Economic Dimensions of Personalized and Precision Medicine

September 21-22, 2016
Ernst Berndt of MIT, Dana Goldman of the University of Southern California, and John Rowe of Columbia University, Organizers

Darius N. Lakdawalla, University of Southern California and NBER, and Jason Shafrin, Precision Health Economics

Predicting Real-World Effectiveness using Overall Survival and Progression-Free Survival from Clinical Trials: Empirical Evidence for the ASCO Value Framework

Physicians and patients must translate clinical evidence into assessments of likely real-world benefits. To help them tackle this difficult problem, the American Society of Clinical Oncology (ASCO) recently updated a framework for measuring the real-world value of cancer treatments. The framework assumes that real-world survival benefits will be 20% below progression-free survival gains from randomized controlled trials (RCTs), but will be exactly equal to overall survival (OS) gains from RCTs. No empirical evidence has been cited to support these crucial assumptions. Lakdawalla and Shafrin found that real-world OS treatment benefits were similar to those observed in RCTs based on OS endpoints, but were approximately 16% less than RCTs based on surrogate endpoints in the five tumors studied. The researchers' findings provide an empirical basis for refining the ASCO value framework and associated clinical decision tools.

Mark Pauly, University of Pennsylvania and NBER

Cost Sharing in Insurance Coverage for Precision Medicine: Preliminary Notes

David H. Howard, Emory University; Jason Hockenberry, Emory University and NBER; and Guy David, University of Pennsylvania and NBER

Personalized Medicine in Action: The Case of Intensity Modulated Radiation Therapy

Breast cancer patients typically receive either conventional beam radiation or intensity modulated radiation therapy (IMRT) following surgery. Medicare spending is $8,000 higher for patients who receive IMRT, but there is little evidence that IMRT is superior to conventional beam radiotherapy. Using SEER-Medicare data, Howard, Hockenberry, and David show that patients treated in freestanding radiation therapy centers, most of which are physician-owned, are substantially more likely to receive IMRT. Use of a readily-observable marker of patients' ability to benefit from IMRT, tumor laterality, has a strong impact on its use. However, incentives affect how physicians incorporate the marker into treatment decisions: patients treated in freestanding centers are more likely to receive IMRT regardless of whether their tumor is in the left or right breast. These results highlight the challenge of maximizing the benefit of tests that imperfectly predict patients' ability to benefit from a treatment in an environment where physicians' compensation is tied to the volume of treatments they provide.

Frank R. Lichtenberg, Columbia University and NBER, and Rebecca A. Pulk, Marc S. Williams, and Eric Wright, Geisinger Health System

The Social Cost of Suboptimal Medication Use and the Value of Pharmacogenomic Information: Evidence from Geisinger Health System

Many drugs don't work the same way for everyone, but it is often difficult to predict who will benefit from a medication, who will not respond at all, and who will experience adverse drug reactions, which are a significant cause of hospitalizations and deaths in the United States. In this study Lichtenberg, Pulk, Williams, and Wright will use a unique database, consisting of a 10-year history of clinical and claims data on tens of thousands of patients linked to their genetic information obtained by whole exome sequencing, to estimate the social cost of suboptimal medication use and the value of pharmacogenomic information.

Kristopher Hult, University of Chicago

Measuring the Potential Health Impact of Personalized Medicine: Evidence from MS Treatments

Individuals respond to pharmaceutical treatments differently due to the heterogeneity of patient populations. This heterogeneity can make it difficult to determine how efficacious or burdensome a treatment is for an individual patient. Personalized medicine involves using patient characteristics, therapeutics, or diagnostic testing to understand how individual patients will respond to a given treatment. Personalized medicine increases the health impact of existing treatments by improving the matching process between patients and treatments and by improving a patient's understanding of the risk of serious side effects. In this paper, Hult compares the health impact of new treatment innovations with the potential impact of personalized medicine. He finds that the impact of personalized medicine depends on the number of treatments, the correlation between treatment effects, and the amount of noise in a patient's individual treatment effect signal. He also finds that for multiple sclerosis treatments, personalized medicine has the potential to increase the health impact of existing treatments by up to 60 percent by informing patients of their individual treatment effect and risk of serious side effects.

Karen Eggleston, Stanford University and NBER, and Rachel Jui-f. Lu, Chang Gung University, Taiwan

Economic Dimensions of Personalized and Precision Medicine in Taiwan and Korea: Evidence from Breast Cancer Treatment

The economic and clinical factors that affect the growth of Personalized and Precision Medicine (PPM) vary across countries and institutional contexts. The economies of East Asia are interesting cases for understanding how recent rapid economic growth and population aging interacts with changing technologies of care. Taiwan and Korea, as prototypical "Asian tigers," both have National Health Insurance (NHI) systems straining to finance universal health coverage under pressures of rising population expectations and the ever-increasing capabilities of medicine. Eggleston and Lu propose to examine the Taiwan and Korean experience over the past two decades with incorporating PPM into NHI coverage and its implications for disparities in treatment, patient outcomes, and medical spending.

John A. Graves and Josh Peterson, Vanderbilt University

Rational Integration of Genomic Healthcare Technology: Evidence from PREDICT

A widely-held vision arising from the sequencing of the human genome is to guide health care decision-making with genetic data to improve patient care -- a promise that is fueled by extraordinary advances in the discovery of genomic variation that predicts therapeutic response. Already, the Food and Drug Administration (FDA) recognizes many interactions between gene variants and drug outcomes; currently more than 70 drug labels include references to germline pharmacogenomic information that can affect prescribing across a wide array of diseases and conditions. Yet while scientific evidence underlying pharmacogenomics is expanding rapidly, parallel efforts to understand the economic incentives and behavioral changes related to characterizing individuals' genetic risks are lacking. Existing research on the value of pharmacogenomics has largely focused on the short-term cost effectiveness of single gene tests – an approach that ignores the potential lifetime value of multigene assays and sequencing. Further, the cascading impact of inexpensive gene panel tests on individual and provider incentives and behavior, new health care spending, and changes in patient outcomes is still poorly understood. Thus, the feasibility and economic value of large-scale genetic testing for current health systems remains unproven. This has slowed translation to clinical practice, and prompted major payers (e.g. Medicare and private insurers) to reassess reimbursements for genetic testing. If these economic challenges are not addressed, it will be difficult if not impossible to capture the potential value of pharmacogenomics in particular and precision medicine more broadly. Graves and Peterson will address these gaps by leveraging empirical insights from an active, real-world precision medicine program (PREDICT; Pharmacogenomic Resource for Enhanced Decisions in Care & Treatment). Their paper will focus on how physicians respond to multiplexed pharmacogenetic testing – and how this response is mediated by changes in the insurance, payment, scientific, and health system environments. This distinct contribution fits squarely within the researchers' ongoing research program funded by the National Institutes of Health Common Fund Health Economics program.

Philippe P.E. Gorry, Cyril Benoit, Diego Useche, and Martin Zumpe, University of Bordeaux

Empirical Economic Analysis of Orphan Drug Innovation

In many countries, governments are trying to stimulate the development of new drugs for unmet health needs with public policy measures. A common policy between the United States (US) and European Union (EU) is the legislation on Orphan Drugs (OD). This term is dedicated to treatments for rare diseases benefiting in EU and the US of a special status with economic incentives to encourage R&D in the pharmaceutical industry. While OD legislation has more than 15 years in EU, and more than 30 years in US, one may wonder about the effectiveness of this legislation on the development of new OD, on the profitability of these OD for the pharmaceutical industry, and the rationality of the pricing and reimbursement of these drugs. Gorry, Benoit, Useche, and Zumpe wish to address the impact of this legislation on pharmaceutical innovation for rare diseases with a EU-US comparison. The research project focuses on 6 objectives: analyzing the effectiveness of technology transfer and identifying market friction mechanisms, studying the attractiveness of private investment in OD R&D and the survival of biotechnology companies, measuring the R&D failure rate and the risks associated with OD R&D, and finally comparing the different mechanisms of price regulation, and their impact on OD.

Amitabh Chandra, Harvard University and NBER; Craig Garthwaite, Northwestern University and NBER; and Ariel Dora Stern, Harvard University

Characterizing the Drug Development Pipeline for Precision Therapies

Manuel Hermosilla, Johns Hopkins University, and Jorge Lemus, Northwestern University

Scientific Support for Genetically-Targeted Therapies: Investigating the Hypothesis of Technological Hype

The completion of Human Genome Project in 2003 enabled the systematic study of gene-disease associations. Exploiting a rich dataset, and focusing on the first generation of studies on gene-disease associations, Hermosilla and Lemus evaluate the extent, pace, and outcomes of the translation of new scientific knowledge into drug discovery efforts. Overly optimistic expectations about the potential of genetic-based methods for the identification of novel drug targets fueled initial fast-paced translation. The extent of translation gradually slowed down and ultimately vanished as lackluster clinical trial performance began to be observed. Currently, authors studying how firms switched from the first technology (GWAS) to a more promising one (Next Generation Sequencing)

Ernst Berndt, and Mark Trusheim, MIT

The Competitive Dynamics of Personalized and Precision Medicine: Insights from Game Theory

(background paper) (slides)

Tomas Philipson, University of Chicago and NBER

The New Information Economics of Personalized Medicine: Current Findings and Future Research

NBER Videos

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