Perelman School of Medicine Events Calendar

Speaker: Darrell Kotton, M.D.  Affiliation: Boston University Center for Regenerative Medicine

Associate Professor

Pathology and Laboratory Medicine

 Shirley Zhang:
Homing of T cell progenitors to the thymus is compromised after transplant and in acute inflammation

Christelle Harly:
Distinct roles for transcription factor TCF-1 in early T cell progenitors and mature T cells

Reunion Hall, JMB

Speaker 1: Arjun Raj

Speaker 2: David Issadore

Student host: Paul Ryvkin

Location: TRC 1-149AB

Advisor: Dr. Jeffrey Golden

"Statistical methods for non-ignorable missing data with applications to quality-of-life data"

Kaijun Liao
PhD Candidate
Division of Biostatistics
Department of Biostatistics and Epidemiology

Dissertation Advisor: Andrea B. Troxel, ScD
Committee Chair: Mary E. Putt, PhD, ScD
Committee Members: Katrina Armstrong, MD, MSCE, Benjamin C. French, PhD

Abstract: In chronic disease studies, researchers increasingly use more and more survey studies, and design medical studies to better understand the relationships of patients, physicians, their health care system utilization, and their decision making process in disease prevention and management. Longitudinal data is widely used to capture disease progression or trends occurring over time. Each subject is observed as time progresses. A common problem is that repeated measurements are not fully observed due to missing responses or loss to follow up. However, in such medical studies, the sample sizes are limited due to restrictions on disease type, study area and medical information availability. Small sample sizes with large proportions of missing information are problematic for researchers trying to understand the experience of the total population. Data modeled without considering this missing information may cause biased results.

A first-order Markov dependence structure is a natural data structure to model the tendency of changes. First, we developed a Markov transition model in a full-likelihood based algorithm to provide robust estimation accounting for non-ignorable missingness, and applied it to data from the Penn Center of Excellence in Cancer Communication Research. Next, we extended the method to a pseudo-likelihood based approach by considering only pairs of adjacent observations to significantly ease the computational complexities of the full-likelihood based method. Finally, we build a two stage pseudo hidden Markov model to analyze the association between quality of life measurements and cancer treatments from a randomized phase III trial in brain cancer patients. By incorporating both selection models and shared parameter models with a hidden Markov model, this approach provides targeted identification of treatment effects. We outline procedures for parameterizing and estimating such models and apply it to the motivating data. Our model provides a simple framework for reducing the multi-dimensional integration in traditional non-ignorable missingness methods into one dimensional integration in the observed likelihood. In addition, the proposed models avoid the problem of specification of the correlation structure of repeated outcomes, instead emphasizing estimation in Markov chain parameters.