Exploring Objective Causal Inference in Case-Noncase Studies under the Rubin Causal Model

Case-noncase studies, also known as case-control studies, are ubiquitous in epidemiology, where a common goal is to estimate the effect of an exposure on an outcome of interest. In many areas of application, such as policy-informing drug utilization research, this effect is inherently causal. Although logistic regression, the predominant method for analysis of case-noncase data, and other traditional methodologies, may provide associative insights, they are generally inappropriate for causal conclusions. As such, they fail to address the very essence of many epidemiological investigations that employ them. In addition, these methodologies do not allow for outcome-free design (Rubin, 2007) of case-noncase data, which compromises the objectivity of resulting inferences. This thesis is directed at exploring what can be done to preserve objectivity in the causal analysis of case-noncase study data. It is structured as follows. In Chapter 1 we introduce a formal framework for studying causal effects from case-noncase data, which builds upon the well-established Rubin Causal Model for prospective studies. In Chapter 2 we propose a two-party, three-step methodology — PrepDA — for objective causal inference with case-noncase data. We illustrate the application of our methodology in a simple non-trivial setting. Its operating characteristics are investigated via simulation, and compared to those of logistic and probit regression. Chapter 3 focuses on the re-analysis of a subset of data from a published article, Karkouti et al. (2006). We investigate whether PrepDA and logistic regression, when applied to case-noncase data, can generate estimates that are concordant with those from the causal analysis of prospectively collected data. We introduce tools for covariate balance assessment across multiple imputed datasets. We explore the potential for analyst bias with logistic regression, when said method is used to analyze case-noncase data. In Chapter 4 we discuss our technology’s advantages over, and drawbacks as compared to, traditional approaches.Statistic