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
  • All members of the Harvard Catalyst community, but primarily geared toward biostatisticians
Session Dates
  • Varies; see below for details

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

Journal Club/Work in Progress

September 2017 Work-in-Progress

Title: Control of False Positives in Randomized Phase III Clinical Trials

Changyu Shen, PhD
Associate Professor of Medicine, Harvard Medical School
Lead Statistician in the Smith Center for Outcomes Research in Cardiology, BIDMC
September 27, 2017
Harvard T.H. Chan School of Public Health, Room 2-426

Please join us for this work-in-progress meeting. The leader of this meeting will be Changyu Shen, Associate Professor of Medicine, Harvard Medical School, and Lead Statistician in the Smith Center for Outcomes Research in Cardiology, BIDMC. Dr. Shen will be presenting research to discuss the control of false positives in randomized phase III clinical trials. The session is intended to be highly participatory with an active exchange of ideas.

Seminar Series

Statistical Learning of Dynamic Systems - a Direct Approach
October 16, 2017

No registration required.

Itai Dattner, PhD
Lecturer, Department of Statistics
University of Haifa

3:30pm - 5:30pm
Ballard Room 503
HMS Countway Library

Abstract: Dynamic systems are ubiquitous in nature and are used to model many processes in biology, chemistry, physics, medicine, and engineering. In particular, systems of (deterministic or stochastic) differential equations as well as discrete models are commonly used for the mathematical modeling of dynamic processes. These systems describe the interrelationships between the variables involved, and depend in a complicated way on unknown quantities (e.g., initial values, constants, or time dependent parameters). Modern dynamic systems are typically very complex: nonlinear, high dimensional, and only partly measured. Moreover, data may be sparse and noisy. Thus, statistical learning (inference, prediction) of dynamical systems is not a trivial task in practice.

In the first part of the talk we will present the direct integral method, a novel approach for estimating the parameters of systems of ordinary differential equations. We will discuss some theoretical results such as identifiability and consistency for both, fully and partially observed systems.

The second part of the talk will be concerned with applications of the direct method. We will consider examples from infectious diseases and biology. In particular, we will present a recent study where we experimentally monitored the temporal dynamic of a predatory-prey system and demonstrated the ability to obtain realistic parameter estimates given sparse and noisy data. Next, we will discuss the statistical learning of age-dependent dynamics which is an important characteristic of many infectious diseases. We examine the estimation of the so called next-generation matrix using incidence data of influenza-like-illness. Unlike previous studies, using our estimation method we do not have to assume any constraints regarding the structure of the matrix.


Short Courses