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The first in a two-course series designed for students who seek to develop skills in biostatistical reasoning and data analysis. Offers an introduction to basic concepts and methods of statistics as applied to diverse problems in the health sciences. Methods for exploring and presenting data; direct and indirect standardization; probability; hypothesis testing; interval estimation; inference for means and proportions; simple linear regression, etc. Statistical computing is fully integrated into the course.

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The first in a series of two-half semester courses on regression methods, designed for students who seek to develop biostatistical reasoning and data analysis skills. This course provides an introduction to multiple linear and logistic regression models as applied to diverse problems in the health sciences. BC 203 or equivalent is a prerequisite.

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The second course in the sequence on Introductory Biostatistics methods. This course will focus on regression methods (multiple linear regress, ANOVA, ANCOVA) and their natural extensions such as Logistic and Poisson regression in applications to diverse problems in the health sciences. Additionally, this course will cover regression methods for time to event data such as Cox regression for survival data. BC 203 or equivalent is a prerequisite.

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Intensive first course in biostatistical methodology, focusing on problems arising in public health, life sciences, and biomedical disciplines. Summarizing and representing data; basic probability; fundamentals of inference; hypothesis testing; likelihood methods. Inference for means and proportions; linear regression and analysis of variance; basics of experimental design; nonparametrics; logistic regression.
Pre-requisites:: MA 10 or equivalent. Open to advanced undergraduates with permission.

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Applied multivariate statistics, presenting a unified treatment of modern regression models for discrete and continuous data. Topics include multiple linear and nonlinear regression for continuous response data, analysis of variance and covariance, logistic regression, Poisson regression, and Cox regression. Primarily for graduate students and advanced undergraduates.
Pre-requisites:: BC 213 or equivalent and working knowledge of matrix algebra.
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First of two courses that provide a comprehensive introduction to the theory of modern statistical inference. BC 257 presents a survey of fundamental ideas and methods, including sufficiency, likelihood based inference, hypothesis testing, asymptotic theory, and Bayesian inference. Measure theory not required.
Pre-requisites:: MA 12, MA 161, and either AM 165-66 or BC 213-16.

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Surveys the state of the art in Bayesian methods and their applications. Discussion of the fundamentals followed by more advanced topics including hierarchical models, Markov Chain Monte Carlo, and other methods for sampling from the posterior distribution, robustness, and sensitivity analysis, and approaches to model selection and diagnostics. Features nontrivial applications of Bayesian methods from diverse scientific fields, with emphasis on biomedical research.
Pre-requisites:: AM 165-166, BC 213-216, or equivalent.

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This sequence of two courses provides a comprehensive introduction to the theory of modern inference. BC 258 covers such topics as non-parametric statistics, quasi-likelihood, resampling techniques, statistical learning, and methods for high-dimensional Bioinformatics data.
Pre-requisites:: BC 257 or equivalent.

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Investigates theory and methods for drawing inference from discrete categorical data, including contingency tables, measures and tests of association, sampling distributions, goodness-of-fit, and both large- and small-sample inference. Other topics include modeling binary, ordinal, and multinomial data; repeated measures; and matched pair study designs.
Prerequisites:: PHP2510 and familiarity with statistical inference at AM 165-166 level.

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Generalized linear models provide a unifying framework for regression. Important examples include linear regression, log-linear models, and logistic regression. GLMs for continuous, binary, ordinal, nominal, and count data. Topics include model parameterization, parametric and semiparametric estimation, and model diagnostics. Methods for incomplete data are introduced. Computing with modern software is emphasized.
Prerequisites:: AM 167, BC 216.

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Comprehensive overview of methods for inference from censored event time data, with emphasis on nonparametric and semiparametric approaches. Topics include nonparametric hazard estimation, semiparametric proportional hazards models, frailty models, multiple event processes, with application to biomedical and public health data. Computational approaches using statistical software are emphasized.
Prerequisites:: Intermediate-level courses in biostatistics: BC 213, 216 or equivalent.

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Comprehensive coverage of methods for drawing inference from longitudinal observations. Theoretical and practical aspects of modeling, with emphasis on regression methods. Topics include: multilevel and marginal models; estimation methods; study design; handling dropout andnonresponse; methods for observational data (e.g. time-dependent confounding, endogeneity, selection bias). SAS and S-Plus software are used.
Prerequisites:: Statistical inference (AM 165- 166 at minimum), regression (BC 216), working knowledge of matrix algebra (e.g. MA 52).

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Systematic overview of modern statistical methods for handling incomplete data and for drawing causal inferences from "broken experiments" and observational studies. Topics include modeling approaches, propensity score adjustment, instrumental variables, inverse weighting methods and sensitivity analysis. Case studies used throughout to illustrate ideas and concepts.
Prerequisites:: BC 216; MA 161, familiarity with object-oriented programming (e.g. R, S-Plus, Matlab).

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Introduction to statistical concepts and methods used in selected areas of bioinformatics. Course is organized in three modules, covering statistical methodology for: (a) gene expression studies, with emphasis on DNA microarray data, (b) proteomics studies, (c) analysis of biological sequences. Succinct discussion of biological subject matter will be provided for each area. Available software will be introduced.
Prerequisites:: Statistics background at the level of BC 213-216 or BC 203-207/8.

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We will examine the modern clinical trial as a methodology for evaluating interventions related to treatment, rehabilitation, prevention and diagnosis. Topics include the history and rationale for clinical trials, ethical issues, study design, protocol development, sample size considerations, quality assurance, statistical analysis, systematic reviews and meta-analysis, and reporting of results. Extensively illustrated with examples from various fields of health care research. Prerequisites: introductory epidemiology and statistics.
Pre-requisites:: PHP2500 and PHP2510.

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Seminars and topics course on advanced methods or applications of biostatistics, or new and innovative research. Pre-requisites: Typically intended for advanced PhD students in biostatistics, public health, and fields where advanced methods are directly applicable.
Prerequisites:: Typically will include BC 213 and 216 at minimum.