﻿ Personal Website of Dr. Mei-Cheng Wang

 Mei-Cheng Wang Home | Survival Analysis  (Biostatistics 140.641) — offered every year This intermediate-level course introduces fundamental concepts, theory and methods in survival analysis. The course emphasizes statistical tools and model interpretations which are useful in medical follow-up studies and in general time-to-event studies. The content includes hazard functions, survival functions, types of censoring and truncation, Kaplan-Meier estimates, log-rank tests and their generalization. Parametric models and inference include likelihood estimation and the exponential, Weibull, log-logistic and other relevant distributions. Statistical methods and theory for the proportional hazard model (Cox model) are discussed  in detail with extensions to time-dependent covariates. Clinical and epidemiological examples (through class presentations) will be discussed and illustrated with various statistical procedures in class and also through homework assignments.     Advanced Survival Analysis  (Biostatistics 140.741) — offered every other year   This advanced course introduces statistical models and methods useful for analyzing univariate and multivariate failure time data. It extends the course of Survival Analysis (Biostatistics 140.641) to topics on semi-transformation model, competing risks models, length-bias and prevalent samplings, multivariate and frailty survival models, models and methods for analyzing recurrent events data, and martingale theory for counting processes. Emphases are placed on nonparametric and semiparametric approaches for modeling, estimation and inferential results. Clinical and epidemiological examples are presented in class to illustrate statistical procedures.     Biomarkers, Risk Prediction and Precision Medicine (Biostatistics 140.742) — offered every other year and jointly taught by Chattergee N. and Wang MC   Statistical models are often used to predict or evaluate the probability that an individual with a given set of risk factors or biomarkers will experience a clinical or disease outcome. A risk prediction model can help in clinical decision making and help patients make an informed choice regarding their treatment. The predictive ability of a model is usually evaluated by the model’s ability to discriminate between low and high risk patients, and an assessment of calibration, that is, the agreement between observed outcomes and predicted outcome.   Part I of the course (Wang) will focus on characterization and estimation of numerous risks in relation to biomarker measurements and binary or time-to-event outcome. Lectures will be more toward method-theory discussion, with topics on relative risk, absolute risk, odds ratio and hazard ratio parameters in prospective and case-control studies.   Part II of the course (Chattergee) emphasized risk prediction with real data applications: Discovery of risk factors —> Characterization of relative risk —> Estimation of absolute risk —> Evaluation of model calibration —> Evaluation of public health utility (Chatterjee et al. 2016). Send Mail