Platforms and Integration in Statistical Research (Part 2/2)

Roger Peng

In my last post, I talked about two general approaches to conducting statistical research: platforms and integration. In this followup I thought I’d describe the characteristics of certain fields that suggesting taking one approach over another.

I think in practice, most statisticians will dedicate some time to both the platform and integrative approaches to doing statistical research because different approaches work better in different situations. The question then is not “Which approach is better?” but rather “What characteristics of a field suggest one should take a platform / integrative approach in order to have the greatest impact?” I think one way to answer this question is to make an analogy with transaction costs a la the theory of the firm. (This kind of analogy also plays a role in determining who best to collaborate with but that’s a different post).

In the context of an academic community, I think if it’s easy to exchange information, for example, about data, then building platforms that are widely used makes sense. For example, if everyone uses a standardized technology for collecting a certain kind of data, then it’s easy to develop a platform that applies some method to that data. Regression methodology works in any field that can organize their data into a rectangular table. On the other hand, if information exchange is limited, then building platforms is more difficult and closer collaboration may be required with individual investigators. For example, if there is no standard data collection method or if everyone uses a different proprietary format, then it’s difficult to build a platform that generalizes to many different areas.

There are two case studies with which I am somewhat familiar that I think are useful for demonstrating these characteristics.

In the end I think areas that are ripe for the platform approach to statistical research are those that are very open and have culture of information sharing, have a large community of active methodologists, and have a lot of useful publicly available data. Areas that do not have these qualities might be better served by an integrative approach where statisticians work more directly with scientific collaborators and focus on the specific questions and problems of a given study.