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NIH Funding Portfolio Evaluated With Investment Metrics

New York Stock Exchange entrance (A. Kotok)

(A. Kotok)

An interdisciplinary team of business, computer science, and medical researchers tested funding decisions at National Institutes of Health over a 42-year period using measures of investment efficiency from the world of finance. The findings of the team from Massachusetts Institute of Technology, Brigham and Women’s Hospital, and the investment management firm AlphaSimplex LLC in Cambridge, Massachusetts appear online in the journal PLoS ONE.

The study applied financial portfolio theory to NIH funding allocation decisions, where the the risk/reward trade-off of funding allocations can be optimized with respect to quantitative measures of burden of disease such as “years of life lost” (YLL). NIH expects to spend some $31 billion in the current fiscal year that began on 1 October 2011, and is the largest government funder of biomedical research.

The authors — Dimitrios Bisias of the MIT Operations Research Center, Andrew Lo of MIT’s Sloan School of Management and chairman of AlphaSimplex, and James Watkins, a surgeon at Brigham and Women’s Hospital — say that funding decisions by a public agency like NIH are similar to managing an investment portfolio. In both cases, decision makers need to consider competing choices for investment and limited resources to invest, forcing trade-offs.  The expected returns, risks, value of serendipity, and the cost of lost opportunities are also important factors in both types of decisions.

Using NIH funding allocation data for seven major disease groups between 1965 and 2005, and subsequent YLL changes within those disease groups between 1979 and 2007, the researchers estimated the optimal trade-off between the YLL-denominated risk and reward of various funding allocations or “portfolios”. This optimum trade-off, called the efficient frontier, corresponds to funding allocations that maximize the expected return on investment. In this study, return on investment is defined as subsequent expected improvements in YLL for a given level of risk, itself defined as volatility of subsequent improvements in YLL.

Bisias, Lo, and Watkins found that optimized funding allocations may yield more desirable public-health outcomes in terms of higher expected improvements and reduced risk in YLL. Their calculations show NIH’s current formulas perform better than simple benchmarks, such as equally-weighted allocations. Using the efficient-frontier method, however, can return decreases of 28 to 89 percent in YLL per unit of risk.

The researchers note that YLL is an imperfect measure of public health, with other factors possibly contributing to improvements in YLL. The authors also point to other limitations including the impact of statistical estimation error, the lack of clearly articulated policy objectives with respect to burden of disease, and the assumption that the return on investment in biomedical research can be extrapolated from past successes.

The results from this proof-of-concept study do suggest the presence of significant differences between disease areas in research productivity, risk, and spillover into other areas of research. “Portfolio theory may allow us to identify different funding allocations that can lead to greater progress in reducing burden of disease,” says Bisias, “even without any increase in research funding.”

The team’s findings, however, show that the level of NIH funding also matters, with significant benefits as measured by YLL improvements from increased funds for NIH research. “If policymakers truly understood the implications of our empirical results,” says Lo, “they would be pushing for greater appropriations for the NIH expeditiously.”

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