David Todem, MSPH, PhD is an Associate Professor of Biostatistics in the Department of Epidemiology & Biostatistics, and an Adjunct Associate Professor in Statistics & Probability at Michigan State University (MSU). His current line of research focuses on the development of statistical methods to analyze data generated from populationbased studies pertaining to Dental Caries, Cancer and Alzheimerís disease. Specifically, his primary research interests in methods evolve around the following topics: 1) Models for longitudinal, clustered and functional data; 2) Joint models for (multi-state) time-toevent endpoints and longitudinal outcomes; 3) Inferential techniques for non- and weakly-identifiable models with application to informative nonresponse; and 4) Testing procedures for evaluating non-negative heterogeneity parameters with application to mixture models. One interesting application of longitudinal and multi-state models is the study of temporal patterns of promising biomarkers and brain imaging parameters to understand the pre-clinical stages of Alzheimer's disease and its progression over time. This methodological work is motivated by the Alzheimer's disease neuroimaging initiative (ADNI1, ADNI-GO and ADNI2) study, a large public database well suited for longitudinal investigations on AD, due to its breath of serial data on biomarkers and cognitive markers. Another interesting application is in cancer research involving longitudinal data on quality of life endpoints for patients during and post treatment. Models for serial data on quality of life outcomes are critically important in understanding the effectiveness of a treatment regimen in cancer patients. This methodological research has expanded to include methods for missing data, given the very nature of longitudinal studies to generate dropouts. Dr Todem is particularly interested in situations where the missing data process and attrition depend on the unobserved response. His analytical strategy has been to use a versatile approach that embeds the treatment of incomplete data in the context of sensitivity analysis that is informed by the subject matter. Within this framework, Dr Todem is particularly interested in developing methods for conducting inferences when the assumed working (non-ignorable) model is at best weakly identifiable in light of observed data. Dr Todem is also interested in various aspects of mixtures models which provide a natural framework to describe heterogeneity in a population. A prevailing concern for this class of models is whether the inherent heterogeneity is consistent with observed data. In this line of research, Dr Todem is particularly interested in developing testing procedures for evaluating homogeneity against varying heterogeneity, a non-standard problem arising in dental caries research and other applications in epidemiology and medical research. In addition to his methods research, Dr Todem collaborates actively with other researchers on various projects in epidemiology and medicine. Ongoing collaboration projects include: 1) a neuroimaging study on the neural development underlying childhood stuttering, 2) a study on Organochlorine and gene expressions of sex steroids in a multi-generational cohort, and 3) an intervention study aiming at evaluating a family-based cancer literacy model to increase participation in breast and cervical cancer control programs among medically underserved female population in the US.
Bio-statistical methods with applications to Dental Caries/Oral Health.