On the definition of a confounder

VanderWeele, Tyler J.; Shpitser, Ilya

doi:10.1214/12-aos1058

Cited by 205 publications

(148 citation statements)

References 39 publications

(70 reference statements)

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“…Several strategies have been discussed elsewhere 16,17 and they all caution against choosing instruments. Such variables do not predict the outcome, can bias point estimates, and inflate standard errors.…”

Section: Discussionmentioning

confidence: 99%

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Diagnostics for Confounding of Time-varying and Other Joint Exposures

Jackson¹

2016

Epidemiology

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The effects of joint exposures (or exposure regimes) include those of adhering to assigned treatment vs. placebo in a randomized controlled trial, duration of exposure in a cohort study, interactions between exposures, and direct effects of exposure, among others. Unlike the setting of a single point exposure (e.g. propensity score matching), there are few tools to describe confounding for joint exposures or how well a method resolves it. Investigators need tools that describe confounding in ways that are conceptually grounded and intuitive for those who read, review, and use applied research to guide policy. We revisit the implications of exchangeability conditions that hold in sequentially randomized trials, and the bias structure that motivates the use of g-methods such as marginal structural models. From these we develop covariate balance diagnostics for joint exposures that can (1) describe time-varying confounding (2) assess whether covariates are predicted by prior exposures given their past, the indication for g-methods (3) describe residual confounding after inverse probability weighting. For each diagnostic we present time-specific metrics that encompass a wide class of joint exposures, including regimes of multivariate time-varying exposures in censored data, with multivariate point exposures as a special case. We outline how to estimate these directly or with regression and how to average them over person-time. Using a simulated example, we show how these metrics can be presented graphically. This conceptually grounded framework can potentially aid the transparent design, analysis, and reporting of studies that examine joint exposures. We provide easy-to-use tools to implement it.

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Section: Discussionmentioning

confidence: 99%

“…While there are many ways to select variables for this purpose, 16,17 drawing causal inference presumes that the ones chosen can grant exchangeability Y ā ∐ A ( t )| Ā ( t − 1), C̄ ( t ). In this setting, every departure from statistical exogeneity is of concern.…”

Section: Simulated Example and Notationmentioning

confidence: 99%

Diagnostics for Confounding of Time-varying and Other Joint Exposures

Jackson¹

2016

Epidemiology

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“…Cofactors in the experimental groups are distributed equally. This eliminates the need for further statistical correction of confound- ing [26,27]. The following factors were taken into account: age, NIHSS, TICI at admission, ASPECT score, presence of Diabetes mellitus or number of collaterals and applies to the classification according to NECT, as well as the classification according to CECT groups (Tables 2, 3) [26].…”

Section: Density and Enhancement Groupsmentioning

confidence: 99%

Thrombus Enhancement Is a Predictor of Clinical Outcome in Acute Ischemic Stroke after Mechanical Thrombectomy

et al. 2018

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Background: Ex vivo computed tomography (CT) studies of artificial blood thrombi showed that contrast enhancement (CE) is determined by fibrin-content, while unenhanced density is associated with red blood cells. Thus, the present study investigates patient outcome in association with combined thrombus density measures in native and contrast-enhanced CT (CECT) of acute ischemic stroke patients. Methods: This retrospective study includes 137 patients with M1 occlusions treated by mechanical thrombectomy (MT) between 2010 and 2016. Clinical outcome was determined with modified Rankin Scale (mRS) at 90 days. Differentiation of complete and incomplete large vessel occlusion (CLVO/ILVO) was based on CT and angiography. Two blinded readers classified blood thrombi based on native non-enhanced CT (NECT) as (a) hypo-, (b) iso-, and (c) hyperdense and in CECT angio measurements as (d) not-enhancing, (e) intermediate and (f) enhancing. To make sure that the mean is not represented in any of the maximum/minimum groups, thresholds in both cases were selected in a way that all values within one SD around the mean value form the isodense/intermediate group. In addition, the CE per se was correlated with the outcome. Correlations between imaging and clinical scales were performed with Spearman’s Rho. For the group testing Pearson chi-square test, Mann-Whitney U, as well parametric and nonparametric one-factor ANOVA “Kruskal-Wallis” test including Bonferroni correction for multiple tests ware used. Results: Twenty-three patients with ILVO (16.8%) differed significantly from patients with CLVO in mRS at admission (median 4 vs. 5) and after 90 days (median 1 vs. 4; p < 0.05) and thus were excluded. In the ILVO cohort, the classification according to NECT did not show statistical difference between hypo-, iso- and hyperdense CLVOs in regard to outcome. Classification of CLVOs according to CECT allowed an outcome prediction between the intermediate (median 3) and enhancing group (median 5) and between the enhancing and non-enhancing group (median 3; both p < 0.05) with a correlation of 291 between CE and higher mRS after 90 days (p < 0.005). Conclusions: CE of thrombi – especially in a range from over 18.4 to 40.35 Hounsfield Units – is an independent predictor of poor clinical outcome in patients undergoing MT due to acute middle cerebral artery occlusion.

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“…Recently, attempts have been made to fill the resulting conceptual vacuum. For instance, T.J. VanderWeele and I. Shpitser [57] considered six candidate definitions proposed either formally or informally in the literature. Among them only one satisfies two properties that should be met.…”

Section: Idmentioning

confidence: 99%

Causality, a Trialogue

Chambaz

Drouet²,

Thalabard

2014

Journal of Causal Inference

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A philosopher, a medical doctor, and a statistician talk about causality. They discuss the relationships between causality, chance, and statistics, resorting to examples from medicine to develop their arguments. This debate gives rise to an original trialogue, a tribute to the famous conversation between d'Alembert and Diderot, two great French thinkers of the Enlightenment. The trialogue notably offers an introduction to the philosophy of causality and an initiation to statistics, including recent developments that should prove interesting to specialists and laypeople alike.Otros cien pasos serían los que anduvieron, cuando al doblar de una punta pareció descubierta y patente la misma causa, sin que pudiese ser otra, de aquel horrísono y para ellos espantable ruido, que tan suspensos y medrosos toda la noche les había tenido; y eran (si no lo has, ! oh lector! por pesadumbre y enojo) seis mazos de batán que con sus alternativos golpes aquel estruendo formaban.They went it might be a hundred paces farther, when on turning a corner the true cause, beyond the possibility of any mistake, of that dread-sounding and to them awe-inspiring noise that had kept them all the night in such fear and perplexity, appeared plain and obvious; and it was (if, reader, thou art not disgusted and disappointed) six fulling hammers which by their alternate strokes made all the din. M. de Cervantes, El Ingenioso Hidalgo de Don Quijote de la Mancha (English translation by J. Ormsby)In relating what follows I must confess to a certain chronological vagueness. The events themselves I can see in sharp focus, and I want to think they happened that same evening, and there are good reasons to suppose they did. In a narrative sense they present a nice neat package, effect dutifully tripping along at the heels of cause. Perhaps it is the attraction of such simplicity that makes me suspicious, that along with the conviction that real life seldom works this way.R. Russo, The risk pool Notation -9, existential quantifier, mathematical symbol meaning "there exists." -), mathematical symbol meaning "implies" (or, equivalently, "if (…) then (…)").-0-1, mathematical notation meaning "0 or 1." -f0; 1g, set consisting of the two numbers 0 and 1. -½a; b, interval consisting of all the real numbers equal to or larger than a and equal to or smaller than b.*Corresponding author: Antoine Chambaz, Modal'X (EA 3454), Université Paris Ouest Nanterre, 200 av de la République, From this follows a counterfactual model for reality, where what happens in the real world is the projection of what concomitantly occurs in parallel, counterfactual worlds. Used for the sake of approaching causality, this induces a conceptual difficulty: there may be something more, or something else, in effects and causes than what actually takes place in our world. Or not, since a puzzling twist allows the statistician to build counterfactual worlds inducing by projection the actual world based on the observation of the sole actual world.The aforementioned projection is driven by possibl...

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On the definition of a confounder

Cited by 205 publications

References 39 publications

Diagnostics for Confounding of Time-varying and Other Joint Exposures

Diagnostics for Confounding of Time-varying and Other Joint Exposures

Thrombus Enhancement Is a Predictor of Clinical Outcome in Acute Ischemic Stroke after Mechanical Thrombectomy

Causality, a Trialogue

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