Implicit in the drug-approval process is a trade-off between Type I and Type II error. We propose using Bayesian decision analysis (BDA) to minimize the expected cost of drug approval, where relative costs are calibrated using U.S. Burden of Disease Study 2010 data. The results for conventional fixed-sample randomized clinical-trial designs suggest that for terminal illnesses with no existing therapies such as pancreatic cancer, the standard threshold of 2.5% is too conservative; the BDA-optimal threshold is 27.9%. However, for relatively less deadly conditions such as prostate cancer, 2.5% may be too risk-tolerant or aggressive; the BDA-optimal threshold is 1.2%. We compute BDA-optimal sizes for 25 of the most lethal diseases and show how a BDA-informed approval process can incorporate all stakeholders’ views in a systematic, transparent, internally consistent, and repeatable manner.

We develop a theory of optimal financing for R&D-intensive firms. With only market financing, the firm relies exclusively on equity financing and carries excess cash, but underinvests in R&D. We use mechanism design to examine how intermediated financing can attentuate this underinvestment. The mechanism combines equity with put options such that investors insure firms against R&D failure and firms insure investors against high R&D payoffs not being realized.

Opinion article by Andrew W. Lo and Kenneth Kosik on the potential role of California in the space of Alzheimer's Disease research.

In the past decade, financial industry excesses have been cited as the source of many ills afflicting economies and political systems in the West. But, if used responsibly, finance could help provide the cure for some of humanity’s most pressing problems – from cancer to fossil fuel depletion and climate change.

Few patient populations are as helpless and in need of advocacy as children with cancer. Pharmaceutical companies have historically faced significant financial disincentives to pursue pediatric oncology therapeutics, including low incidence, high costs of conducting pediatric trials, and a lack of funding for early-stage research. Review of published studies of pediatric oncology research and the cost of drug development, as well as clinical trials of pediatric oncology therapeutics at ClinicalTrials.gov, identified 77 potential drug development projects to be included in a hypothetical portfolio. The returns of this portfolio were simulated so as to compute the financial returns and risk. Simulated business strategies include combining projects at different clinical phases of development, obtaining partial funding from philanthropic grants, and obtaining government guarantees to reduce risk. The purely private-sector portfolio exhibited expected returns ranging from −24.2% to 10.2%, depending on the model variables assumed. This finding suggests significant financial disincentives for pursuing pediatric oncology therapeutics and implies that financial support from the public and philanthropic sectors is essential. Phase diversification increases the likelihood of a successful drug and yielded expected returns of −5.3% to 50.1%. Standard philanthropic grants had a marginal association with expected returns, and government guarantees had a greater association by reducing downside exposure. An assessment of a proposed venture philanthropy fund demonstrated stronger performance than the purely private-sector–funded portfolio or those with traditional amounts of philanthropic support. A combination of financial and business strategies has the potential to maximize expected return while eliminating some downside risk—in certain cases enabling expected returns as high as 50.1%—that can overcome current financial disincentives and accelerate the development of pediatric oncology therapeutics.

We apply Bayesian decision analysis (BDA) to incorporate patient preferences in the regulatory approval process for new therapies. By assigning weights to type I and type II errors based on patient preferences, the significance level (a) and power (1 b) of a randomized clinical trial (RCT) for a new therapy can be optimized to maximize the value to current and future patients and, consequently, to public health. We find that for weight-loss devices, potentially effective low-risk treatments have optimal as larger than the traditional one-sided significance level of 5%, whereas potentially less effective and riskier treatments have optimalas below 5%. Moreover,the optimal RCT design, including trial size, varies with the risk aversion and time-to-access preferences and the medical need of the target population.

Importance  Randomized clinical trials (RCTs) currently apply the same statistical threshold of alpha = 2.5% for controlling for false-positive results or type 1 error, regardless of the burden of disease or patient preferences. Is there an objective and systematic framework for designing RCTs that incorporates these considerations on a case-by-case basis?

Objective  To apply Bayesian decision analysis (BDA) to cancer therapeutics to choose an alpha and sample size that minimize the potential harm to current and future patients under both null and alternative hypotheses.

Data Sources  We used the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) database and data from the 10 clinical trials of the Alliance for Clinical Trials in Oncology.

Study Selection  The NCI SEER database was used because it is the most comprehensive cancer database in the United States. The Alliance trial data was used owing to the quality and breadth of data, and because of the expertise in these trials of one of us (D.J.S.).

Data Extraction and Synthesis  The NCI SEER and Alliance data have already been thoroughly vetted. Computations were replicated independently by 2 coauthors and reviewed by all coauthors.

Main Outcomes and Measures  Our prior hypothesis was that an alpha of 2.5% would not minimize the overall expected harm to current and future patients for the most deadly cancers, and that a less conservative alpha may be necessary. Our primary study outcomes involve measuring the potential harm to patients under both null and alternative hypotheses using NCI and Alliance data, and then computing BDA-optimal type 1 error rates and sample sizes for oncology RCTs.

Results  We computed BDA-optimal parameters for the 23 most common cancer sites using NCI data, and for the 10 Alliance clinical trials. For RCTs involving therapies for cancers with short survival times, no existing treatments, and low prevalence, the BDA-optimal type 1 error rates were much higher than the traditional 2.5%. For cancers with longer survival times, existing treatments, and high prevalence, the corresponding BDA-optimal error rates were much lower, in some cases even lower than 2.5%.

Conclusions and Relevance  Bayesian decision analysis is a systematic, objective, transparent, and repeatable process for deciding the outcomes of RCTs that explicitly incorporates burden of disease and patient preferences.

The high cost of capital for firms conducting medical research and development (R&D) has been partly attributed to the government risk facing investors in medical innovation. This risk slows down medical innovation because investors must be compensated for it. We propose new and simple financial instruments, Food and Drug Administration (FDA) hedges, to allow medical R&D investors to better share the pipeline risk associated with FDA approval with broader capital markets. Using historical FDA approval data, we discuss the pricing of FDA hedges and mechanisms under which they can be traded and estimate issuer returns from offering them. Using various unique data sources, we find that FDA approval risk has a low correlation across drug classes as well as with other assets and the overall market. We argue that this zero-beta property of scientific FDA risk could be a main source of gains from trade between issuers of FDA hedges looking for diversified investments and developers looking to offload the FDA approval risk. We offer proof of concept of the feasibility of trading this type of pipeline risk by examining related securities issued around mergers and acquisitions activity in the drug industry. Overall, our argument is that, by allowing better risk sharing between those investing in medical innovation and capital markets more generally, FDA hedges could ultimately spur medical innovation and improve the health of patients.

Recent cases of aggressive pricing behavior in the biopharmaceutical industry have raised serious concerns among payers and policymakers about industry ethics. However, these cases should not be confused with price increases motivated by challenging business conditions that ultimately lead to greater investment in R&D and improved patient access to therapeutics. We study the example of Questcor Pharmaceuticals, which was forced to choose between increasing the price of an effective drug in 2007 and ceasing production and shutting down. We consider Questcor’s journey from inception to its acquisition in 2014, analyze the factors leading up to the price hike of its main revenue generator, Acthar Gel, and discuss its resulting impact on patients after 2007. A counterfactual financial simulation of the company’s prospects in the case where prices were not increased shows that Questcor would have become insolvent between 2008 and 2010.

Uncertainty surrounding the risk and reward of investments in biopharmaceutical companies poses a challenge to those interested in funding such enterprises. Using data on publicly traded stocks, we track the performance of 1,066 biopharmaceutical companies from 1930 to 2015—the most comprehensive financial analysis of this sector to date. Our systematic exploration of methods for distinguishing biotech and pharmaceutical companies yields a dynamic, more accurate classification method. We find that the performance of the biotech sector is highly sensitive to the presence of a few outlier companies, and confirm that nearly all biotech companies are loss-making enterprises, exhibiting high stock volatility. In contrast, since 2000, pharmaceutical companies have become increasingly profitable, with risk-adjusted returns consistently outperforming the market. The performance of all biopharmaceutical companies is subject not only to factors arising from their drug pipelines (idiosyncratic risk), but also from general economic conditions (systematic risk). The risk associated with returns has profound implications both for patterns of investment and for funding innovation in biomedical R&D.

In this case study, we profile the I-SPY 2 TRIAL (Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And molecular anaLysis 2), a unique breast cancer clinical trial led by researchers at 20 leading cancer centers across the US, and examine its potential to serve as a model of drug development for other disease areas. This multicenter collaboration launched in 2010 to reengineer the drug development process to be more efficient and patient-centered. We observe that I-SPY 2 possesses several novel features that could be used as a template for more efficient and cost effective drug development, namely its adaptive trial design; precompetitive network of stakeholders; and flexible infrastructure to accommodate innovation.

Translating academic medical research into new therapies is an important challenge for the biopharmaceutical industry and investment communities, which have historically favored later-stage assets with lower risk and clearer commercial value. The Stanford SPARK program is an innovative model for addressing this challenge. The program was created in 2006 to educate students and faculty about bringing academic research from bench to bedside. Every year, the program provides mentorship and funding for approximately a dozen SPARK ‘scholars,’ with a focus on impacting patient lives, regardless of economic factors. By reviewing the detailed structure, function and operation of SPARK we hope to provide a template for other universities and institutions interested in de-risking and facilitating the translation of biomedical research.

There is general agreement in the biomedical community that the development of therapies for certain diseases should take priority. This ethic has motivated legislative initiatives, such as the Orphan Drug Act of 1983, and underpins several important innovations in regulatory approval processes, such as the US Food and Drug Administration’s (FDA) fast-track, breakthrough-therapy, accelerated-approval, and priority-review designations. However, none of these innovations directly address the critical issue of how to incorporate the patient’s perspective in deciding whether a drug candidate should be approved or not. The current approach in clinical trial design is to minimize the chance of ineffective treatment caused by a type 1 error, that is, a false-positive result. However, the arbitrary nature of the threshold for the probability of type 1 error, alpha, raises an ethical question about its justification. A 2.5% threshold may not be appropriate for terminal illnesses that have no effective therapies; such patients may prefer to take a bigger chance on a false-positive result, even if the likelihood of an effective therapy is small. To quote the noted biostatistician Donald Berry, “We should also focus on patient values, not just P values.”

The Food and Drug Administration (FDA) is a remarkable agency, one of the crown jewels of the US government. Its staff and structure are dedicated to safeguarding American public health, and although we sometimes complain about its role as gatekeeper, we all sleep better knowing that our foods and drugs have passed the FDA’s careful scrutiny. Its regulatory scope and process reflect the technical demands of its responsibilities, and the FDA is one of the very few federal agencies that have taken a lead in defining and developing the new field of regulatory science

In response to the senators January 21, 2016 request for comment on their Sovaldi report, February 27, 2016. On behalf of all patients and their family members and friends, thank you for conducting the study on the pricing strategy of Gilead Sciences and shining a spotlight on the issue of drug pricing. When access to life-saving therapies is limited by affordability, important moral and ethical issues must be considered in addition to economic and political ones. For too long, we in the United States have ignored these issues for fear of “death panels” and difficult end-of-life decisions. But the growing number of breakthrough therapies and the rising cost of healthcare will soon force us to confront these issues directly. Your report and is an important step in helping us to develop a rational, ethical approach to dealing with this looming challenge.