Adjustment for Unobserved Confounders in Health Administrative Databases

Abstract : Background In health administrative databases (HAD) information on potential confounders such as tobacco and alcohol consumption are missing. Often, this information is readily available in a cohort data. Multivariate imputation by chained equations (MICE) and Two stage calibration (TSC) may be used to adjust for unobserved confounders (UC) in HAD using cohort data. Objectives We aim at comparing the performances of MICE and TSC in adjusting for UC in HAD using a cohort data in a simulation study. Methods We generated a HAD with 10000 observations, a binary exposure, binary response and two observed confounders (OC). Likewise a cohort data with 1000 observations and additional two UC. The design exploited various distribution of OC and UC, strength of confounding effect, misspecification of propensity score model and lack of representativeness of the cohort data to HAD. MICE was applied by imputing the UC or propensity scores while TSC was applied with or without spline. Comparison was based on Bias, coverage rate of the confidence interval and mean square (MSE). Results When the cohort data is a representative sample with Gaussian confounders and a well-defined propensity score model assumed; both methods gives no bias, nominal coverage rate with smallest variance from TSC. Similar results were got in a misspecified propensity score (MPS) setting with smaller coverage rate for TSC. In addition, with strong confounding effect of UC and nonstandard distributions assumed, the coverage rate of TSC may slightly decrease in a MPS setting, but this is ameliorated by TSC with spline. Moreover, under lack of representativeness of the cohort sample, both methods are bias with low coverage rates. Conclusions Our results justify that when a well specified Propensity score model is assumed, TSC and MICE gives better and equivalent results but in a misspecified setting, the coverage of TSC is poorer than that of MICE although the bias and standard errors might still be small. These methods will hereafter be used to study the association between benzodiazepine consumption and fracture in the French HAD by utilising information on UC from a cohort study.
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https://hal.inria.fr/hal-01396349
Contributeur : Marta Avalos <>
Soumis le : lundi 14 novembre 2016 - 12:56:40
Dernière modification le : vendredi 1 septembre 2017 - 11:12:00

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  • HAL Id : hal-01396349, version 1

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Bernard Silenou Chawo, Marta Avalos, Antoine Pariente, Hélène Jacqmin-Gadda. Adjustment for Unobserved Confounders in Health Administrative Databases. 32nd International Conference on Pharmacoepidemiology & Therapeutic Risk Management, Aug 2016, Dublin, Ireland. 2016, 〈https://www.pharmacoepi.org/meetings/32ICPE/〉. 〈hal-01396349〉

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