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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review

Adam J Streeter1, Nan Xuan Lin2, Louise Crathorne3

  • 1Health Statistics Group, Institute of Health Research, University of Exeter Medical School, University of Exeter, St. Luke's Campus, Exeter EX1 2LU, United Kingdom; Medical Statistics, Institute of Translational and Stratified Medicine, Plymouth University Peninsula School of Medicine & Dentistry, University of Plymouth, Plymouth Science Park, Derriford, Plymouth PL6 8BX, United Kingdom.

Journal of Clinical Epidemiology
|May 3, 2017
PubMed
Summary

Researchers reviewed methods for unmeasured confounding in longitudinal data using electronic health records. Established techniques like instrumental variable analysis (IVA) and difference-in-differences (DiD) are common, but new methods show promise.

Keywords:
Electronic health recordsLongitudinalMethod reviewObservational dataUnmeasured confoundingUnobserved confounding

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