Assessing effects of cholera vaccination in the presence of interference

Perez-Heydrich, Carolina; Hudgens, Michael G.; Halloran, M. Elizabeth; Clemens, John D.; Ali, Mohammad; Emch, Michael

doi:10.1111/biom.12184

Cited by 75 publications

(126 citation statements)

References 18 publications

(36 reference statements)

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“…The asymptotic distributions of the inverse probability-weighted and Hájek-type estimators can be derived from standard estimating equation theory (Stefanski & Boos, 2002; Perez-Heydrich et al, 2014). For example, the proposition below establishes that the three direct effect estimators are asymptotically normal and gives closed-form expressions for the asymptotic variances when the propensity scores are known.…”

Section: Asymptotic Distributionsmentioning

confidence: 99%

On inverse probability-weighted estimators in the presence of interference

Hudgens²,

2016

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We consider inference about the causal effect of a treatment or exposure in the presence of interference, i.e., when one individual’s treatment affects the outcome of another individual. In the observational setting where the treatment assignment mechanism is not known, inverse probability-weighted estimators have been proposed when individuals can be partitioned into groups such that there is no interference between individuals in different groups. Unfortunately this assumption, which is sometimes referred to as partial interference, may not hold, and moreover existing weighted estimators may have large variances. In this paper we consider weighted estimators that could be employed when interference is present. We first propose a generalized inverse probability-weighted estimator and two Hájek-type stabilized weighted estimators that allow any form of interference. We derive their asymptotic distributions and propose consistent variance estimators assuming partial interference. Empirical results show that one of the Hájek estimators can have substantially smaller finite-sample variance than the other estimators. The different estimators are illustrated using data on the effects of rotavirus vaccination in Nicaragua.

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Section: Asymptotic Distributionsmentioning

confidence: 99%

On inverse probability-weighted estimators in the presence of interference

Hudgens²,

2016

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“…They showed these estimators to be unbiased when the propensity score is known. Perez-Heydrich et al (2014) derived the large sample properties of these estimators when the propensity scores are unknown but correctly modeled. They applied these results to draw inference about the direct and indirect effects of cholera vaccination in Matlab, Bangladesh.…”

Section: Preliminariesmentioning

confidence: 99%

“…The Tchetgen Tchetgen and VanderWeele (2012) IPW estimators require a model for the group-level propensity score (i.e., the probability of a group’s observed treatment allocation). The large sample properties of these estimators were derived by Perez-Heydrich et al (2014).…”

Section: Introductionmentioning

confidence: 99%

“…Existing interference-related packages, including interferenceCI (Rigdon 2015) and blockTools (Moore 2015), were designed for analysis of randomized experiments. In this paper, we present the package inferference which computes the Tchetgen Tchetgen and VanderWeele (2012) IPW estimators and the large sample variance estimators developed by Perez-Heydrich et al (2014).…”

Section: Introductionmentioning

confidence: 99%

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A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R.

Saul¹,

Hudgens²

2017

J. Stat. Soft.

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In causal inference, interference occurs when the treatment of one subject affects the outcome of other subjects. Interference can distort research conclusions about causal effects when not accounted for properly. In the absence of interference, inverse probability weighted (IPW) estimators are commonly used to estimate causal effects from observational data. Recently, IPW estimators have been extended to handle interference. Tchetgen Tchetgen and VanderWeele (2012) proposed IPW methods to estimate direct and indirect (or spillover) effects that allow for interference between individuals within groups. In this paper, we present inferference, an package that computes these IPW causal effect estimates when interference may be present within groups. We illustrate use of the package with examples from political science and infectious disease.

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“…One or more unmeasured confounders may bias effect estimates. Confounding control becomes even more complex in settings with interference since when one individual’s outcome is under consideration, control will often need to be made for the covariates of other individuals in the same cluster (Tchetgen Tchetgen and VanderWeele, 2012; Ogburn and VanderWeele, 2012, Perez-Heydrich et al 2013). Unmeasured confounding can thus operate either through the unmeasured covariates for the focal individual or for other individuals in the same cluster.…”

Section: Interference and Sensitivity Analysismentioning

confidence: 99%

Interference and Sensitivity Analysis

VanderWeele¹,

Tchetgen²,

Halloran³

2014

Statist. Sci.

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Causal inference with interference is a rapidly growing area. The literature has begun to relax the “no-interference” assumption that the treatment received by one individual does not affect the outcomes of other individuals. In this paper we briefly review the literature on causal inference in the presence of interference when treatments have been randomized. We then consider settings in which causal effects in the presence of interference are not identified, either because randomization alone does not suffice for identification, or because treatment is not randomized and there may be unmeasured confounders of the treatment-outcome relationship. We develop sensitivity analysis techniques for these settings. We describe several sensitivity analysis techniques for the infectiousness effect which, in a vaccine trial, captures the effect of the vaccine of one person on protecting a second person from infection even if the first is infected. We also develop two sensitivity analysis techniques for causal effects in the presence of unmeasured confounding which generalize analogous techniques when interference is absent. These two techniques for unmeasured confounding are compared and contrasted.

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Assessing effects of cholera vaccination in the presence of interference

Cited by 75 publications

References 18 publications

On inverse probability-weighted estimators in the presence of interference

On inverse probability-weighted estimators in the presence of interference

A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R.

Interference and Sensitivity Analysis

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