| Saturday, February 17 | |
| CS24 Causal Inference |
Sat, Feb 17, 11:00 AM - 12:30 PM
Salon E |
A Decision Tool for Causal Inference and Observational Data Analysis Methods in Comparative Effectiveness Research (DECODE CER) (303600)(Joyce) Chung-Chou Chang, University of Pittsburgh*Douglas Landsittel, University of Pittsburgh Sally Morton, Virginia Tech Keywords: causal inference, comparative effectiveness research Comparative effectiveness research can be thought of as evaluating which treatment works best for whom and under what circumstances. While randomized controlled trials (RCTs) are still usually considered the gold standard for evaluating comparative effectiveness of treatments or interventions, RCTs are often not generalize to the total population of interest, and may not be possible in some circumstances. Therefore, observational studies have become increasingly popular for CER; however, these studies must be carefully designed and analyzed in a way that controls for self-selection bias, where patients and/or physicians select who receives which treatment. This talk describes the Decision Tool for Causal Inference and Observational Data Analysis Methods in Comparative Effectiveness Research (DECODE CER) that we developed with funding from the Patient-Centered Outcomes Research Institute. DECODE CER presents a framework for guiding investigators through the steps of asking the question, assessing data adequacy, understanding assumptions, and conducting propensity score-based and instrumental variable analyses.
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