TY - JOUR
T1 - Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review
AU - Streeter, AJ
AU - Lin, NX
AU - Crathorne, L
AU - Haasova, M
AU - Hyde, C
AU - Melzer, D
AU - Henley, WE
PY - 2017/4/28
Y1 - 2017/4/28
N2 - OBJECTIVE: 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. DESIGN: Methodological review of existing literature SETTING: We searched MEDLINE and EMBASE for articles addressing the threat to causal inference from unmeasured confounding in nonrandomised 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. CONCLUSIONS: Well-established econometric methods such as DiD and IVA are commonly used to address unmeasured confounding in non-randomised, 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.
AB - OBJECTIVE: 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. DESIGN: Methodological review of existing literature SETTING: We searched MEDLINE and EMBASE for articles addressing the threat to causal inference from unmeasured confounding in nonrandomised 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. CONCLUSIONS: Well-established econometric methods such as DiD and IVA are commonly used to address unmeasured confounding in non-randomised, 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.
KW - electronic health records
KW - longitudinal
KW - method review
KW - observational data
KW - unmeasured confounding
KW - unobserved confounding
UR - https://pearl.plymouth.ac.uk/context/pms-research/article/1109/viewcontent/1_s2.0_S0895435616303341_main.pdf
U2 - 10.1016/j.jclinepi.2017.04.022
DO - 10.1016/j.jclinepi.2017.04.022
M3 - Article
SN - 0895-4356
VL - 0
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
IS - 0
ER -