Journal of Clinical Epidemiology

Available online 28 April 2017

Review

Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review

Adam J.Streeter^a^b Nan XuanLin^a^c LouiseCrathorne^d MarcelaHaasova^e ChristopherHyde^f DavidMelzer^g William E.Henley^a

^a: Health Statistics Group, Institute of Health Research, University of Exeter Medical School, University of Exeter, St. Luke's Campus, Exeter EX1 2LU, United Kingdom

^b: 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

^c: Mathematics, Physics & Electrical Engineering, Northumbria University, Sutherland Building, Newcastle upon Tyne NE1 8ST, United Kingdom

^d: Health Economics, Institute of Health Research, University of Exeter Medical School, University of Exeter, St. Luke's Campus, Exeter EX1 2LU, United Kingdom

^e: Evidence Synthesis & Modelling for Health Improvement, Institute of Health Research, University of Exeter Medical School, University of Exeter, St. Luke's Campus, Exeter EX1 2LU, United Kingdom

^f: Peninsula Technology Assessment Group, Institute of Health Research, University of Exeter Medical School, University of Exeter, St. Luke's Campus, Exeter EX1 2LU, United Kingdom

^g: Epidemiology & Public Health, RD&E Hospital Wonford, University of Exeter Medical School, RILD Building, Barrack Road, Exeter EX2 5DW, United Kingdom

https://doi.org/10.1016/j.jclinepi.2017.04.022 Get rights and content

Open Access funded by Medical Research Council

Under a Creative Commons license

open access

Abstract

Objectives

Motivated by recent calls to use electronic health records for research, we reviewed the application and development of methods for addressing the bias from unmeasured confounding in longitudinal data.

Study Design and Setting

Methodological review of existing literature. We searched MEDLINE and EMBASE for articles addressing the threat to causal inference from unmeasured confounding in nonrandomized longitudinal health data through quasi-experimental analysis.

Results

Among the 121 studies included for review, 84 used instrumental variable analysis (IVA), of which 36 used lagged or historical instruments. Difference-in-differences (DiD) and fixed effects (FE) models were found in 29 studies. Five of these combined IVA with DiD or FE to try to mitigate for time-dependent confounding. Other less frequently used methods included prior event rate ratio adjustment, regression discontinuity nested within pre-post studies, propensity score calibration, perturbation analysis, and negative control outcomes.

Conclusion

Well-established econometric methods such as DiD and IVA are commonly used to address unmeasured confounding in nonrandomized longitudinal studies, but researchers often fail to take full advantage of available longitudinal information. A range of promising new methods have been developed, but further studies are needed to understand their relative performance in different contexts before they can be recommended for widespread use.

Keywords

Method review

Unmeasured confounding

Unobserved confounding

Longitudinal

Observational data

Electronic health records

Funding: This research was funded by the Medical Research Council (grant number G0902158) with additional support from the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for the South West Peninsula and the National Institute for Health Research (NIHR) School for Public Health Research Aging Well programme. The School for Public Health Research (SPHR) is funded by the National Institute for Health Research (NIHR). SPHR is a partnership between the Universities of Sheffield, Bristol, Cambridge, UCL; the London School for Hygiene and Tropical Medicine; the University of Exeter Medical School; the LiLaC collaboration between the Universities of Liverpool and Lancaster and Fuse; the Center for Translational Research in Public Health, a collaboration between Newcastle, Durham, Northumbria, Sunderland, and Teesside Universities. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.