For the past 9 years, I have been a statistician in a Department of Health Care Policy—a department also composed of health economists, medical sociologists, physicians, and general health policy researchers. The mission of the department is to conduct and publish research that assists policy makers to improve the quality and financing of health care in the United States.
Because of the collaborative culture in my department, recognition of the value of considering and ultimately using methods developed in other disciplines is the norm, not the exception. In this environment, a statistician who is unfamiliar with instrumental variables, structural equation modeling, or the use of theoretical economic and sociological models to motivate and supplement empirical analysis quickly adds these to his/her repertoire. This contrasts with the scenario in the study-section depicted in Dowd's paper! Conversely, the economists, sociologists, and physician researchers in my department become acquainted with a wider range of statistical models, methods, and practices (e.g., hierarchical, mixed-membership, and multivariate models; Bayesian analysis; experimental design; and methods for evaluating the sensitivity of results to model assumptions) than they might have otherwise.
Because most of my collaborative research projects involve observational data, concerns about selection of patients (or health providers and health plans) into treatments are paramount. In formulating an analysis plan, consideration is given to all methods of analysis with the choice ultimately depending on what is most appropriate for the problem at hand. If a strong instrumental variable is available (i.e., one backed by strong theoretical arguments), this would suggest using an instrumental variables analysis. If inundated with predictor variables but lacking a good instrument, then propensity scores methods would become more attractive. If interaction(s) involving the treatment were thought to be present or there was interest in testing for such effects, then multiple regression analysis, possibly in the context of an instrumental variable or propensity score analysis, would be considered. If more than one of these approaches can be applied to a problem, we might perform each and compare the results in order to evaluate the sensitivity to results of the differing assumptions. If large differences are found, we try to provide explanations. This often involves further empirical analysis, perhaps using simulation experiments, as well as theoretical models based on the insights of social scientists and physicians, all of which can lead to a more enlightened analysis.
Often the randomized trial data we use have their own difficulties. For example, randomized clinical trials in mental health are notorious for treatment noncompliance and subsequent nonresponse. To overcome these problems, we have used principal stratification methods to move beyond intention-to-treat analysis (O'Malley and Normand 2005
). Surveys are another valuable source of data for health policy and health services research. An integral step in designing a survey is the removal of redundant items, while in designing reports based on survey data it is often desirable to combine items into meaningful scales. Latent variable models such as factor analysis (traditionally the domain of sociology and psychology) are often used to study the correlation structure of the survey items and in so doing help accomplish these objectives (O'Malley et al. 2005
Some problems we encounter present new challenges for causal inference. A prime example is social network analysis. Network data are characterized by interdependence among individuals. For example, the same individual may influence and be influenced by multiple other individuals. Thus, the data embody complex dependence structures that provide serious difficulties for statistical inference. For example, if individual A influences individual B and individual C influences individual A, then A's treatment (including influence from C) may affect B's outcome. This violates the stable unit treatment value assumption (SUTVA), a condition for identifying causal effects (Rubin 1980
). Sobel (2006)
and Hudgens and Halloran (2008)
investigate identifying causal effects when SUTVA is violated, but to my knowledge this has not been addressed generally in the context of social network data.