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Supercourse Statistics Course

Mathematical models are used in science to better understand and make predictions of real world phemenon. Often they may help answer questions that cannot be answered by empirical data or experiments. When the phenomenon has elements of uncertainty, which are governed by probability laws, the model is called a probability or a stochastic model. Stochastic models are often characterized by observing a real world situation over time Examples of stochastic models in the biomedical sciences have been developed for therapeutic and early detection clinical trials, epidemics of infectious diseases, cell growth This course is an introduction to stochastic processes as applied to the biomedical sciences. Among the topics which will be discussed are: epidemiology models for incidence, prevalence and mortality, backward and forward recurrence times and their relationship to length biased sampling, Poisson processes, birth and death processes, Markov chains and semi-Markov processes.

Supercourse Statistics Course Lectures

An Introduction to Stochastic Processes in Public Health

• Birth and death processes
• Elements of Laplace transforms. Part I
• Markov chains
• Poisson processes. Part I
• Recurrent and Transient States
• Relations between incidence, prevalence and time with disease. Part I
• Renewal processes
• Semi-Markov processes
• Semi-Markov Processes. Part I
• "Stochastic Processes in Public Health" An Introduction

Elements of the Analysis of Discrete Data

• Correlated Outcomes
• Examples and Elements of Theory
• Independence and Urn Sampling
• Logistic Regression
• Multiple Logistic Regression
• Multivariate problems
• Polychotomous Regression
• Proportional Hazards Models and Urn sampling