The instrumental variables framework is commonly used for the estimation of causal effects from cohort samples. However, the combination of instrumental variables with more efficient designs such as case-control sampling requires new methodological consideration. For example, as the use of...

Consider the simple setting of point exposure, outcome and confounding variables, all of which are discrete. As is well known, parametric modeling of outcome given exposure and confounders and also exposure given confounders can yield a double-robust estimator. This has the property of being...

The determination of a cut-off value for a continuous prognostic test is an important problem, which is statistically challenging and practically important for risk assessment. We propose in this paper a method to estimate the optimal cut-off from this type of longitudinal data with censored...

Meta-analysis of observational studies is an exciting new area of innovation in statistical science. Unlike randomized controlled trials, which are the gold standard for proving causation, observational studies are prone to biases including confounding. In this article, we describe a novel...

If a vaccine does not protect individuals completely against infection, it could still reduce infectiousness of infected vaccinated individuals to others. Typically, vaccine efficacy for infectiousness is estimated based on contrasts between the transmission risk to susceptible individuals from...

In many causal inference problems, one is interested in the direct causal effect of an exposure on an outcome of interest that is not mediated by certain intermediate variables. Robins and Greenland (1992) and Pearl (2001) formalized the definition of two types of direct effects (natural and...

A major goal of vaccine development is the identification of immune responses that are responsible for vaccine efficacy. In theory, modest vaccines could be successfully improved by increasing such immune responses. And for a vaccine with a great benefit in one population, inducing such immune...

Longitudinal data arise frequently in medical studies and it is a common practice to analyze such complex data with nonlinear mixed-effects (NLME) models, which enable us to account for between-subject and within-subject variations. To partially explain the variations, time-dependent covariates...