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Estimation Of Causal Effects In Complex Clustered Data, Joshua R. Nugent
Estimation Of Causal Effects In Complex Clustered Data, Joshua R. Nugent
Doctoral Dissertations
Analysis of clustered data from randomized trials or observational data often poses theoretical and practical statistical challenges, including but not limited to small numbers of independent units, many adjustment variables, continuous exposures, and/or differential clustering across trial arms. Further, commonly-used parametric methods rely on assumptions that may be violated in practice. Motivated by three scientific questions in public health, methods are developed and/or demonstrated for non-parametric estimation of causal effects. In Chapter 1, methods are elaborated for a cluster randomized trial (CRT) with missing individual-level data at baseline and follow-up, a complex sampling strategy, and limited number of clusters. Chapter …
Methods For Making Policy-Relevant Forecasts Of Infectious Disease Incidence, Stephen A. Lauer
Methods For Making Policy-Relevant Forecasts Of Infectious Disease Incidence, Stephen A. Lauer
Doctoral Dissertations
Infectious diseases place an enormous burden on the people of the developing world and their governments. When, where, and how to allocate resources in order to slow the spread of a virus or deal with the aftermath of an outbreak is often the responsibility of local public health officials. In this thesis, we develop statistical methods for forecasting future incidence of infectious diseases and estimating the effects of interventions designed to reduce future incidence, bearing in mind the needs and concerns of those public health officials. While most infectious disease forecasting models focus on short-term horizons (i.e. weeks or …