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

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  • Michael Stoto
  • Michael Oakes
  • Elizabeth Stuart
  • Randall Brown
  • Jelena Zurovac
  • Elisa L. Priest

Abstract

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.â€

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  • Michael Stoto & Michael Oakes & Elizabeth Stuart & Randall Brown & Jelena Zurovac & Elisa L. Priest, "undated". "Analytical Methods for a Learning Health System: 3. Analysis of Observational Studies," Mathematica Policy Research Reports 12dadeb3b9cb4aa8a422128ef, Mathematica Policy Research.
    • Handle: RePEc:mpr:mprres:12dadeb3b9cb4aa8a422128efa5f2c3c
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    File URL: https://egems.journal.ubiquity.website//article/10.5334/egems.252/
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    References listed on IDEAS

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    1. Peikes, Deborah N. & Moreno, Lorenzo & Orzol, Sean Michael, 2008. "Propensity Score Matching: A Note of Caution for Evaluators of Social Programs," The American Statistician, American Statistical Association, vol. 62, pages 222-231, August.
    2. Thomas D. Cook & William R. Shadish & Vivian C. Wong, 2008. "Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 724-750.
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    1. Michael Stoto & Michael Oakes & Elizabeth Stuart & Lucy Savitz & Elisa L. Priest & Jelena Zurovac, "undated". "Analytical Methods for a Learning Health System: 1. Framing the Research Question," Mathematica Policy Research Reports 83067603bf87438494078f75b, Mathematica Policy Research.

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