ArticleBMC medical research methodology2018
Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology.
Article in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A randomised, double blind, placebo-controlled trial of a two-week course of dexamethasone for adult patients with a symptomatic Chronic Subdural Haematoma (Dex-CSDH trial).Health technology assessment (Winchester, England) · 2024Trial
- Comparison of two-stage methods for count data in Mendelian randomization: a simulation study.Journal of comparative effectiveness research · 2026Article
- Preoperative rehabilitation and in-hospital mortality in delayed hip fracture surgery: a nationwide cohort study with stratification by kidney function.BMC geriatrics · 2025Article
- Observational birth cohorts for causal and predictive inference: The example of childhood asthma and allergic diseases.The Journal of allergy and clinical immunology · 2025Review
- Comparing the performance of two-stage residual inclusion methods when using physician's prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility.Journal of comparative effectiveness research · 2024Article
- Dealing with confounding in observational studies: A scoping review of methods evaluated in simulation studies with single-point exposure.Statistics in medicine · 2023Article
- A methodological framework for tackling confounding by indication when assessing the treatment effects of Chinese herbal injections in the real world.Journal of evidence-based medicine · 2022Article
- Association of ICU Admission and Outcomes in Sepsis and Acute Respiratory Failure.American journal of respiratory and critical care medicine · 2022Article
- Propensity Score and Instrumental Variable Techniques in Observational Transplantation Studies: An Overview and Worked Example Relating to Pre-Transplant Cardiac Screening.Transplant international : official journal of the European Society for Organ Transplantation · 2022Article
- Quantifying risk of injury from usual alcohol consumption: An instrumental variable analysis.Alcoholism, clinical and experimental research · 2021Article
- The effect of malaria on stunting: an instrumental variables approach.Transactions of the Royal Society of Tropical Medicine and Hygiene · 2021Article
- Modelling hospital outcome: problems with endogeneity.BMC medical research methodology · 2021Article
- Association between neuraxial anaesthesia or general anaesthesia for lower limb revascularisation surgery in adults and clinical outcomes: population based comparative effectiveness study.BMJ (Clinical research ed.) · 2020Article
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3 authors.
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Abstract
backgroundIn 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.
methodsWe 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.
resultsWe 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.
conclusionsThis work shows that when analysing real data with dichotomous outcome and exposure, appropriate 2SRI estimation could be used in presence of unmeasured confounding.
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