Adjustment for Unobserved Confounders in Health Administrative Databases
| Autor: | Silenou Chawo, Bernard, Avalos, Marta, Pariente, Antoine, Jacqmin-Gadda, Hélène |
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| Přispěvatelé: | Université de Bordeaux (UB), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Bordeaux [Bordeaux], CIC - Bordeaux, Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), ANSM, INTERNATIONAL SOCIETY FOR PHARMACOEPIDEMIOLOGY (ISPE), DRUGS-SAFE, Avalos, Marta |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
congenital
hereditary and neonatal diseases and abnormalities [STAT.AP]Statistics [stat]/Applications [stat.AP] [STAT.ME] Statistics [stat]/Methodology [stat.ME] Phamacoepidemiology [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] [STAT.CO] Statistics [stat]/Computation [stat.CO] [STAT.ML] Statistics [stat]/Machine Learning [stat.ML] Multivariate imputation by chained equations [STAT.AP] Statistics [stat]/Applications [stat.AP] EGB [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie Two stage calibration [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie Unmeasured confounding [STAT.CO]Statistics [stat]/Computation [stat.CO] [STAT.ME]Statistics [stat]/Methodology [stat.ME] |
| Zdroj: | 32nd International Conference on Pharmacoepidemiology & Therapeutic Risk Management 32nd International Conference on Pharmacoepidemiology & Therapeutic Risk Management, INTERNATIONAL SOCIETY FOR PHARMACOEPIDEMIOLOGY (ISPE), Aug 2016, Dublin, Ireland |
| Popis: | International audience; BackgroundIn 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.ObjectivesWe aim at comparing the performances of MICE and TSC in adjusting for UC in HAD using a cohort data in a simulation study.MethodsWe 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).ResultsWhen 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.ConclusionsOur 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. |
| Databáze: | OpenAIRE |
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