVES In this paper, we develop the precise conditions needed for a financing mechanism, HealthCoin, to work between a private payer and Medicare, to incentivize the former to invest in breakthrough therapies or cures in the US. METHODS We illustrate the valuation of such a currency for a cure of Type 2 diabetes. RESULTS We show that without a HealthCoin, a private payer does not invest in the cure, a small fraction of the patients live up to age 65, Medicare pays for the full price of the cure at age 65 and incurs net loss in returns over the elderly ages, and the manufacturer only sells cures for those who reach age 65. In contrast, a HealthCoin is feasible in this market, incentivizing the private payer to invest in the cure during the non-elderly ages and leading to Pareto improvements for all three stakeholders. CONCLUSIONS Discussions around the applicability of HealthCoin for breakthrough therapies on the horizon, such as gene therapies for blindness and hemophilia B, and the feasibility of instituting such payments through new legislations or demonstration projects could be of great value.

A Review of Empirical Analyses of Disinvestment Initiatives

BACKGROUND Disinvesting in low-value health care services provides opportunities for investment in higher value care and thus an increase in health care efficiency. OBJECTIVES To identify international experience with disinvestment initiatives and to review empirical analyses of disinvestment initiatives. METHODS We performed a literature search using the PubMed database to identify international experience with disinvestment initiatives. We also reviewed empirical analyses of disinvestment initiatives. RESULTS We identified 26 unique disinvestment initiatives implemented across 11 countries. Nineteen addressed multiple intervention types, six addressed only drugs, and one addressed only devices. We reviewed 18 empirical analyses of disinvestment initiatives: 7 reported that the initiative was successful, 8 reported that the initiative was unsuccessful, and 3 reported that findings were mixed; that is, the study considered multiple services and reported a decrease in the use of some but not others. Thirty-seven low-value services were evaluated across the 18 empirical analyses, for 14 (38%) of which the disinvestment initiative led to a decline in use. Six of the seven studies that reported the disinvestment initiative to be successful included an attempt to promote the disinvestment initiative among participating clinicians. CONCLUSIONS The success of disinvestment initiatives has been mixed, with fewer than half the identified empirical studies reporting that use of the low-value service was reduced. Our findings suggest that promotion of the disinvestment initiative among clinicians is a key component to the success of the disinvestment initiative.

Payer and Pharmaceutical Manufacturer Considerations for Outcomes-Based Agreements in the United States☆

BACKGROUND Considerable interest exists among health care payers and pharmaceutical manufacturers in designing outcomes-based agreements (OBAs) for medications for which evidence on real-world effectiveness is limited at product launch. OBJECTIVES To build hypothetical OBA models in which both payer and manufacturer can benefit. METHODS Models were developed for a hypothetical hypercholesterolemia OBA, in which the OBA was assumed to increase market access for a newly marketed medication. Fixed inputs were drug and outcome event costs from the literature over a 1-year OBA period. Model estimates were developed using a range of inputs for medication effectiveness, medical cost offsets, and the treated population size. Positive or negative feedback to the manufacturer was incorporated on the basis of expectations of drug performance through changes in the reimbursement level. Model simulations demonstrated that parameters had the greatest impact on payer cost and manufacturer reimbursement. RESULTS Models suggested that changes in the size of the population treated and drug effectiveness had the largest influence on reimbursement and costs. Despite sharing risk for potential product underperformance, manufacturer reimbursement increased relative to having no OBA, if the OBA improved market access for the new product. Although reduction in medical costs did not fully offset the cost of the medication, the payer could still save on net costs per patient relative to having no OBA by tying reimbursement to drug effectiveness. CONCLUSIONS Pharmaceutical manufacturers and health care payers have demonstrated interest in OBAs, and under a certain set of assumptions both may benefit.