In this chapter we elucidate four main themes. The first is that modern data analyses, including “big data” analyses, often rely on data from different sources, which can present challenges in constructing statistical models that can make effective use of all of the data. The second theme is that, although data analysis is usually centralized, frequently the final outcome is to provide information or allow decision making for individuals. Third, data analyses often have multiple uses by design: the outcomes of the analysis are intended to be used by more than one person or group, for more than one purpose. Finally, issues of privacy and confidentiality can cause problems in more subtle ways than are usually considered; we illustrate this point by discussing a case in which there is substantial and effective political opposition to simply acknowledging the geographic distribution of a health hazard.
A researcher analyzes some data and learns something important. What happens next? What does it take for the results to make a difference in people's lives? In this chapter we tell a story – a true story – about a statistical analysis that should have changed government policy, but did not. The project was a research success that did not make its way into policy, and we think it provides some useful insights into the interplay between locally collected data, statistical analysis, and individual decision making.
A DATA SET COMPILED FROM MANY LOCAL SOURCES
Before getting to our story we set the stage with a brief discussion of general issues regarding data availability. Some data analysis problems, even large or complicated ones, involve data from a single source or collected through a single mechanism. For example, the U.S. Census generates data on hundreds of millions of people using just a few different survey instruments. More typically, however, an analyses involves data from multiple sources.Moreover, although the input datamight come frommany sources and involve thousands or millions of people, at least some of the results of the analysis are often geared toward individuals. Some examples include the following:
• In evidence-based medicine (e.g., Lau, Ioannidis, and Schmid, 1997), information from many separate experiments and observational studies are combined in a meta-analysis, with the goal being to produce recommendations that can be adapted to individual patients by doctors or regulatory boards. […]