Showing papers by "M. Elizabeth Halloran published in 2006"

Antiviral Effects on Influenza Viral Transmission and Pathogenicity: Observations from Household-based Trials

Abstract: Four household-based, randomized clinical trials, two each of zanamivir and oseltamivir, were designed primarily to estimate the effect of postexposure prophylaxis on preventing influenza illness in household contacts. However, the effect of influenza antivirals on infectiousness as well as on the ability of the virus to cause disease--the pathogenicity--have important public health consequences. The authors show how such studies can provide estimates of pathogenicity, antiviral efficacy for pathogenicity, and the antiviral effect on infectiousness. Analysis of the four studies confirmed the high prophylactic efficacy against illness of both zanamivir (75%, 95% confidence interval (CI): 54, 86) and oseltamivir (81%, 95% CI: 35, 94). The effect on reducing infectiousness was 19% (95% CI: -160, 75) for zanamivir and 80% (95% CI: 43, 93) for oseltamivir. Pathogenicity in controls ranged from 44% (95% CI: 33, 55) to 66% (95% CI: 48, 72). Efficacy in reducing pathogenicity for zanamivir was 52% (95% CI: 19, 72) and 56% (95% CI: 14, 77) in the two studies; for oseltamivir, it was 56% (95% CI: 10, 73) and 79% (95% CI: 45, 92). Studies of influenza antivirals in transmission units would be improved if randomization schemes were used that allow estimation of the antiviral effect on infectiousness from individual studies.

Design and Evaluation of Prophylactic Interventions Using Infectious Disease Incidence Data from Close Contact Groups.

Abstract: Prophylaxis of contacts of infectious cases such as household members and treatment of infectious cases are methods to prevent spread of infectious diseases. We develop a method based on maximum likelihood to estimate the efficacy of such interventions and the transmission probabilities. We consider both the design with prospective follow-up of close contact groups and the design with ascertainment of close contact groups by an index case as well as randomization by groups and by individuals. We compare the designs using simulations. We estimate the efficacy of the influenza antiviral agent oseltamivir in reducing susceptibility and infectiousness in two case-ascertained household trials.

Community Studies for Vaccinating Schoolchildren Against Influenza

Abstract: A plan to vaccinate schoolchildren against flu presents an opportunity to assess risks and benefits

Causal Vaccine Effects on Binary Postinfection Outcomes

Abstract: The effects of vaccine on postinfection outcomes, such as disease, death, and secondary transmission to others, are important scientific and public health aspects of prophylactic vaccination. As a result, evaluation of many vaccine effects condition on being infected. Conditioning on an event that occurs posttreatment (in our case, infection subsequent to assignment to vaccine or control) can result in selection bias. Moreover, because the set of individuals who would become infected if vaccinated is likely not identical to the set of those who would become infected if given control, comparisons that condition on infection do not have a causal interpretation. In this article we consider identifiability and estimation of causal vaccine effects on binary postinfection outcomes. Using the principal stratification framework, we define a postinfection causal vaccine efficacy estimand in individuals who would be infected regardless of treatment assignment. The estimand is shown to be not identifiable under the standard assumptions of the stable unit treatment value, monotonicity, and independence of treatment assignment. Thus selection models are proposed that identify the causal estimand. Closed-form maximum likelihood estimators (MLEs) are then derived under these models, including those assuming maximum possible levels of positive and negative selection bias. These results show the relations between the MLE of the causal estimand and two commonly used estimators for vaccine effects on postinfection outcomes. For example, the usual intent-to-treat estimator is shown to be an upper bound on the postinfection causal vaccine effect provided that the magnitude of protection against infection is not too large. The methods are used to evaluate postinfection vaccine effects in a clinical trial of a rotavirus vaccine candidate and in a field study of a pertussis vaccine. Our results show that pertussis vaccination has a significant causal effect in reducing disease severity.

On estimation of vaccine efficacy using validation samples with selection bias

Abstract: Using validation sets for outcomes can greatly improve the estimation of vaccine efficacy (VE) in the field (Halloran and Longini, 2001; Halloran and others, 2003). Most statistical methods for using validation sets rely on the assumption that outcomes on those with no cultures are missing at random (MAR). However, often the validation sets will not be chosen at random. For example, confirmational cultures are often done on people with influenza-like illness as part of routine influenza surveillance. VE estimates based on such non-MAR validation sets could be biased. Here we propose frequentist and Bayesian approaches for estimating VE in the presence of validation bias. Our work builds on the ideas of Rotnitzky and others (1998, 2001), Scharfstein and others (1999, 2003), and Robins and others (2000). Our methods require expert opinion about the nature of the validation selection bias. In a re-analysis of an influenza vaccine study, we found, using the beliefs of a flu expert, that within any plausible range of selection bias the VE estimate based on the validation sets is much higher than the point estimate using just the non-specific case definition. Our approach is generally applicable to studies with missing binary outcomes with categorical covariates.

Invited commentary: Challenges of using contact data to understand acute respiratory disease transmission.

Abstract: Wallinga et al. (1) have done an excellent job of demonstrating how simple data can be used to improve our estimation of transmission parameters for infectious disease models. Their analysis involves several steps illustrating a unifying framework, from collecting data on social contacts to model-fitting with relevant infectious disease data. First, there is estimation of the contact matrix from age-specific data on conversations. Second, assumptions are made to estimate the age-specific transmission parameters of a particular transmission model. Third, comparison is made with infectious disease outcome data for goodness of fit and model choice. The problem is very important. Contact patterns are crucial determinants in both the spread of an infectious disease and the decision on which interventions would be most effective. Sometimes a group of investigators pulls together several ideas, in preliminary form, showing the way for future research. Such is the case with this paper by Wallinga et al. I would like to comment on four areas that deserve additional research: 1) data structure, 2) model-dependent transmission parameters, 3) statistical inference, and 4) infectious disease data for model-fitting. In the paper by Wallinga et al. (1), the data on which the contact matrix analysis was based were remarkably simple. A random sample of people in Utrecht, the Netherlands, were asked about the number of conversations they had with people of different age groups during a typical week. These simple data were adequate to estimate an age-structured contact matrix. The simple age-structured matrix was in turn adequate to estimate transmission parameters for an agestructured model. In this type of model, the mixing groups are mutually exclusive; that is, a person can belong to only one age group. The transmission parameters that are estimated in this paper are specific to the type of model being used. However, many current models being used to study the effects of interventions in populations, such as those for pandemic influenza (2–5) and smallpox (6), have more complex population structures. In these models, people can mix in several different places, including households, schools, and workplaces, as well as have age-specific components to the mixing within the different mixing groups. These more complex patterns are required to analyze the effect of household interventions, such as targeted antiviral prophylaxis, school closures, or quarantines. I would like to see more empirical studies like the one presented in this paper with these more complex models in mind (7). A natural extension of the data structure would be to ask people not only about the age groups of people with whom they have had conversations but also where they had the conversations. Such data would allow estimation of transmission parameters for models with more complex mixing structures. Since the interpretation of transmission parameters is model-specific, such data for estimation of transmission parameters would be very important. The third area, statistical inference, also needs further development to take honest account of the uncertainty in the estimates. The current analysis probably underestimates the uncertainty in the mixing matrix, which then carries over to an underestimate of uncertainty in the estimates for the transmission model. First, the bootstrap confidence intervals presented in Wallinga et al.’s table 1 are only for the mean values of the negative binomial distributions that were fitted to the data. There is no mention of k, the shape parameter, so we do not know the full uncertainty of the entire distribution. Second, to estimate the age-specific transmission parameters, Wallinga et al. keep the estimated means fixed at the maximum likelihood value. Heterogeneity in the number of contacts within age groups and the variability of the data