Spatial regression and spillover effects in cluster randomized trials with count outcomes

Karim Anaya-Izquierdo; Neal Alexander

doi:10.1111/biom.13316

Spatial regression and spillover effects in cluster randomized trials with count outcomes

Karim Anaya-Izquierdo, Neal Alexander

London School of Hygiene & Tropical Medicine

Research output: Contribution to journal › Article › peer-review

2 Citations (SciVal)

Abstract

This paper describes methodology for analyzing data from cluster randomized trials with count outcomes, taking indirect effects as well spatial effects into account. Indirect effects are modelled using a novel application of a measure of depth within the intervention arm. Both direct and indirect effects can be estimated accurately even when the proposed model is misspecified. We use spatial regression models with Gaussian random effects, where the individual outcomes have distributions overdispersed with respect to the Poisson, and the corresponding direct
and indirect effects have a marginal interpretation. To avoid spatial confounding, we use orthogonal regression, in which random effects represent spatial dependence using a homoscedastic and dimensionally-reduced modification
of the intrinsic conditional autoregression (ICAR) model. We illustrate the methodology using spatial data from a pair-matched cluster randomized trial against the dengue mosquito vector Aedes aegypti, done in Trujillo, Venezuela

Original language	English
Pages (from-to)	490-505
Journal	Biometrics
Volume	77
Issue number	2
Early online date	18 Jun 2020
DOIs	https://doi.org/10.1111/biom.13316
Publication status	Published - 1 Jun 2021

Bibliographical note

Funding Information:
This work was supported by the United Kingdom Medical Research Council (MRC) via Grant Reference G7508177, also by MRC and the UK Department for International Development (DFID), MRC Grant References MR/K012126/1 and MR/R010161/1, awards jointly funded by the MRC and DFID under the MRC/DFID Concordat agreement and also part of the EDCTP2 programme supported by the European Union. We also thank Christian Bottomley for helpful suggestions, Valeria Simoncini for guidance on iterative projection algorithms, and anonymous referees for useful suggestions.

Publisher Copyright:
© 2020 The Authors. Biometrics published by Wiley Periodicals, LLC on behalf of International Biometric Society.

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Karim Anaya-Izquierdo
- K.Anaya-Izquierdobath.acuk
Person: Research & Teaching

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title = "Spatial regression and spillover effects in cluster randomized trials with count outcomes",

abstract = "This paper describes methodology for analyzing data from cluster randomized trials with count outcomes, taking indirect effects as well spatial effects into account. Indirect effects are modelled using a novel application of a measure of depth within the intervention arm. Both direct and indirect effects can be estimated accurately even when the proposed model is misspecified. We use spatial regression models with Gaussian random effects, where the individual outcomes have distributions overdispersed with respect to the Poisson, and the corresponding directand indirect effects have a marginal interpretation. To avoid spatial confounding, we use orthogonal regression, in which random effects represent spatial dependence using a homoscedastic and dimensionally-reduced modificationof the intrinsic conditional autoregression (ICAR) model. We illustrate the methodology using spatial data from a pair-matched cluster randomized trial against the dengue mosquito vector Aedes aegypti, done in Trujillo, Venezuela",

author = "Karim Anaya-Izquierdo and Neal Alexander",

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Spatial regression and spillover effects in cluster randomized trials with count outcomes

Abstract

Bibliographical note

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