Hamilton's rule in economic decision-making.

Hamilton's rule [W. D. Hamilton, Am. Nat. 97, 354­356 (1963); W. D. Hamilton, J. Theor. Biol. 7, 17­52 (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 origin of cooperation.

We construct an evolutionary model of a population consisting of two types of interacting individuals that reproduce under random environmental conditions. We show that not only does the evolutionarily dominant behavior maximize the number of offspring of each type, it also minimizes the correlation between the number of offspring of each type, driving it toward -1. We provide several examples that illustrate how correlation can be used to explain the evolution of cooperation.

The estimated annual financial impact of gene therapy in the United States.

Gene therapy is a new class of medical treatment that alters part of a patient's genome through the replacement, deletion, or insertion of genetic material. While still in its infancy, gene therapy has demonstrated immense potential to treat and even cure previously intractable diseases. Nevertheless, existing gene therapy prices are high, raising concerns about its affordability for U.S. payers and its availability to patients. We assess the potential financial impact of novel gene therapies by developing and implementing an original simulation model which entails the following steps: identifying the 109 late-stage gene therapy clinical trials underway before January 2020, estimating the prevalence and incidence of their corresponding diseases, applying a model of the increase in quality-adjusted life years for each therapy, and simulating the launch prices and expected spending of all available gene therapies annually. The results of our simulation suggest that annual spending on gene therapies will be approximately $20.4 billion, under conservative assumptions. We decompose the estimated spending by treated age group as a proxy for insurance type, finding that approximately one-half of annual spending will on the use of gene therapies to treat non-Medicare-insured adults and children. We conduct multiple sensitivity analyses regarding our assumptions and model parameters. We conclude by considering the tradeoffs of different payment methods and policies that intend to ensure patient access to the expected benefits of gene therapy.

A review of economic issues for gene-targeted therapies: Value, affordability, and access.

The National Center for Advancing Translational Sciences' virtual 2021 conference on gene-targeted therapies (GTTs) encouraged multidisciplinary dialogue on a wide range of GTT topic areas. Each of three parallel working groups included social scientists and clinical scientists, and the three major sessions included a presentation on economic issues related to their focus area. These experts also coordinated their efforts across the three groups. The economics-related presentations covered three areas with some overlap: (1) value assessment, uncertainty, and dynamic efficiency; (2) affordability, pricing, and financing; and (3) evidence generation, coverage, and access. This article provides a synopsis of three presentations, some of their key recommendations, and an update on related developments in the past year. The key high-level findings are that GTTs present unique data and policy challenges, and that existing regulatory, health technology assessment, as well as payment and financing systems will need to adapt. But these adjustments can build on our existing foundation of regulatory and incentive systems for innovation, and much can be done to accelerate progress in GTTs. Given the substantial unmet medical need that exists for these oft-neglected patients suffering from rare diseases, it would be a tragedy to not leverage these exciting scientific advances in GTTs.

Use of Bayesian decision analysis to maximize value in patient-centered randomized clinical trials in Parkinson's disease.

A fixed one-sided significance level of 5% is commonly used to interpret the statistical significance of randomized clinical trial (RCT) outcomes. While it is necessary to reduce the false positive rate, the threshold used could be chosen quantitatively and transparently to specifically reflect patient preferences regarding benefit-risk tradeoffs as well as other considerations. How can patient preferences be explicitly incorporated into RCTs in Parkinson's disease (PD), and what is the impact on statistical thresholds for device approval? In this analysis, we apply Bayesian decision analysis (BDA) to PD patient preference scores elicited from survey data. BDA allows us to choose a sample size (n) and significance level (α) that maximizes the overall expected value to patients of a balanced two-arm fixed-sample RCT, where the expected value is computed under both null and alternative hypotheses. For PD patients who had previously received deep brain stimulation (DBS) treatment, the BDA-optimal significance levels fell between 4.0% and 10.0%, similar to or greater than the traditional value of 5%. Conversely, for patients who had never received DBS, the optimal significance level ranged from 0.2% to 4.4%. In both of these populations, the optimal significance level increased with the severity of the patients' cognitive and motor function symptoms. By explicitly incorporating patient preferences into clinical trial designs and the regulatory decision-making process, BDA provides a quantitative and transparent approach to combine clinical and statistical significance. For PD patients who have never received DBS treatment, a 5% significance threshold may not be conservative enough to reflect their risk-aversion level. However, this study shows that patients who previously received DBS treatment present a higher tolerance to accept therapeutic risks in exchange for improved efficacy which is reflected in a higher statistical threshold.

Estimates of Probabilities of Successful Development of Pain Medications: An Analysis of Pharmaceutical Clinical Development Programs from 2000 to 2020.

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.

Can Financial Economics Cure Cancer?

Funding for early-stage biomedical innovation has become more difficult to secure at the same time that medical breakthroughs seem to be occurring at ever increasing rates. One explanation for this counterintuitive trend is that increasing scientific knowledge can actually lead to greater economic risk for investors in the life sciences. While the Human Genome Project, high-throughput screening, genetic biomarkers, immunotherapies, and gene therapies have made a tremendously positive impact on biomedical research and, consequently, patient lives, they have also increased the cost and complexity of the drug development process, causing many investors to shift their assets to more attractive investment opportunities. This suggests that new business models and financing strategies can be used to reduce the risk and increase the attractiveness of biomedical innovation so as to bring new and better therapies to patients faster.

Estimation of clinical trial success rates and related parameters.

Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.

The origin of risk aversion.

Risk aversion is one of the most basic assumptions of economic behavior, but few studies have addressed the question of where risk preferences come from and why they differ from one individual to the next. Here, we propose an evolutionary explanation for the origin of risk aversion. In the context of a simple binary-choice model, we show that risk aversion emerges by natural selection if reproductive risk is systematic (i.e., correlated across individuals in a given generation). In contrast, risk neutrality emerges if reproductive risk is idiosyncratic (i.e., uncorrelated across each given generation). More generally, our framework implies that the degree of risk aversion is determined by the stochastic nature of reproductive rates, and we show that different statistical properties lead to different utility functions. The simplicity and generality of our model suggest that these implications are primitive and cut across species, physiology, and genetic origins.

Dealing with femtorisks in international relations.

The contemporary global community is increasingly interdependent and confronted with systemic risks posed by the actions and interactions of actors existing beneath the level of formal institutions, often operating outside effective governance structures. Frequently, these actors are human agents, such as rogue traders or aggressive financial innovators, terrorists, groups of dissidents, or unauthorized sources of sensitive or secret information about government or private sector activities. In other instances, influential "actors" take the form of climate change, communications technologies, or socioeconomic globalization. Although these individual forces may be small relative to state governments or international institutions, or may operate on long time scales, the changes they catalyze can pose significant challenges to the analysis and practice of international relations through the operation of complex feedbacks and interactions of individual agents and interconnected systems. We call these challenges "femtorisks," and emphasize their importance for two reasons. First, in isolation, they may be inconsequential and semiautonomous; but when embedded in complex adaptive systems, characterized by individual agents able to change, learn from experience, and pursue their own agendas, the strategic interaction between actors can propel systems down paths of increasing, even global, instability. Second, because their influence stems from complex interactions at interfaces of multiple systems (e.g., social, financial, political, technological, ecological, etc.), femtorisks challenge standard approaches to risk assessment, as higher-order consequences cascade across the boundaries of socially constructed complex systems. We argue that new approaches to assessing and managing systemic risk in international relations are required, inspired by principles of evolutionary theory and development of resilient ecological systems.

How does news affect biopharma stock prices?: An event study.

We investigate the impact of information on biopharmaceutical stock prices via an event study encompassing 503,107 news releases from 1,012 companies. We distinguish between pharmaceutical and biotechnology companies, and apply three asset pricing models to estimate their abnormal returns. Acquisition-related news yields the highest positive return, while drug-development setbacks trigger significant negative returns. We also find that biotechnology companies have larger means and standard deviations of abnormal returns, while the abnormal returns of pharmaceutical companies are influenced by more general financial news. To better understand the empirical properties of price movement dynamics, we regress abnormal returns on market capitalization and a sub-industry indicator variable to distinguish biotechnology and pharmaceutical companies, and find that biopharma companies with larger capitalization generally experience lower magnitude of abnormal returns in response to events. Using longer event windows, we show that news related to acquisitions and clinical trials are the sources of potential news leakage. We expect this study to provide valuable insights into how diverse news types affect market perceptions and stock valuations, particularly in the volatile and information-sensitive biopharmaceutical sector, thus aiding stakeholders in making informed investment and strategic decisions.

Incorporating patient preferences and burden-of-disease in evaluating ALS drug candidate AMX0035: a Bayesian decision analysis perspective.

OBJECTIVE: Provide US FDA and amyotrophic lateral sclerosis (ALS) society with a systematic, transparent, and quantitative framework to evaluate the efficacy of the ALS therapeutic candidate AMX0035 in its phase 2 trial, which showed statistically significant effects (p-value 3%) in slowing the rate of ALS progression on a relatively small sample size of 137 patients. METHODS: We apply Bayesian decision analysis (BDA) to determine the optimal type I error rate (p-value) under which the clinical evidence of AMX0035 supports FDA approval. Using rigorous estimates of ALS disease burden, our BDA framework strikes the optimal balance between FDA's need to limit adverse effects (type I error) and patients' need for expedited access to a potentially effective therapy (type II error). We apply BDA to evaluate long-term patient survival based on clinical evidence from AMX0035 and Riluzole. RESULTS: The BDA-optimal type I error for approving AMX0035 is higher than the 3% p-value reported in the phase 2 trial if the probability of the therapy being effective is at least 30%. Assuming a 50% probability of efficacy and a signal-to-noise ratio of treatment effect between 25% and 50% (benchmark: 33%), the optimal type I error rate ranges from 2.6% to 26.3% (benchmark: 15.4%). The BDA-optimal type I error rate is robust to perturbations in most assumptions except for a probability of efficacy below 5%. CONCLUSION: BDA provides a useful framework to incorporate subjective perspectives of ALS patients and objective burden-of-disease metrics to evaluate the therapeutic effects of AMX0035 in its phase 2 trial.

Financing repurposed drugs for rare diseases: a case study of Unravel Biosciences.

BACKGROUND: We consider two key challenges that early-stage biotechnology firms face in developing a sustainable financing strategy and a sustainable business model: developing a valuation model for drug compounds, and choosing an appropriate operating model and corporate structure. We use the specific example of Unravel Biosciences-a therapeutics platform company that identifies novel drug targets through off-target mechanisms of existing drugs and then develops optimized new molecules-throughout the paper and explore a specific scenario of drug repurposing for rare genetic diseases. RESULTS: The first challenge consists of producing a realistic financial valuation of a potential rare disease repurposed drug compound, in this case targeting Rett syndrome. More generally, we develop a framework to value a portfolio of pairwise correlated rare disease compounds in early-stage development and quantify its risk profile. We estimate the probability of a negative return to be [Formula: see text] for a single compound and [Formula: see text] for a portfolio of 8 drugs. The probability of selling the project at a loss decreases from [Formula: see text] (phase 3) for a single compound to [Formula: see text] (phase 3) for the 8-drug portfolio. For the second challenge, we find that the choice of operating model and corporate structure is crucial for early-stage biotech startups and illustrate this point with three concrete examples. CONCLUSIONS: Repurposing existing compounds offers important advantages that could help early-stage biotech startups better align their business and financing issues with their scientific and medical objectives, enter a space that is not occupied by large pharmaceutical companies, and accelerate the validation of their drug development platform.

Leveraging Patient Preference Information in Medical Device Clinical Trial Design.

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.

Patient-Centered Clinical Trial Design for Heart Failure Devices via Bayesian Decision Analysis.

BACKGROUND: The statistical significance of clinical trial outcomes is generally interpreted quantitatively according to the same threshold of 2.5% (in one-sided tests) to control the false-positive rate or type I error, regardless of the burden of disease or patient preferences. The clinical significance of trial outcomes-including patient preferences-are also considered, but through qualitative means that may be challenging to reconcile with the statistical evidence. OBJECTIVE: We aimed to apply Bayesian decision analysis to heart failure device studies to choose an optimal significance threshold that maximizes the expected utility to patients across both the null and alternative hypotheses, thereby allowing clinical significance to be incorporated into statistical decisions either in the trial design stage or in the post-trial interpretation stage. In this context, utility is a measure of how much well-being the approval decision for the treatment provides to the patient. METHODS: We use the results from a discrete-choice experiment study focusing on heart failure patients' preferences, questioning respondents about their willingness to accept therapeutic risks in exchange for quantifiable benefits with alternative hypothetical medical device performance characteristics. These benefit-risk trade-off data allow us to estimate the loss in utility-from the patient perspective-of a false-positive or false-negative pivotal trial result. We compute the Bayesian decision analysis-optimal statistical significance threshold that maximizes the expected utility to heart failure patients for a hypothetical two-arm, fixed-sample, randomized controlled trial. An interactive Excel-based tool is provided that illustrates how the optimal statistical significance threshold changes as a function of patients' preferences for varying rates of false positives and false negatives, and as a function of assumed key parameters. RESULTS: In our baseline analysis, the Bayesian decision analysis-optimal significance threshold for a hypothetical two-arm randomized controlled trial with a fixed sample size of 600 patients per arm was 3.2%, with a statistical power of 83.2%. This result reflects the willingness of heart failure patients to bear additional risks of the investigational device in exchange for its probable benefits. However, for increased device-associated risks and for risk-averse subclasses of heart failure patients, Bayesian decision analysis-optimal significance thresholds may be smaller than 2.5%. CONCLUSIONS: A Bayesian decision analysis is a systematic, transparent, and repeatable process for combining clinical and statistical significance, explicitly incorporating burden of disease and patient preferences into the regulatory decision-making process.