Second Year Courses

Year 2 Courses
Each course is given over one 10-week quarter.

Biostatistics I : W. Christopher Mathews, M.D., M.S.P.H.; Simon Frost, Ph.D.

Objectives : Scholars will understand principles of measurement of clinical data, recognize data types, and correctly identify statistical methods appropriate for analysis of a given clinical data set. They will gain experience in assembling a clinical dataset in formats suitable for analysis by NCSS or other comparable statistical packages. They will learn skills to conduct graphical and numerical exploratory data analysis, comparative tests of categorical, ordinal, and continuous data, elementary probability theory, hypothesis testing and interval estimation, sample size calculation and power analysis.

Course Content:

Topic	Content
Introduction	Demonstration of NCSS/PASS
Data presentation	Graphical and tabular methods
Probability	Elementary probability theory
Sampling Distributions	Applications to statistical inference
Statistical Inference	Hypothesis testing and interval estimation; Type I and II errors, one and 2-sided tests
Inference regarding one or two means	Independent and matched t-tests
Non-parametric methods	Independent and matched data
Inference regarding Proportions	Contingency table analysis; binomial approximation to z-distribution; exact tests
Diagnostic test evaluation	Sensitivity, specificity, predictive values, likelihood ratios, ROC curves

Biostatistics II : W. Christopher Mathews, M.D., M.S.P.H.; Simon Frost, Ph.D.

Objectives : Scholars will understand and conduct more advanced biostatistical analyses including: ANOVA, multiple linear and logistic regression, survival analysis, and Cox proportional hazards modeling. The scholar will also be familiar with person-time rate analysis with Poisson regression and develop a conceptual understanding of major multivariate methods. Quantitative aspects of decision analysis and cost-effectiveness analysis will be covered. Analysis of survey research data will focus on measures of reliability and validity and on sampling designs.

Course Content:

Topic	Content
Analysis of Variance	1- and 2-way ANOVA
Correlation and simple linear regression	Measures of association; concepts and procedures
Multiple Linear Regression	Continuous and indicator predictor variables
Logistic Regression	Odds ratios, model discrimination and calibration
Survival Analysis	Life tables, Kaplan-Meier, log rank tests, Cox proportional hazards model
Person-Time Rate Analysis	Poisson regression
Other multivariable methods	Factor analysis, discriminant analysis, cluster analysis, MANOVA, CART
Clinical Decision Analysis	Setting up and folding back trees, sensitivity analysis, cost-effectiveness analysis
Analytic methods in survey research	Reliability, Validity, Sampling designs

Health Services Research: Theodore Ganiats, M.D.

Objectives : Scholars will evaluate relevant outcomes in patient-oriented research from the patient (quality of life) and societal (economic) perspectives and locate potential resources for assessing the relevant outcomes in a wide variety of study designs. They will also be able to describe the relative strengths of different health services research approaches to a clinical problem. Finally, they will understand the components of clinical practice guidelines, including patient preferences, and how these guidelines both depend upon as well as inform patient-oriented research.

Course Content:

Topic	Content
Evidence-based medicine	Fundamentals of EBM; Reading the literature; Levels of evidence
Survey Research	Types of survey questions, Developing surveys Measurement principles
Patient Safety	Safety research; JCAHO safety goals
Costs	Types of costs, Costs vs. charges, Data collection Incremental & marginal costs, Types of analyses Perspective
Qualitative Research	Principles of qualitative research, How to perform
Quality of Life Measurement	Quality of life instruments, Patient preferences Measuring QOL
Guidelines and Quality	Level of evidence/ strength of recommendation Practice guidelines, Patient preferences Quality Improvement/ Quality Assurance
Cost-effectiveness Cost-effectiveness	Theoretical foundation, Assumptions, Practical overview
Effectiveness Research	Disease reservoir, Practice variation

Data Management & Informatics: Les Lenert, M.D.

Objectives: This module provides an orientation to database design and management and covers key issues regarding data handling for clinical research and clinical trials.

Course Content:

Topics

Overview - Principles of FDA Good Clinical Practice in clinical research

Rational Forms Design

Principles of Database Design for Clinical Research

Data Confidentiality, Security and HIPAA

Using the Internet for clinical research

Specialized Data Management Technologies

Bioinformatics Tools & Methods

Information Technology Assessment methods

Creating data management plan

Review and critique of data management plans