A Policy for Science

Policy and science often interact. Typically, we think of policymakers looking to scientists for advice on issues informed by science. We may appreciate less the opposite look: where people outside science inform policies that affect the conduct of science. In clinical medicine, we are forced to make decisions about practices for which there is insufficient, inadequate evidence to know whether they improve clinical outcomes, yet the health care system may not be structured to rapidly generate needed evidence. For example, when the Centers for Medicare and Medicaid Services noted insufficient evidence to support routine use of computed tomography angiography and they called for a national commitment to completion of randomized trials, their call ran into substantial opposition. I use the computed tomography angiography story to illustrate how we might consider a “policy for science” in which stakeholders would band together to identify evidence gaps and to use their influence to promote the efficient design, implementation, and completion of high-quality randomized trials. Such a policy for science could create a culture that incentivizes and invigorates the rapid generation of evidence, ultimately engaging all clinicians, all patients, and indeed all stakeholders into the scientific enterprise

Rigorous science as the road to better public health

In the current issue of Population Health Metrics, two reports paint a bleak picture of American public health. Both physical inactivity and obesity remain highly prevalent; yet, it is not clear that increased physical activity will reduce the burden of obesity. There continue to be widespread disparities in life expectancy across United States counties. These reports appear against a backdrop of debate regarding how we should allocate our scarce resources for improving health: should we focus more on improving access to high-quality medical care, or should we instead focus on more and better public health interventions? While optimal solutions remain obscure, a look at prior successes suggests that ultimately they will come from the conduct and implementation of rigorous science, and in particular event-driven trials

Random survival forests

We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortality that can be used as a predicted outcome. Several illustrative examples are given, including a case study of the prognostic implications of body mass for individuals with coronary artery disease. Computations for all examples were implemented using the freely available R-software package, randomSurvivalForest.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS169 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org