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Can the Learning Health Care System Be Educated With Observational Data?

Issa J. Dahabreh, MD, MS1,2; David M. Kent, MD, MS3
[+] Author Affiliations
1Center for Evidence-Based Medicine, School of Public Health, Brown University, Providence, Rhode Island
2Department of Health Services, Policy, and Practice, School of Public Health, Brown University, Providence, Rhode Island
3Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts
JAMA. 2014;312(2):129-130. doi:10.1001/jama.2014.4364.
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Given the complexity of medical decision making and the myriad questions that arise during the care of individuals, an expectation that every causal question be addressed with a randomized clinical trial (RCT) is not realistic. Nevertheless, large administrative databases linked with electronic health records, coupled with new statistical methods for extracting causal information from raw data, can complement clinical trial evidence, enabling a “learning health care system.” Yet despite continued advances in epidemiological and statistical methods and the advent of “big data,”1 there is concern that inferences from observational data can lead to poor health care decisions by misrepresenting association for causation.

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Figure.
Comparison of Propensity Score Analyses and RCT Results From 3 Recent Empirical Assessments35

Scatter plots of results from empirical comparisons of propensity score analyses (y-axis) and corresponding randomized clinical trial (RCT) results (x-axis). Markers denote comparisons between observational and randomized study estimates for the same research question (similar populations, interventions, and outcomes); statistically significant differences (P < .05) are shown in orange. The dotted lines indicate lack of effect in RCTs (vertical lines) and observational studies (horizontal lines). Values lower than 1 indicate that the new treatment evaluated in the trial was more effective than the more established treatment; observational study results are expressed in the same way as the corresponding trial results. Markers in the top-right and bottom-left quadrants in each panel indicate agreement between randomized and observational results with respect to the direction of effects. Markers in the top-left and bottom-right quadrants indicate discordant direction of effects between designs. Black dashed diagonal lines indicate the line of identity (perfect agreement) between RCT and observational study results; gray dashed lines demarcate observational study relative risks that are between 0.67 and 1.5 times those produced by the corresponding RCT results. The term “relative risk” is used to denote risk, odds, or hazard ratio estimates, as reported in the 3 empirical analyses contributing data to this figure.

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