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%.

CONCLUSION 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.

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.

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.

Andrew Lo discusses the tension between statistical significance (p-values) and patient values in medical research. Based on his research, he proposes a Bayesian decision analysis framework for making regulatory decisions that reflects differences in both the impact of diseases and stakeholder perspectives in a systematic, objective, transparent and repeatable manner.

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.

Americans are increasingly apprehensive about our future, so it is inspiring when Congress produces legislation intended to both enhance our health and expand our economy. The 21st Century Cures Act, recently passed by the House with an impressive bipartisan majority vote of 344 to 77, intends to accelerate the many-step process of drug discovery and development, from basic scientific research to clinical development to delivery, distribution, and ongoing monitoring. Among other things, the legislation boosts National Institute of Health funding, dramatically speeds up the US Food and Drug Administration (FDA) approval process, and aims to make use of new information technology to better monitor the performance of medical products after they reach the market. This landmark bill now awaits a comparable piece of legislation being developed by the Senate Health Education, Labor, and Pensions Committee. Together, they will transform the biomedical ecosystem and provide the foundation for the next several decades of innovative life-saving and health-enhancing solutions for our nation and the world.

The reports by Wim van Harten and colleagues and Sabine Vogler and colleagues in The Lancet Oncology on the costs of cancer drugs in European countries deserve special attention from all oncology and biopharmaceutical stakeholders. van Harten identified that, in 15 European countries, list prices can be up to 92% lower than the highest reported, with actual prices paid up to 58% lower. These findings are backed up by Vogler and colleagues' study in 16 European countries, Australia, and New Zealand, which documented that highest-minus-lowest list price differences ranged from 28% to 388% for cancer drugs. Such variability argues strongly for greater transparency in drug pricing and the circumstances leading to such differences. But most importantly, it underscores the need to establish the true value of cancer therapies, and those who have championed this cause have been handed unequivocal evidence confirming what they have long suspected: drug prices are typically driven by what the market will bear.

Duchenne muscular dystrophy (DMD) is a rare genetic disorder affecting thousands of individuals, mainly young males, worldwide. Currently, the disease has no cure, and is fatal in all cases. Advances in our understanding of the disease and innovations in basic science have recently allowed biotechnology companies to pursue promising treatment candidates for the disease, but so far, only one drug with limited application has achieved FDA approval. In this case study, we profile the work of an early-stage life sciences company, Solid Biosciences, founded by a father of a young boy with DMD. In particular, we discuss Solid’s one-disease focus and its strategy to treat the disease with a diversified portfolio of approaches. The company is currently building a product pipeline consisting of genetic interventions, small molecules and biologics, and assistive devices, each aimed at addressing a different aspect of DMD. We highlight the potential for Solid’s business model and portfolio to achieve breakthrough treatments for the DMD patient community.

A crisis is building over the prices of new transformative therapies for cancer, hepatitis C virus infection, and rare diseases. The clinical imperative is to offer these therapies as broadly and rapidly as possible. We propose a practical way to increase drug affordability through health care loans (HCLs)—the equivalent of mortgages for large health care expenses. HCLs allow patients in both multipayer and single-payer markets to access a broader set of therapeutics, including expensive short-duration treatments that are curative. HCLs also link payment to clinical benefit and should help lower per-patient cost while incentivizing the development of transformative therapies rather than those that offer small incremental advances. Moreover, we propose the use of securitization—a well-known financial engineering method—to finance a large diversified pool of HCLs through both debt and equity. Numerical simulations suggest that securitization is viable for a wide range of economic environments and cost parameters, allowing a much broader patient population to access transformative therapies while also aligning the interests of patients, payers, and the pharmaceutical industry.

Opinion Editorial

"A new class of medications was recently approved that cures more than 95 percent of people with Hepatitis C in only six weeks at a cost of about $84,000 per person, and new therapies with price tags that are likely to exceed $1 million per person are now available or coming soon. How can patients possibly afford them?

"In an article published in the journal Science Translation Medicine, we outline a feasible market-based solution that could immediately expand access to transformative medications, including cures for Hepatitis C and cancer. The basic concept is to convert a large upfront medical expense into a series of more affordable payments, akin to getting a mortgage when buying a house. The challenge of curative medications that only require a short course of therapy is that the whole price is paid upfront — how many homeowners could buy their houses using only cash? Instead, most home buyers get a mortgage and make monthly payments for as long as they benefit from owning the house or until the full amount is paid. We propose the same solution to overcome the liquidity problem that prevents access to curative medications, which we call “health care loans,” or HCLs..."

We extend the megafund concept for funding drug discovery to enable dynamic leverage in which the portfolio of candidate therapeutic assets is predominantly financed initially by equity, and debt is introduced gradually as assets mature and begin generating cash flows. Leverage is adjusted so as to maintain an approximately constant level of default risk throughout the life of the fund. Numerical simulations show that applying dynamic leverage to a small portfolio of orphan drug candidates can boost the return on equity almost twofold compared with securitization with a static capital structure. Dynamic leverage can also add significant value to comparable all-equity-financed portfolios, enhancing the return on equity without jeopardizing debt performance or increasing risk to equity investors.

In this article, we suggest an alternative structure for undertaking the long-term, high-risk, highly capital-intensive R&D programs that typify science-based settings. We refer to this structure as a project-focused organization (PFO). PFOs are entities that are created with the sole purpose of conducting a specific R&D project. When the project is completed, the PFO is disbanded, residual returns (if there are any) are distributed to investors, and intellectual property and other assets are sold off. We think PFOs are an attractive alternative to both the traditional vertical integration model and the traditional venture capital/entrepreneurial startup model. We discuss how such PFOs could work in practice, using the example of biopharmaceutical R&D, although we argue that the structure has much broader applicability.