Analytical Methods for a Learning Health System: 3. Analysis of Observational Studies

The third paper in a series on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review describes how analytical methods for individual-level electronic health data EHD, including regression approaches, interrupted time series (ITS) analyses, instrumental variables, and propensity score methods, can also be used to address the question of whether the intervention “works.” The two major potential sources of bias in non-experimental studies of health care interventions are that the treatment groups compared do not have the same probability of treatment or exposure and the potential for confounding by unmeasured covariates. Although very different, the approaches presented in this chapter are all based on assumptions about data, causal relationships, and biases. For instance, regression approaches assume that the relationship between the treatment, outcome, and other variables is properly specified, all of the variables are available for analysis (i.e., no unobserved confounders) and measured without error, and that the error term is independent and identically distributed. The instrumental variables approach requires identifying an instrument that is related to the assignment of treatment but otherwise has no direct on the outcome. Propensity score methods approaches, on the other hand, assume that there are no unobserved confounders. The epidemiological designs discussed also make assumptions, for instance that individuals can serve as their own control. To properly address these assumptions, analysts should conduct sensitivity analyses within the assumptions of each method to assess the potential impact of what cannot be observed. Researchers also should analyze the same data with different analytical approaches that make alternative assumptions, and to apply the same methods to different data sets. Finally, different analytical methods, each subject to different biases, should be used in combination and together with different designs, to limit the potential for bias in the final results.