Research.Com’s Top Mathematic Research Scientists

Research.Com's Top Mathematic Research Scientists: LJ Wei, Xihong Lin, James Robbins

The Biostatistics Department is proud to be home to a number of  faculty rated by as  among the top mathematics research scientists, on both a national and global level.  A report with the annual ranking aims to increase the online visibility of both existing and rising mathematics experts.  We are particularly proud to recognize the research and contributions of LJ WeiXihong Lin, and James Robins, who are all included in the list.

Dr. Wei’s research is in the area of developing statistical methods for the design and analysis of clinical trials. In 1977-78 he introduced the “urn design” for two-arm sequential clinical studies. This design has been utilized in several large-scaled multi-center trials.  Dr. Wei has developed numerous methods for analyzing data with multiple outcome or repeated measurements obtained from study subjects. In particular, his “multivariate Cox procedures” to handle multiple event times have become quite popular. Currently, Dr. Wei and his colleagues are developing graphical and numerical methods for checking the adequacy of the Cox proportional hazards model, other semi-parametric survival models, parametric models, and random effects models for repeated measurements.

Dr.  Lin’s research interests lie in the development and application of scalable statistical and machine learning methods for the analysis of massive and complex genetic and genomic, epidemiological and health data. Some examples of her current research include analytic methods and applications for large scale Whole Genome Sequencing studies, biobanks and Electronic Health Records, techniques and tools for whole genome variant functional annotations, analysis of the interplay of genes and environment, multiple phenotype analysis, polygenic risk prediction and heritability estimation.

Dr. Robins is  best known for advancing methods for drawing causal inferences from complex observational studies and randomized trials, particularly those in which the treatment varies with time. In 1986, Robins introduced a new framework for drawing causal inference from observational data. He showed that in non-experimental data, exposure is almost always time-dependent, and that standard methods such as regression are therefore almost always biased. Robins described two new methods for controlling for confounding bias, which can be applied in the generalized setting of time-dependent exposures: the G-formula and G-Estimation of Structural Nested Models. Later, he introduced a third class of models, Marginal Structural Models, in which the parameters are estimated using inverse probability of treatment weights. He has also contributed significantly to the theory of dynamic treatment regimes, which are of high significance in comparative effectiveness research and personalized medicine.