ArticleHealth services research2016
Treatment Effect Estimation Using Nonlinear Two-Stage Instrumental Variable Estimators: Another Cautionary Note.
Article in Health services research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity.BMC medical research methodology · 2024Article
- How does a local instrumental variable method perform across settings with instruments of differing strengths? A simulation study and an evaluation of emergency surgery.Health economics · 2023Article
- Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables.BMC medical research methodology · 2023Review
- Dealing with confounding in observational studies: A scoping review of methods evaluated in simulation studies with single-point exposure.Statistics in medicine · 2023Article
- Local Instrumental Variable Methods to Address Confounding and Heterogeneity when Using Electronic Health Records: An Application to Emergency Surgery.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Assessing the ability of an instrumental variable causal forest algorithm to personalize treatment evidence using observational data: the case of early surgery for shoulder fracture.BMC medical research methodology · 2022Article
- Private equity ownership and nursing home quality: an instrumental variables approach.International journal of health economics and management · 2019Article
- Do timely mental health services reduce re-incarceration among prison releasees with severe mental illness?Health services research · 2019Article
- Instrumental variable analysis in the context of dichotomous outcome and exposure with a numerical experiment in pharmacoepidemiology.BMC medical research methodology · 2018Article
- Understanding Treatment Effect Estimates When Treatment Effects Are Heterogeneous for More Than One Outcome.Applied health economics and health policy · 2018Article
- 2SLS versus 2SRI: Appropriate methods for rare outcomes and/or rare exposures.Health economics · 2018Article
- Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers for Geriatric Ischemic Stroke Patients: Are the Rates Right?Journal of the American Heart Association · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectiveTo examine the settings of simulation evidence supporting use of nonlinear two-stage residual inclusion (2SRI) instrumental variable (IV) methods for estimating average treatment effects (ATE) using observational data and investigate potential bias of 2SRI across alternative scenarios of essential heterogeneity and uniqueness of marginal patients. STUDY
designPotential bias of linear and nonlinear IV methods for ATE and local average treatment effects (LATE) is assessed using simulation models with a binary outcome and binary endogenous treatment across settings varying by the relationship between treatment effectiveness and treatment choice. PRINCIPAL
findingsResults show that nonlinear 2SRI models produce estimates of ATE and LATE that are substantially biased when the relationships between treatment and outcome for marginal patients are unique from relationships for the full population. Bias of linear IV estimates for LATE was low across all scenarios.
conclusionsResearchers are increasingly opting for nonlinear 2SRI to estimate treatment effects in models with binary and otherwise inherently nonlinear dependent variables, believing that it produces generally unbiased and consistent estimates. This research shows that positive properties of nonlinear 2SRI rely on assumptions about the relationships between treatment effect heterogeneity and choice.
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Registered trials
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