Study Designs for Dependent Happenings

Halloran, M. Elizabeth; Struchiner, Cláudio J.

doi:10.1097/00001648-199109000-00004

Cited by 204 publications

(158 citation statements)

References 39 publications

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“…In this setting, the observed treatment effect is a function not only of the direct effect of treatment but also indirect effects from spillover or contamination (Halloran and Struchiner 1991). This paper deals specifically with the context of cluster-randomized trials of an intervention intended to prevent the transmission of an infectious disease.…”

Section: Introductionmentioning

confidence: 99%

“…For individually-randomized interventions and when interference is contained within clusters of individuals, several causal effects are identifiable using two-stage randomization (Halloran and Struchiner 1991;Hudgens and Halloran 2008;Tchetgen Tchetgen and Vander Weele 2012). At the first stage, clusters are randomized to treatment allocation programs; at the second, individuals within clusters are randomized to treatment or control according to the assigned allocation program.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-Randomized Trials of Infectious Disease Prevention

Carnegie

Wang²,

Gruttola

2016

Epidemiologic Methods

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An issue that remains challenging in the field of causal inference is how to relax the assumption of no interference between units. Interference occurs when the treatment of one unit can affect the outcome of another, a situation which is likely to arise with outcomes that may depend on social interactions, such as occurrence of infectious disease. Existing methods to accommodate interference largely depend upon an assumption of "partial interference" -interference only within identifiable groups but not among them. There remains a considerable need for development of methods that allow further relaxation of the nointerference assumption. This paper focuses on an estimand that is the difference in the outcome that one would observe if the treatment were provided to all clusters compared to that outcome if treatment were provided to none -referred as the overall treatment effect. In trials of infectious disease prevention, the randomized treatment effect estimate will be attenuated relative to this overall treatment effect if a fraction of the exposures in the treatment clusters come from individuals who are outside these clusters. This source of interference -contacts sufficient for transmission that are with treated clusters -is potentially measurable. In this manuscript, we leverage epidemic models to infer the way in which a given level of interference affects the incidence of infection in clusters. This leads naturally to an estimator of the overall treatment effect that is easily implemented using existing software.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-Randomized Trials of Infectious Disease Prevention

Carnegie

Wang²,

Gruttola

2016

Epidemiologic Methods

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“…The main statistical analyses proposed are total vaccine efficacy among children 6 months-10 years of age and indirect vaccine efficacy among non-vaccinated household contacts of children enrolled for vaccination [22]. Statistical significance in all our analyses will be determined using a two-sided 0.05 significance level.…”

Section: Analysis Planmentioning

confidence: 99%

Design and initiation of a study to assess the direct and indirect effects of influenza vaccine given to children in rural India

Sullender

Fowler

Krishnan

et al. 2012

Vaccine

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The burden of disease due to influenza is not well characterized for children in developing countries and the effectiveness of available influenza vaccines in lower resource settings has not been established. We initiated a prospective, longitudinal, phase IV, household-randomized, controlled, observer-blinded three year study (2009)(2010)(2011) in a rural community of India to measure the total and indirect household protective effects of immunizing children ages 6 months through 10 years with seasonal inactivated trivalent influenza vaccine (TIV) or a control vaccine (n = 3697). Active weekly surveillance was conducted year round with home visits for identification of febrile acute respiratory illness (FARI) conducted for all vaccine recipients and household members (n = 18,220). Nasal and throat swabs were collected from each FARI episode for influenza detection by real-time reverse transcription polymerase chain reaction. The primary outcome was reduction in laboratory confirmed influenza infections in the influenza vaccine versus control vaccine group, with secondary outcome assessing indirect effects among the entire study population. This report describes the study site, cluster study design, choice of study and control vaccines, and the initial enrollment in the study.

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“…Just as an unvaccinated person experiences reduced risk of infection when another person in the same household is vaccinated, a low-income person experiences reduced risk of suffering the negative consequences of poverty when sharing a household with a high-income person (Krieger, Chen, & Selby, 1999). Randomization of higher units of aggregation, such as households, neighborhoods or communities, may alleviate this problem to some extent (Ukoumunne, Gulliford, Chinn, Sterne, & Burney, 1999), but this consideration places severe constraints on the design and interpretation of randomized social interventions in which unit dependency would be expected (Halloran & Struchiner, 1991).…”

Section: Limitations Of Randomized Social Interventionsmentioning

confidence: 99%

Causal inference from randomized trials in social epidemiology

Kaufman

Poole

2003

Social Science & Medicine

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Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, causal inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem. We review the causal inference problem in social epidemiology, and the potential for causal inference in randomized social interventions. Using the example of a currently on-going intervention that randomly assigns families to non-poverty housing, we review the limitations to causal inference even under experimental conditions and explain which causal effects become identifiable. We note the benefit of using the randomized trial as a conceptual model, even for design and interpretation of observational studies in social epidemiology. r

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Study Designs for Dependent Happenings

Cited by 204 publications

References 39 publications

Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-Randomized Trials of Infectious Disease Prevention

Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-Randomized Trials of Infectious Disease Prevention

Design and initiation of a study to assess the direct and indirect effects of influenza vaccine given to children in rural India

Causal inference from randomized trials in social epidemiology

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