Stewart J Anderson

BIOST 241, 2041 Introduction to Statistical Methods 1
A survey course in applied statistics taught to first year graduate students in biostatistics, public health and to postgraduate medical fellows. Covered descriptive statistics, probability (axioms, counting, conditional probability, Bayes' theorem, normal, uniform, binomial and Poisson distributions), estimation and confidence intervals, one sample and two sample hypothesis testing for normally distributed data and for proportions, general introduction to one-way ANOVA, contingency tables, simple linear regression, correlation, and statistical packages (Minitab, SAS, R). Texts used: Fisher and van Belle Biostatistics: A Methodology for the Health Sciences, 1993. Rosner, Fundamentals of Biostatistics, 5th ed., 1999. Also, extensive class notes were distributed. I taught this class every fall from 1989 to 1997 and then again in summer 2006.

BIOST 242, 2042 Introduction to Statistical Methods 2
The second part of the sequence in biostatistics (complements BIOST 2041). Covered nonparametric methods, multiple linear regression, odds ratio, relative risk, logistic regression, Mantel-Haenszel procedure, methods in survival analysis (Kaplan-Meier, life table analyses), multiple comparisons procedures, general linear contrast, multi-way ANOVA (general fixed effects model, factorial design, interactions, randomized blocks, random effects models, repeated measures) and analysis of covariance. Texts used: Fisher and van Belle Biostatistics: A Methodology for the Health Sciences, 1993; Rosner, Fundamental of Biostatistics, 5th ed., 1999. Also, extensive class notes distributed via the internet. From these notes, I published a text book; Anderson, SJ. Biostatistics: A Computing Approach, Taylor & Francis Group, LLC, Boca Raton, FL, 2011. ISBN 978-1-58488-834-5.. I taught this course every spring from 1990 to 2002 and again, in Spring 2015. I will teach this again in spring 2016.

BIOST 2052 Multivariate Analysis
This course covers advanced topics in multivariate analysis including the multivariate normal distribution, estimation of the mean vector and the covariance matrix, distributions and uses of simple, partial and multiple correlation coefficients, the generalized T² statistic, the distribution of the sample generalized variance, multivariate analysis of variance and applications of multivariate methods to repeated measures analysis, factor analysis or discriminant analysis. The emphasis in the beginning of the course is on theory. Later, applications and computational methods are emphasized. Students are required to prepare a project on an application area which requires them to summarize and reproduce results from the literature. Texts used or referred to in the course include Anderson TW. An Introduction to Multivariate Statistical Analysis, 3rd edition, 2003; Mardia, K.V., Kent, JJ and Bibby, JM. Multivariate Analysis,Academic Press, 1979; Khattree, R. and Naik, D.N. Applied Multivariate Statistics with SAS Software, 2nd edition, SAS Institute, Inc, Cary, NC, 1999; and Searle, S.R. Matrix Algebra Useful for Statistics, Wiley & Sons, 1982. In addition to the texts, extensive class notes are distributed by the instructor. I taught this course roughly every other fall from 1999 to 2012.

BIOST 2096 (formerly BIOST 2075) Numerical Methods in Biostatistics
The purpose of this course is to familiarize the student with a wide range of numerical methods which are useful in biostatistical research. Selected computational techniques used in statistical research will be covered in the course. Some background will be provided to facilitate understanding of a few numerical algorithms widely used in statistics. The following topics are covered: recurrence relations, power series and asymptotic expansions, generating pseudo-random deviates, basic simulation methodology, use of antithetic variables, importance sampling, solutions of nonlinear equations, Newton’s method, vector and matrix norms, linear regression and matrix inversion, other topics in multivariate analysis, splines, finite-state Markov chains, Hastings-Metropolis algorithms and Gibbs sampling. The goal of this course is for the student to gain knowledge useful in understanding and developing statistical methodology. Upon completion of this course, the student should have a clear understanding of how to conceptualize and implement computer simulations useful for doing statistical or other quantitative research. The primary texts used for this course are Lange, Numerical Analysis for Statisticians, Second edition, Springer-Verlag, 2010 and Ross, Simulation, Fifth Edition, Academic Press, 2013. Extensive class notes are distributed. As prerequisites, students should have had one year of courses in mathematical statistics and some experience with at least one computer language (e.g., Fortran, C, Pascal or Java) and some knowledge of one or more of the following statistical packages: R, SAS, S-plus, Stata, Matlab, Mathematica. I've taught this course once every 2 years from 2003 to 2013.

Doctoral Students

1. Joanne Beer, Ph.D. (co-advisor with Rob Krafty) – Graduated August 2018. Dissertation: “Predicting clinical variables from neuroimages with fused sparse group LASSO” (Winner, best dissertation)
2. John Pleis, Ph.D. – Graduated August 2018. Dissertation: “Mixtures of discrete and continuous variables: considerations for dimension reduction”
3. Abraham Apfel, Ph.D. –Graduated June 2017.  Dissertation: “Stability analysis of sparse K-means”
4. Andrew Potter, Ph.D. – Graduated April 2017. Dissertation: “Multiscale multivariate functional principal component analysis with an application to multivariate longitudinal cardiac signals”
5. Jia-Yuh (Lily) Chen, Ph.D. – Graduated June  2016.  Dissertation: “Joint modeling of bivariate longitudinal and survival data in spouse pairs”
6. Beth Zamboni, Ph.D. – Graduated April 2015.  Dissertation: “Twisted survival: identifying surrogate endpoints for mortality using QTWIST and conditional disease free survival
7. Xiaoxue Li, Ph.D. – Graduated December 2014.  Dissertation: “Time-varying coefficient gap time models for Ecological Momentary Assessment data”.
8. Yun Ling, Ph.D. – Graduated April 2014.  Dissertation: “Influential observation detection for multilevel multivariate growth curve models”
9. Ji-in Choi, Ph.D. – Graduated August 2012.  Dissertation: “Prediction in the joint modeling of mixed types of multivariate longitudinal outcomes and a time-to-event outcome"
10. Yoko Tanaka, Ph.D. – Graduated December 2010.  Dissertation: "An adaptive two-stage dose-response design method for establishing proof of concept in drug development"
11. Folefac Atem, Ph.D. – Graduated August 2010. Dissertation: “Rationale for choosing an explicit correlation structure in a multilevel analysis with bivariate outcomes”
12. Xing Yuan, Ph.D. – Graduated December 2009.  Dissertation: “A meta-analytic framework for combining incomparable Cox proportional hazard models caused by omitting important covariates”. Winner best dissertation in Department of Biostatistics
13. Meredith (Lotz) Wallace, Ph.D. – Graduated August 2009.  Dissertation: “Modeling missing covariate data and temporal features of time-dependent covariates in tree-structured survival analysis”
14. Fiona Callaghan, Ph.D.  (Co-chaired with Joyce Chang) – Graduated April 2008.  Dissertation: “Classification trees for survival data with competing risks"
15. L. Scott Dean, Ph.D. – Graduated April 2007.  Dissertation: “A method for detecting optimal splits over time in survival analysis using tree-structured models”
16. Jia Li, Ph.D. – Graduated August 2006.  Dissertation: “A strategy for stepwise regression procedures in survival analysis with missing covariates”
17. Feng-shou Ko, Ph.D. – Graduated August 2006.  Dissertation: “Identification and assessment of longitudinal biomarkers using frailty models in survival analysis”
18. Michael B. (Brent) McHenry, Ph.D. – Graduated June 2004.  Dissertation: “New estimation approaches in survival analysis with Aalen’s additive risk model”.  Winner of two ASA student travel awards.
19. Mary Kelley, Ph.D. – Graduated December 2003.  Dissertation: “Zero inflation in ordinal data: applications of a mixture model”
20. Maria K. Mor, Ph.D. – Graduated April 2003.   Dissertation: “A Bayesian group sequential approach for multiple endpoints”
21. Yanming Yin, Ph.D. – Graduated April 2002.  Dissertation: “Tree-structured modeling for interval-censored survival data”
22. Wei Tian, Ph.D. - Graduated June, 1999. Dissertation: “Markov chain models for analyzing multivariate repeated categorical data with incomplete observations
23. Sang Ahnn, Ph.D. – Graduated December 1994. Dissertation: "Sample size determination for comparing more than two survival distributions". 
24. Lingshi Tan, Ph.D. – Graduated April 1993. Dissertation: "A multivariate growth curve model with random effects and CAR(1) errors", winner of a 1994 Biometrics Society (ENAR) student travel award