B4-4: A Novel Technique for Analysis of Uncontrolled Confounding in Non-experimental Comparative Effectiveness Research

摘要

Background/AimsComparative effectiveness research (CER) investigates the effects of treatments and practices, thus requires causal inference. Routine data such as billing, pharmacy or EHR, while often incomplete on important confounding variables, are the usual sources of information for nonexperimental CER. The lack of randomization introduces important considerations regarding uncontrolled confounding, especially in large datasets, which potentially magnify systematic error. Yet, quantitative bias analysis in CER is not common practice. In this paper we formalize and demonstrate easy-to-implement record-level simulation techniques for analysis of uncontrolled confounding in cancer treatment CER.