Use of robust, quantitative tools to measure patient perspectives within product development and regulatory review processes offers the opportunity for medical device researchers, regulators, and other stakeholders to evaluate what matters most to patients and support the development of products that can best meet patient needs. The medical device innovation consortium (MDIC) undertook a series of projects, including multiple case studies and expert consultations, to identify approaches for utilizing patient preference information (PPI) to inform clinical trial design in the US regulatory context. Based on these activities, this paper offers a cogent review of considerations and opportunities for researchers seeking to leverage PPI within their clinical trial development programs and highlights future directions to enhance this field. This paper also discusses various approaches for maximizing stakeholder engagement in the process of incorporating PPI into the study design, including identifying novel endpoints and statistical considerations, crosswalking between attributes and endpoints, and applying findings to the population under study. These strategies can help researchers ensure that clinical trials are designed to generate evidence that is useful to decision makers and captures what matters most to patients.

The case for investing in fusion energy has never been greater, given increasing global energy demand, high annual carbon dioxide output, and technological limitations for wind and solar power. Nevertheless, financing for fusion companies through traditional means has proven challenging. While fusion startups have an unparalleled upside, their high upfront costs, lengthy delay in payoff, and high risk of commercial failure have historically restricted funding interest to a niche set of investors. Drawing on insights from investor interviews and case studies of public–private partnerships, we propose a megafund structure in which a large number of projects are securitized into a single holding company funded through various debt and equity tranches, with first loss capital guarantees from governments and philanthropic partners. The megafund exploits many of the core properties of the fusion industry: the diversity of approaches to engender fusion reactions, the ability to create revenue-generating divestitures in related fields, and the breadth of auxiliary technologies needed to support a functioning power plant. The model expands the pool of available capital by creating tranches with different risk–return tradeoffs and providing a diversified “fusion index” that can be viewed as a long hedge against fossil fuels. Simulations of a fusion megafund demonstrate positive returns on equity (ROE) and low default rates for the capital raised using debt.

Most people will experience a severe pandemic within their lifetime, and the world remains dangerously unprepared. In fact, scientists predict a nearly 50% chance––the same probability as flipping heads or tails on a coin––that we will endure another COVID-19-level pandemic within the next 25 years. Shifting America’s pandemic response capability from reactive to proactive is, therefore, urgent. Failure to do so risks the country’s welfare.

Getting ahead of the next pandemic is impossible without government financing. Vaccine production is costly, and these expenses will hinder industries from preemptively developing the tools needed to halt disease transmission. For example, the total expected revenues over a 20-year vaccine patent lifecycle would cover just half of the upfront research and development (R&D) costs.

However, research suggests that a portfolio-based approach to vaccine development —  especially now with new, broadly applicable mRNA technology — dramatically increases the returns on investment while also guarding against an estimated 31 of the next 45 epidemic outbreaks. With lessons learned from Operation Warp Speed, Congress can deploy this approach by (i) authorizing and appropriating $10 billion to the Biomedical Advanced Research and Development Authority (BARDA) (ii) developing a vaccine portfolio for 10 emerging infectious diseases (EIDs), and (iii) a White House Office of Science and Technology Policy (OSTP)-led interagency effort focused on scaling up production of priority vaccines.

Impact investing typically involves ranking and selecting assets based on a non-financial impact factor, such as the environmental, social, and governance (ESG) score and the prospect of developing a disease-curing drug. We develop a framework for constructing optimal impact portfolios and quantifying their financial performances. Under general bivariate distributions of the impact factor and residual returns from a multi-factor asset-pricing model, the construction and performance of optimal impact portfolios depend critically on the dependence structure (copula) between the two, which reduces to a correlation under normality assumptions. More generally, we explicitly derive the optimal portfolio weights under two widely-used copulas---the Gaussian copula and the Archimedean copula family, and find that the optimal weights depend on the tail characteristics of the copula. In addition, when the marginal distribution of residual returns is skewed or heavy-tailed, assets with the most extreme impact factors have lower weights than non-extreme assets due to their high risk. Our framework requires the estimation of only a constant number of parameters as the number of assets grow, an advantage over traditional Markowitz portfolios. Overall, these results provide a recipe for constructing and quantifying the performance of optimal impact portfolios with arbitrary dependence structures and return distributions.

How does a firm’s market power in existing products affect its incentives to innovate? We explore this fundamental question using granular project-level and firm-level data from the pharmaceutical industry, focusing on a particular mechanism through which incumbent firms maintain their market power: “reverse payment” or “pay-for-delay” agreements to delay the market entry of competitors. We first show that when firms are unfettered in their use of “pay-for-delay” agreements, they reduce their innovation activities in response to the potential entry of direct competitors. We then examine a legal ruling that subjected these agreements to antitrust litigation, thereby reducing the incentive to enter them. After the ruling, incumbent firms increased their net innovation activities in response to competitive entry. These effects center on firms with products that are more directly affected by competition. However, at the product therapeutic area level, we find a reduction in innovation by new entrants after the ruling in response to increased competition. Overall, these results are consistent with firms having reduced incentives to innovate when they are able to maintain their market power, highlighting a specific channel through which this occurs.

We examine the contagion of investment ideas in a multiperiod setting in which investors are more likely to transmit their ideas to other investors after experiencing higher payoffs in one of two investment styles with different return distributions. We show that heterogeneous investment styles are able to coexist in the long run, implying a greater diversity than traditional theory predicts. We characterize the survival and popularity of styles in relation to the distribution of security returns. In addition, we demonstrate that psychological effects such as conformist preference can lead to oscillations and bubbles in the choice of style. These results offer empirically testable predictions, and provide new insights into the persistence of the wide range of investment strategies used by individual investors, hedge funds, and other professional portfolio managers.

Hamilton’s rule [W. D. Hamilton, Am. Nat. 97, 354–356 (1963); W. D. Hamilton, J. Theor. Biol. 7, 17–52 (1964)] quantifies the central evolutionary ideas of inclusive fitness and kin selection into a simple algebraic relationship. Evidence consistent with Hamilton’s rule is found in many animal species. A drawback of investigating Hamilton’s rule in these species is that one can estimate whether a given behavior is consistent with the rule, but a direct examination of the exact cutoff for altruistic behavior predicted by Hamilton is almost impossible. However, to the degree that economic resources confer survival benefits in modern society, Hamilton’s rule may be applicable to economic decision-making, in which case techniques from experimental economics offer a way to determine this cutoff. We employ these techniques to examine whether Hamilton’s rule holds in human decision-making, by measuring the dependence between an experimental subject’s maximal willingness to pay for a gift of $50 to be given to someone else and the genetic relatedness of the subject to the gift’s recipient. We find good agreement with the predictions of Hamilton’s rule. Moreover, regression analysis of the willingness to pay versus genetic relatedness, the number of years living in the same residence, age, and sex shows that almost all the variation is explained by genetic relatedness. Similar but weaker results are obtained from hypothetical questions regarding the maximal risk to her own life that the subject is willing to take in order to save the recipient’s life.

The long-run impact and implications of an endowment’s spending policy and asset allocation decisions are examined. Using a dynamic model, the authors explore how different endowment spending rules influence the dynamics of an endowment’s size and future spending. They find that different parameters within each spending rule have significant long-term impact on wealth accumulation and spending capacity. Using Merton's (1993) endowment model and compiled asset allocation data, they estimate the intertemporal preferences and risk aversion of several major endowments and find significant variation across endowments in their propensity to increase portfolio risk in response to increased spending needs.

Using a novel dataset of 653,455 individual brokerage accounts belonging to 298,556 households, we document the frequency, timing, and duration of panic sales, which we define as a decline of 90% of a household account’s equity assets over the course of one month, of which 50% or more is due to trades. We find that a disproportionate number of households make panic sales when there are sharp market downturns, a phenomenon we call ‘freaking out.’ We show that panic selling and freak-outs are predictable and fundamentally different from other well-known behavioral patterns such as overtrading or the disposition effect.

In this article, the authors develop a data-driven peer grouping system using artificial intelligence (AI) tools to capture market perception and, in turn, group companies into clusters at various levels of granularity. In addition, they develop a continuous measure of similarity between companies; they use this measure to group companies into clusters and construct hedged portfolios. In the peer groupings, companies grouped in the same clusters had strong homogeneous risk and return profiles, whereas different clusters of companies had diverse, varying risk exposures. The authors extensively evaluated the clusters and found that companies grouped by their method had higher out-of-sample return correlation but lower stability and interpretability than companies grouped by a standard industry classification system. The authors also develop an interactive visualization system for identifying AI-based clusters and similar companies.

In this article, I describe my approach to dealing with the challenges and opportunities of synchronous online teaching during the Fall semester of 2020 in the specific context of a 90-student graduate course in Healthcare Finance at the MIT Sloan School of Management.

Introduction

Machine learning models are increasingly applied to predict the drug development outcomes based on intermediary clinical trial results. A key challenge to this task is to address various forms of bias in the historical drug approval data.

Objective

We aimed to identify and mitigate the bias in drug approval predictions and quantify the impacts of debiasing in terms of financial value and drug safety.

Methods

We instantiated the Debiasing Variational Autoencoder, the state-of-the-art model for automated debiasing. We trained and evaluated the model on the Citeline dataset provided by Informa Pharma Intelligence to predict the final drug development outcome from phase II trial results.

Results

The debiased Debiasing Variational Autoencoder model achieved better performance (measured by the F1�1 score 0.48) in predicting the drug development outcomes than its un-debiased baseline (F1�1 score 0.25). It had a much higher true-positive rate than baseline (60% vs 15%), while its true-negative rate was slightly lower (88% vs 99%). The Debiasing Variational Autoencoder distinguished between drugs developed by large pharmaceutical firms and those by small biotech companies. The model prediction is strongly influenced by multiple factors such as prior approval of the drug for another indication, whether the trial meets the positive/negative endpoints, and the year when the trial is completed. We estimate that the debiased model generates financial value for the drug developer in six major therapeutic areas, with a range of US$763–1,365 million.

Conclusions

Our analysis shows that debiasing improves the financial efficiency of late-stage drug development. From the pharmacovigilance perspective, the debiased model is more likely to identify drugs that are both safe and effective. Meanwhile, it may predict a higher probability of success for drugs with potential adverse effects (because of its lower true-negative rate), thus it must be used with caution to predict the development outcomes of drug candidates currently in the pipeline.

We review the recent literature on financing biomedical innovation, with a specific focus on the drug development process and how it may be enhanced to improve outcomes. We begin by laying out stylized facts about the structure of the drug development process and its associated costs and risks, and we present evidence that the rate of discovery for life-saving treatments has declined over time while costs have increased. We make the argument that these structural features require drug development (i.e., biopharmaceutical) firms to rely on external financing and at the same time amplify market frictions that may hinder the ability of these firms to obtain financing, especially for treatments that may have large societal value relative to the benefits going to the firms and their investors. We then provide an overview of the evidence for various types of market frictions to which these drug development firms are exposed and discuss how these frictions affect their incentive to invest in the development of new drugs, leading to underinvestment in valuable treatments. In light of this evidence, numerous studies have proposed ways to overcome this funding gap, including the use of financial innovation. We discuss the potential of these approaches to improve outcomes.

Background: The authors estimate the probability of successful development and duration of clinical trials for medications to treat neuropathic and nociceptive pain. The authors also consider the effect of the perceived abuse potential of the medication on these variables.

Methods: This study uses the Citeline database to compute the probabilities of success, duration, and survivorship of pain medication development programs between January 1, 2000, and June 30, 2020, conditioned on the phase, type of pain (nociceptive vs. neuropathic), and the abuse potential of the medication.

Results: The overall probability of successful development of all pain medications from phase 1 to approval is 10.4% (standard error, 1.5%).Medications to treat nociceptive and neuropathic pain have a probability of successful development of 13.3% (standard error, 2.3%) and 7.1% (standard error, 1.9%), respectively. The probability of successful development of medications with high abuse potential and low abuse potential are 27.8% (standard error, 4.6%) and 4.7% (standard error, 1.2%), respectively. The most common period for attrition is between phase 3 and approval.

Conclusions: The authors’ data suggest that the unique attributes of pain medications, such as their abuse potential and intended pathology, can influence the probability of successful development and duration of development.