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

Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review. Streeter, A. J., Lin, N. X., Crathorne, L., Haasova, M., Hyde, C., Melzer, D., & Henley, W. E. Journal of Clinical Epidemiology, 87:23–34, July, 2017.

Paper doi abstract bibtex

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. Ó 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

@article{streeter_adjusting_2017-1,
	title = {Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review},
	volume = {87},
	issn = {08954356},
	shorttitle = {Adjusting for unmeasured confounding in nonrandomized longitudinal studies},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0895435616303341},
	doi = {10.1016/j.jclinepi.2017.04.022},
	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. Ó 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).},
	language = {en},
	urldate = {2019-05-01},
	journal = {Journal of Clinical Epidemiology},
	author = {Streeter, Adam J. and Lin, Nan Xuan and Crathorne, Louise and Haasova, Marcela and Hyde, Christopher and Melzer, David and Henley, William E.},
	month = jul,
	year = {2017},
	pages = {23--34},
	file = {Streeter et al. - 2017 - Adjusting for unmeasured confounding in nonrandomi.pdf:/Users/neil.hawkins/Zotero/storage/6C4IJ247/Streeter et al. - 2017 - Adjusting for unmeasured confounding in nonrandomi.pdf:application/pdf},
}

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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. Ó 2017 The Authors. Published by Elsevier Inc. 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