Causal Diagrams for Interference

Ogburn, Elizabeth L.; VanderWeele, Tyler J.

doi:10.1214/14-sts501

Cited by 118 publications

(126 citation statements)

References 46 publications

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“…However, when there are interactions between

X_{ij}^{r}

and

A_{i}

exposure and covariate interference are related; in this case, individual ij may be seen as receiving pseudo‐treatment

A_{i} X_{ij}^{r}

. For such a setting, our work may be seen as extending the notion of exposure interference in RTs to CRTs and is related to the work of Ogburn and VanderWeele (). In any case, modeling covariate interference may lead to substantial gains of efficiency if they predict the outcome.…”

Section: Discussionmentioning

confidence: 99%

Accounting for Interactions and Complex Inter-Subject Dependency in Estimating Treatment Effect in Cluster-Randomized Trials with Missing Outcomes

et al. 2016

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Summary Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions between treatment and baseline covariates, neither method alone produces consistent estimates for the marginal treatment effect if the model for interaction is not correctly specified. We propose an AUG-IPW estimator that weights by the inverse of the probability of being a complete case and allows different outcome models in each intervention arm. This estimator is doubly robust (DR), it gives correct estimates whether the missing data process or the outcome model is correctly specified. We consider the problem of covariate interference which arises when the outcome of an individual may depend on covariates of other individuals. When interfering covariates are not modeled, the DR property prevents bias as long as covariate interference is not present simultaneously for the outcome and the missingness. An R package is developed implementing the proposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method.

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“…However, when there are interactions between

X_{ij}^{r}

and

A_{i}

exposure and covariate interference are related; in this case, individual ij may be seen as receiving pseudo‐treatment

A_{i} X_{ij}^{r}

Section: Discussionmentioning

confidence: 99%

Accounting for Interactions and Complex Inter-Subject Dependency in Estimating Treatment Effect in Cluster-Randomized Trials with Missing Outcomes

et al. 2016

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“…For network-randomized GRTs in which the intervention is not directly administered to all individuals and in which it is expected that the intervention spreads over the network (e.g. the snowball trials of a HIV prevention intervention for drug users 84 or a microfinance intervention 85 ), methods 86,87 are available to estimate both the direct and indirect effects of the intervention. When network information is available and the outcome of interest is known to be a disseminated process, adjusting for network features such as information on the location of each individual within the network (i.e.…”

Section: Developments In the Analysis Of Alternatives To The Parallelmentioning

confidence: 99%

Review of Recent Methodological Developments in Group-Randomized Trials: Part 2—Analysis

et al. 2017

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In 2004, Murray et al. published a review of methodological developments in both the design and analysis of group-randomized trials (GRTs). Over the last 13 years, there have been many developments in both areas. The goal of the current paper is to review developments in analysis, with a companion paper to focus on developments in design. As a pair, these papers update the 2004 review. This analysis paper includes developments in topics included in the earlier review, such as methods for parallel-arm GRTs, inference for conditional and marginal effects, and new topics including methods to account for multiple levels of clustering and alternative estimation methods such as augmented GEE, targeted maximum likelihood and quadratic inference functions. We also examine developments in dealing with missing outcome data, including doubly robust approaches, software available for analysis, and analysis of alternative group designs (including stepped wedge GRTs, network-randomized trials, pseudo-cluster randomized trials and individually-randomized group treatment trials). These alternative designs, like the parallel-arm GRT, require clustering to be accounted for in both their design and analysis.

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“…If there is a single intervention and a control condition, mixing between communities assigned to different treatment policies will tend to attenuate this overall effect. The term interference between units has been used to describe the phenomenon that outcomes of one unit may be affected by the treatment assignments of other units (see for example, [19]). Under the null hypothesis of no treatment effect, the permutation test has the correct type I error and remains valid as randomization is the sole basis for inference and no treatment effect implies no interference between units [14].…”

Section: Parallel Cluster Randomized Trials With Time-to-event Endmentioning

confidence: 99%

The use of permutation tests for the analysis of parallel and stepped‐wedge cluster‐randomized trials

Wang

Gruttola

2017

Statistics in Medicine

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We investigate the use of permutation tests for the analysis of parallel and stepped-wedge cluster randomized trials. Permutation tests for parallel designs with exponential family endpoints have been extensively studied. The optimal permutation tests developed for exponential family alternatives require information on intraclass correlation, a quantity not yet defined for time-to-event endpoints. Therefore, it is unclear how efficient permutation tests can be constructed for cluster-randomized trials with such endpoints. We consider a class of test statistics formed by a weighted average of pair-specific treatment effect estimates and offer practical guidance on the choice of weights to improve efficiency. We apply the permutation tests to a cluster-randomized trial evaluating the effect of an intervention to reduce the incidence of hospital-acquired infection. In some settings, outcomes from different clusters may be correlated, and we evaluate the validity and efficiency of permutation test in such settings. Lastly, we propose a permutation test for stepped-wedge designs and compare its performance to mixed effect modeling, and illustrate its superiority when sample sizes are small, the underlying distribution is skewed, or there is correlation across clusters.

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Causal Diagrams for Interference

Cited by 118 publications

References 46 publications

Accounting for Interactions and Complex Inter-Subject Dependency in Estimating Treatment Effect in Cluster-Randomized Trials with Missing Outcomes

Accounting for Interactions and Complex Inter-Subject Dependency in Estimating Treatment Effect in Cluster-Randomized Trials with Missing Outcomes

Review of Recent Methodological Developments in Group-Randomized Trials: Part 2—Analysis

The use of permutation tests for the analysis of parallel and stepped‐wedge cluster‐randomized trials

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