Comparative effectiveness research (CER) primarily involves estimation of causal effects of alternative treatments on outcomes. To this end, observational databases are a promising source of information on patient-level treatments and outcomes. However, observational data analyses are prone to selection biases or confounding by indication, which arise due to the differences in levels of observed and unobserved risk factors across patients receiving different treatments, and which complicate inference on causal effects of treatments. Instrumental variable (IV) methods are one of the most powerful methods that can address these challenges and help estimate causal effects from such data, yet these methods are underutilized for CER. The goal of this application is to increase the appropriate utilization of instrumental variables methods by overcoming three important barriers to adoption of these powerful methods for CER. Appropriate use of IV methods for CER hinges on selecting good instruments and appropriate estimation. A good instrument must 1) induce substantial variation in treatment choices (i.e. be """"""""strong"""""""") but 2) not affect outcomes except through treatment choices (i.e. the """"""""exclusion restriction""""""""). While the consequences of using weak instruments have been investigated, the consequences of violating the exclusion restriction are not well understood. Even under the traditional assumption of a homogenous treatment effect, several new IV approaches are being developed. Knowing which method is appropriate for any particular application remains challenging. The default has been to use two-stage least squares, but many situations common to CER require alternative approaches such as near-far matching or two-stage residual inclusion. This application aims to address these challenges with applying instrumental variables analysis with a goal of providing applied practitioners of CER with appropriate guidance. Results of IV analyses may be generalized to the wrong subpopulations if treatment effects are heterogeneous as these effects become dependent on the analyst's choice of IV(s) and are difficult to interpret for clinical and policy purposes. We will also develop novel IV approaches that address treatment effect heterogeneity and generate interpretable results for CER. Many current applications of CER do not take full advantage of recent IV methodological advances, due to unavailability of readily implementable software or statistical code, resulting in delays in the translation of the science of IV analysis to practice. Therefore, we will develop relevant statistical code to help practitioners implement these methods using common statistical software packages and illustrate the methods through empirical examples in prostate cancer and cardiovascular disease.
Comparative effectiveness research (CER) primarily involves estimation of causal effects of alternative treatments on outcomes. To this end, observational databases are a promising source of information on patient-level treatments and outcomes. However, observational data analyses are prone to selection biases or confounding by indication, which arise due to the differences in levels of observed and unobserved risk factors across patients receiving different treatments, and which complicate inference on causal effects of treatments. Instrumental variable (IV) methods are one of the most powerful methods that can address these challenges and help estimate causal effects from such data, yet these methods are underutilized for CER. The goal of this application is to increase the appropriate utilization of IV methods by overcoming three important barriers to adoption of these powerful methods for CER.
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