Ronghui (Lily) Xu
Recent Research Highlights
With the emergence of biomedical big data,
apply
machine learning methods to predict, as well as develop statistical
inference, for complex data type such as competing risks of cancer versus
noncancer mortality, in the presence of high dimensional covariates.
causal
inference methodology using propensity scores, instrumental variables, principal stratification, mediation or path specific analysis, for
complex data type such as in the above, or for rare events in pregnancy
safety data.
See CV link on left for more information.
Teaching
Useful books and book chapters
High Dimensional Data Analysis in Cancer Research (ed: Li and Xu). Springer 2008.
Explained variation in proportional hazards regression; in: Handbook of Statistics in Clinical Oncology (ed: Crowley and Hoering). CRC Press/Francis&Taylor Group, 2012.
Goodnessoffit in survival analysis; in: Encyclopedia of Biostatistics (ed: Armitage and Colton, Vol.4). Wiley, 1998
Robustness in proportional hazards regression; in: Handbook of Survival Analysis (ed: Klein et al.). CRC Press/Francis&Taylor Group, 2014.
R Packages on CRAN
By colleagues I worked with:
phmm: inference under the proportional hazards mixedeffects model.
TimeVTree: Cox model with timevarying coefficients using a treebased approach.
CoxR2: an information theoretical Rsquared type measure under the Cox model.
R2Addhaz: an R2 measure of explained variation under the additive hazards model.
tsriadditive: an instrumental variable approach (2SRI) for survival and competing risks outcomes under the additive hazards model.
survSens: sensitivity analysis with respect to unmeasured confounding for survival and competing risks outcomes.
CompetingRisk: confidence intervals for the cumulative incidence function (CIF) based on proportional causespecific hazards modeling.
cmprskcoxmsm: inference on risk (CIF) difference and risk ratio under the Cox marginal structure model for competing risks data.
