Talks focused on translating recent advances in biostatistics into practice. Biostatistics Continuing Education
At a glance
Opportunity for
  • Continuing education on recent advances in biostatistics
Eligibility
  • All members of the Harvard Catalyst community, but primarily geared toward biostatisticians
Session Dates
  • Varies; see below for details

The Harvard Catalyst Biostatistics Program presents seminars on current applied topics in biostatistics. These include monthly journal club and work in progress sessions, seminar series, symposia, and short courses.

Journal Club/Work in Progress

September 2018 Journal Club

September 6, 2018, 1:00pm-2:00pm
Harvard T.H. Chan School of Public Health, Building 2, Conference Room 426 (4th Fl)

Please join us for the upcoming Harvard Catalyst Biostatistics Journal Club. Kyu Ha Lee, PhD, assistant research investigator and lecturer on oral health policy and epidemiology from the Forsyth Institute, will lead the discussion on the microbiome count/compositional data analysis. First, he will describe the characteristics of the data from high-throughput microbiome sequencing technologies and then talk about different statistical and computational challenges of the count/compositional data analysis. Current analytic methods will be discussed in relation to their advantages and limitations.

To facilitate the upcoming discussion, please read the following paper:

Li H. Microbiome, metagenomics, and high-dimensional compositional data analysis. Annual Review of Statistics and Its Application. 2015 Apr 10;2:73-94. https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-010814-020351

Kyu Ha Lee, PhD
Assistant Research Investigator
Lecturer on Oral Health Policy and Epidemiology
Forsyth Institute

Registration not required.


Seminar Series


Symposia


Short Courses

Bayesian Thinking: Fundamentals, Computation, and Multilevel Modeling
October 1 & October 2, 2018

October 1, 8:30am-12:30pm
October 2, 10:00am-2:00pm
Kresge G3, Harvard T.H. Chan School of Public Health

This course is intended for statisticians who are interested in learning the foundations of Bayesian inference and prediction in the context of regression and multilevel models. Also, the course will be helpful for statisticians who wish to learn about the use of R as an environment for Bayesian computations. It is assumed that the participant has some basic familiarity with the R system.

The basic tenets of Bayesian thinking are introduced, including construction of priors, summarization of the posterior to perform inferences, and the use of prediction distributions for prediction and model checking. There will be a focus on Bayesian regression for continuous and categorical response data. Bayesian multilevel models are introduced as a flexible way of modeling regressions over groups. The use of R in Bayesian computation is described, including the programming of the posterior distribution and the use of different R tools to summarize the posterior. Special focus will be on the application of Markov chain Monte Carlo algorithms and diagnostic methods to assess convergence of the algorithms. The LearnBayes and rethinking R packages are used to illustrate MCMC fitting by the use of gibbs sampling and Metropolis algorithms. Larger Bayesian models will be fit using JAGS and Stan and the accompanying rjags, rstanrm, and brms packages.

Jim Albert, PhD
Distinguished University Professor, Department of Mathematics and Statistics
Bowling Green State University

Please register.