Advanced Biostatistical Methods

 Biostatistics

 

 

faculty-icon  Dr. Joseph Gardiner

 

Improving Diabetic Patients’ Adherence to Treatment and Prevention of Cardiovascular Disease

An NHBLI-sponsored study led by Dr. Ade Olomu, seeks to deliver an intervention to improve medication adherence with the goal of prevention of cardiovascular disease in patients with diabetes. The study adopts a cluster randomized design wherein at least 12 practice teams at federally qualified health centers in Alcona, Lansing and Saginaw will engage low SES population over a 12-month intervention. The control condition is the standard of care supplemented with patient education. The intervention employs best practices in delivering preventive services and meeting selected health care goals through a customized 15-week program of text-messages. The 5-year project will enroll over 300 patients. Dr. Joseph Gardiner serves as co-investigator and Study Biostatistician on the research team that includes experts in clinical medicine, healthcare communication, medical anthropology, and health services research.  

 

Treating Brain Swelling in Pediatric Cerebral Malaria

An NIAID-sponsored study lead by Dr. Terrie Taylor, seeks to examine strategies to treat children with cerebral malaria (CM). Malawian children with CM and severely increased brain volumes on screening brain magnetic resonance imaging with be randomized to one of three study arms: standard of care (SOC); SOC supplemented by intravenous hypertonic saline; SOC supplemented by early intubation and mechanical ventilation. The primary outcome is failure of the first treatment to which the child is assigned or death, whichever comes first. Practical considerations in conducting the study, and the annual 6-month malaria season were incorporated in a group sequential trial design with early stopping for either efficacy or futility. Dr. Joseph Gardiner serves as co-investigator and Study Biostatistician on this study.

 


 

 

faculty-icon  Dr. Chenxi Li

 

Genetic/genomic survival association and risk prediction

Most of the genetic association studies of human diseases use case-control phenotypes (diseased vs. non-diseased). However, time-to-disease traits are more informative for the gene-disease association and are more suitable for building risk prediction models. We are developing robust and efficient statistical methods to detect genetic associations and predict disease risks with omics data for various types of survival outcomes and models. This project is led by Dr. Chenxi Li.