Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology
Abstract Background In pharmacoepidemiology, the prescription preference-based instrumental variables (IV) are often used with linear models to solve the endogeneity due to unobserved confounders even when the outcome and the endogenous treatment are dichotomous variables. Using this instrumental va...
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doaj-f438c8191cc749d4a60f461e3144d7f12020-11-25T02:22:47ZengBMCBMC Medical Research Methodology1471-22882018-06-0118111410.1186/s12874-018-0513-yInstrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiologyBabagnidé François Koladjo0Sylvie Escolano1Pascale Tubert-Bitter2Biostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases (B2PHI), Inserm, UVSQ, Institut Pasteur, Université Paris-SaclayBiostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases (B2PHI), Inserm, UVSQ, Institut Pasteur, Université Paris-SaclayBiostatistics, Biomathematics, Pharmacoepidemiology and Infectious Diseases (B2PHI), Inserm, UVSQ, Institut Pasteur, Université Paris-SaclayAbstract Background In pharmacoepidemiology, the prescription preference-based instrumental variables (IV) are often used with linear models to solve the endogeneity due to unobserved confounders even when the outcome and the endogenous treatment are dichotomous variables. Using this instrumental variable, we proceed by Monte-Carlo simulations to compare the IV-based generalized method of moment (IV-GMM) and the two-stage residual inclusion (2SRI) method in this context. Methods We established the formula allowing us to compute the instrument’s strength and the confounding level in the context of logistic regression models. We then varied the instrument’s strength and the confounding level to cover a large range of scenarios in the simulation study. We also explore two prescription preference-based instruments. Results We found that the 2SRI is less biased than the other methods and yields satisfactory confidence intervals. The proportion of previous patients of the same physician who were prescribed the treatment of interest displayed a good performance as a proxy of the physician’s preference instrument. Conclusions This work shows that when analysing real data with dichotomous outcome and exposure, appropriate 2SRI estimation could be used in presence of unmeasured confounding.http://link.springer.com/article/10.1186/s12874-018-0513-yInstrumental variableNonlinear least squaresLogistic regressionPhysician’s prescription preferencePharmacoepidemiologyObservational studies |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Babagnidé François Koladjo Sylvie Escolano Pascale Tubert-Bitter |
spellingShingle |
Babagnidé François Koladjo Sylvie Escolano Pascale Tubert-Bitter Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology BMC Medical Research Methodology Instrumental variable Nonlinear least squares Logistic regression Physician’s prescription preference Pharmacoepidemiology Observational studies |
author_facet |
Babagnidé François Koladjo Sylvie Escolano Pascale Tubert-Bitter |
author_sort |
Babagnidé François Koladjo |
title |
Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology |
title_short |
Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology |
title_full |
Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology |
title_fullStr |
Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology |
title_full_unstemmed |
Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology |
title_sort |
instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology |
publisher |
BMC |
series |
BMC Medical Research Methodology |
issn |
1471-2288 |
publishDate |
2018-06-01 |
description |
Abstract Background In pharmacoepidemiology, the prescription preference-based instrumental variables (IV) are often used with linear models to solve the endogeneity due to unobserved confounders even when the outcome and the endogenous treatment are dichotomous variables. Using this instrumental variable, we proceed by Monte-Carlo simulations to compare the IV-based generalized method of moment (IV-GMM) and the two-stage residual inclusion (2SRI) method in this context. Methods We established the formula allowing us to compute the instrument’s strength and the confounding level in the context of logistic regression models. We then varied the instrument’s strength and the confounding level to cover a large range of scenarios in the simulation study. We also explore two prescription preference-based instruments. Results We found that the 2SRI is less biased than the other methods and yields satisfactory confidence intervals. The proportion of previous patients of the same physician who were prescribed the treatment of interest displayed a good performance as a proxy of the physician’s preference instrument. Conclusions This work shows that when analysing real data with dichotomous outcome and exposure, appropriate 2SRI estimation could be used in presence of unmeasured confounding. |
topic |
Instrumental variable Nonlinear least squares Logistic regression Physician’s prescription preference Pharmacoepidemiology Observational studies |
url |
http://link.springer.com/article/10.1186/s12874-018-0513-y |
work_keys_str_mv |
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1724861741546864640 |