Estimating Vaccine Efficacy From Secondary Attack Rates

Halloran, M. Elizabeth; Preziosi, Marie Pierre; Chu, Haitao

doi:10.1198/016214503388619076

Cited by 38 publications

(49 citation statements)

References 28 publications

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“…However, Becker's GLM method [4] does not allow for correlation within transmission units. In the case of just one infective and one generation, the simpliÿed model of Becker is similar to that ÿtted by Halloran et al [6] using secondary attack rates, but does not include within-transmission unit correlation. Neither Becker's GLM method [4] nor Halloran's method [6] can estimate e ects of the infective individuals' covariates when susceptibles are exposed to multiple infectives with di erent covariates.…”

Section: Introductionmentioning

confidence: 91%

“…In the case of just one infective and one generation, the simpliÿed model of Becker is similar to that ÿtted by Halloran et al [6] using secondary attack rates, but does not include within-transmission unit correlation. Neither Becker's GLM method [4] nor Halloran's method [6] can estimate e ects of the infective individuals' covariates when susceptibles are exposed to multiple infectives with di erent covariates. In practice, analyses either ignore co-infectives [5,7], which excludes the pairwise transmission between co-infectives and susceptibles within transmission units with multiple infectives in the analyses, or exclude the transmission units with multiple infectives [8], which exclude all pairwise transmission between all infectives and susceptibles within the transmission units in the analyses.…”

Section: Introductionmentioning

confidence: 91%

“…Co-primaries were deÿned as those cases whose onset of cough was ¡7 days, the minimum incubation period, after that of the index case. In the initial analyses of the data [6,8], they included only households with a unique index case and excluded households with co-primaries. To take correlation into account, they used generalized estimating equations (GEE) [13], generalized non-linear mixed models [14] and Bayesian hierarchical models [15,16].…”

Section: Data and Motivating Analysesmentioning

confidence: 99%

“…We deÿned pertussis according to the main case deÿnition in References [8,6]. We included in the analysis people who were either unvaccinated or who were fully vaccinated with at least three doses of pertussis vaccine.…”

Section: Data and Motivating Analysesmentioning

confidence: 99%

“…Methods have been developed to estimate the transmission probability based on the binomial model [1], the chain binomial model [2][3][4] and the secondary attack rate [5][6][7], including, in many instances, covariates for either the susceptible, the infective or both. However, some important problems are not solved by currently existing methods.…”

Section: Introductionmentioning

confidence: 99%

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Estimating heterogeneous transmission with multiple infectives using MCMC methods

Chu

Preziosi

Halloran

2003

Statistics in Medicine

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We developed a general procedure for estimating the transmission probability adjusting for covariates when susceptibles are exposed to several infectives concurrently and taking correlation within transmission units into account. The procedure is motivated by a study estimating efficacy of pertussis vaccination based on the secondary attack rate in a rural sub-Saharan community (Niakhar, Senegal) and illustrated with simulations. The procedure is also appropriate to estimate the pairwise transmission probability in transmission studies of live vaccine virus in a collection of transmission units, such as day-care centres or retirement centres. Previously, analyses either excluded transmission units with multiple infectives or ignored co-infectives. Excluding transmission units with multiple infectives is statistically less efficient and ignoring co-infectives can lead to biased estimation. Modelling is carried out by regressing the latent pairwise transmission probability from each infective to a susceptible on covariates and specifying a transmission linkage function linking the latent pairwise transmission probability to the overall transmission probability. Parameters are estimated using Markov chain Monte Carlo methods.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 91%

Section: Data and Motivating Analysesmentioning

confidence: 99%

Section: Data and Motivating Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimating heterogeneous transmission with multiple infectives using MCMC methods

Chu

Preziosi

Halloran

2003

Statistics in Medicine

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Household Secondary Attack Rates of SARS-CoV-2 by Variant and Vaccination Status

et al. 2022

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Pairwise Accelerated Failure Time Regression Models for Infectious Disease Transmission in Close‐Contact Groups With External Sources of Infection

Sharker,

Diallo,

KhudaBukhsh

et al. 2024

Statistics in Medicine

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Many important questions in infectious disease epidemiology involve associations between covariates (e.g., age or vaccination status) and infectiousness or susceptibility. Because disease transmission produces dependent outcomes, these questions are difficult or impossible to address using standard regression models from biostatistics. Pairwise survival analysis handles dependent outcomes by calculating likelihoods in terms of contact interval distributions in ordered pairs of individuals. The contact interval in the ordered pair is the time from the onset of infectiousness in to infectious contact from to , where an infectious contact is sufficient to infect if they are susceptible. Here, we introduce a pairwise accelerated failure time regression model for infectious disease transmission that allows the rate parameter of the contact interval distribution to depend on individual‐level infectiousness covariates for , individual‐level susceptibility covariates for , and pair‐level covariates (e.g., type of relationship). This model can simultaneously handle internal infections (caused by transmission between individuals under observation) and external infections (caused by environmental or community sources of infection). We show that this model produces consistent and asymptotically normal parameter estimates. In a simulation study, we evaluate bias and confidence interval coverage probabilities, explore the role of epidemiologic study design, and investigate the effects of model misspecification. We use this regression model to analyze household data from Los Angeles County during the 2009 influenza A (H1N1) pandemic, where we find that the ability to account for external sources of infection increases the statistical power to estimate the effect of antiviral prophylaxis.

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Estimating Vaccine Efficacy From Secondary Attack Rates

Cited by 38 publications

References 28 publications

Estimating heterogeneous transmission with multiple infectives using MCMC methods

Estimating heterogeneous transmission with multiple infectives using MCMC methods

Household Secondary Attack Rates of SARS-CoV-2 by Variant and Vaccination Status

Pairwise Accelerated Failure Time Regression Models for Infectious Disease Transmission in Close‐Contact Groups With External Sources of Infection

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