Instrumental Variable Analysis in Epidemiologic Studies: An Overview of the Estimation Methods

Uddin, Jamal; Groenwold, Rolf H H; Boer, Ton de; Sv, Belitser; Kc, Roes; Oh, Klungel

doi:10.4172/2153-2435.1000353

Cited by 12 publications

(11 citation statements)

References 82 publications

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“…There is now an extensive statistical literature on various causal inference methods (see: Imbens & Rubin, 2015;Morgan & Winship, 2015;Pearl, 2009;Pearl, Glymour, & Jewell, 2016). Several extensions of the method of instrumental variables have been described that rely on different estimation approaches (Uddin et al, 2015). The structural mean model approach, developed specifically in the context of attempting to address randomized experiments that have been compromised by attrition or lack of compliance, uses a semiparametric method of estimation called G estimation (Robins, 1994).…”

mentioning

confidence: 99%

Instrumental variable analyses for causal inference: Application to multilevel analyses of the alliance–outcome relation

Crits‐Christoph

Gallop

Gaines

et al. 2018

Psychotherapy Research

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Objective.-To introduce readers to instrumental variable analyses for causal inferences using as an example a test of the hypothesis that the quality of the therapeutic alliance has a causal role in relation to the outcome of psychotherapy.Method.-We used data from a recent non-inferiority trial of cognitive and dynamic therapies for major depressive disorder in a community mental health setting. The data (N = 161) were analyzed using standard approaches as well as a multilevel 2-stage instrumental variables approach that allows for causal interpretations by removing the influence of unmeasured confounds.Results.-Instrumental variables were created at the patient and therapist level using baseline patient and therapist variables. These baseline variables predicted the alliance but were otherwise unrelated to treatment outcome other than through their effects on the alliance. Standard multilevel mixed effects analyses revealed statistically significant associations of the alliance with outcome at the therapist level of analysis. The therapist level effect remained statistically significant when using the instrumental variables approach.Conclusion.-Our results support the hypothesis that, at least at the therapist level, the alliance plays a causal role in producing better outcomes. Instrumental variable analyses can be a useful tool to supplement standard analyses.

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confidence: 99%

Instrumental variable analyses for causal inference: Application to multilevel analyses of the alliance–outcome relation

Crits‐Christoph

Gallop

Gaines

et al. 2018

Psychotherapy Research

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“…IVA was conducted in several steps. 31,38 First, an instrumental variable that measures parental preference for care was identified, the number of providers seen during a hospice episode (see Figure 1). The hypothesis was that the preference for the number of providers seen during hospice enrollment affects the choice of model of hospice care, concurrent versus standard, but does not affect the duration of hospice stay.…”

Section: Analytical Approachmentioning

confidence: 99%

Application of Instrumental Variable Analysis in Pediatric End-of-Life Research: A Case Study

et al. 2023

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Instrumental variable analysis (IVA) has been widely used in many fields, including health care, to determine the comparative effectiveness of a treatment, intervention, or policy. However, its application in pediatric end-of-life care research has been limited. This article provides a brief overview of IVA and its assumptions. It illustrates the use of IVA by investigating the comparative effectiveness of concurrent versus standard hospice care for reducing 1-day hospice enrollments. Concurrent hospice care is a relatively recent type of care enabled by the Affordable Care Act in 2010 for children enrolled in the Medicaid program and allows for receiving life-prolonging medical treatment concurrently with hospice care. The IVA was conducted using observational data from 18,152 pediatric patients enrolled in hospice between 2011 and 2013. The results indicated that enrollment in concurrent hospice care reduced 1-day enrollment by 19.3%.

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“…We performed 2SLS estimation using the linear probability model, where the exposure effect is on the risk difference scale [35]. Robust standard errors were calculated to account for assumptions about homogeneous exposure effects and the outcome distributions.…”

Section: Applied Examplementioning

confidence: 99%

“…Although selection bias is understood in the methodological literature (e.g., [6,12,5]), it is seldom acknowledged in IV analyses or discussed in guidelines for IV analysis (e.g., [13,14,15,16,17]). However, recent exceptions include examples where selection depends on the: exposure plus confounder, or outcome [18], exposure [19,18], instrument plus measured and unmeasured confounders (of the outcome-exposure association) [20], exposure and measured variable (which causes the outcome) [21], missing values of measured covariates [22,23], and unmeasured confounder plus measured covariates [24].…”

Section: Introductionmentioning

confidence: 99%

Selection bias in instrumental variable analyses

Davies

Smith

et al. 2017

Preprint

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AbstractParticipants in epidemiological and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). The circumstances in which selection causes bias in an instrumental variable (IV) analysis are not widely understood by practitioners of IV analyses. We use directed acyclic graphs (DAGs) to depict assumptions about the selection mechanism (factors affecting selection) and show how DAGs can be used to determine when a two-stage least squares (2SLS) IV analysis is biased by different selection mechanisms. Via simulations, we show that selection can result in a biased IV estimate with substantial confidence interval undercoverage, and the level of bias can differ between instrument strengths, a linear and nonlinear exposure-instrument association, and a causal and noncausal exposure effect. We present an application from the UK Biobank study, which is known to be a selected sample of the general population. Of interest was the causal effect of education on the decision to smoke. The 2SLS exposure estimates were very different between the IV analysis ignoring selection and the IV analysis which adjusted for selection (e.g., 1.8 [95% confidence interval −1.5, 5.0] and −4.5 [−6.6, −2.4], respectively). We conclude that selection bias can have a major effect on an IV analysis and that statistical methods for estimating causal effects using data from nonrandom samples are needed.

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Instrumental Variable Analysis in Epidemiologic Studies: An Overview of the Estimation Methods

Cited by 12 publications

References 82 publications

Instrumental variable analyses for causal inference: Application to multilevel analyses of the alliance–outcome relation

Instrumental variable analyses for causal inference: Application to multilevel analyses of the alliance–outcome relation

Application of Instrumental Variable Analysis in Pediatric End-of-Life Research: A Case Study

Selection bias in instrumental variable analyses

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