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Year 2 Courses

Each course is given over one 10-week quarter. 

Biostatistics I : Florin Vaida, Ph.D.

Objectives: the students will develop and acquire the insight, tools and skills needed to be educated users and consumers of biostatistics.  They will recognize data types, and correctly identify the statistical methods appropriate for analysis of a given clinical dataset.  They will understand the basic concepts of statistics, including elementary probability theory, sampling, estimation, confidence intervals, and hypothesis testing.  They will be able to conduct graphical and numerical exploratory data analysis using SPSS, and to perform statistical analyses, including comparative tests of categorical and continuous data.

Course Content:



Data summaries          

Numeric and graphic data summaries in SPSS

Probability and normal distribution

Elementary probability theory; the normal distribution

Central limit theorem and confidence intervals

Sampling distributions and applications to statistical inference

Hypothesis testing for one group

Hypothesis testing: type I and II error, 1-sided and 2-sided tests; t-test for one group

Statistical inference for two groups

Paired and independent groups t-test; Wilcoxon rank-sum test; sample size calculation

Inference for proportions

Binomial distribution; one-sample z-test for proportions; McNemar’s test for paired samples

Inference for proportions, two groups

Comparing proportions in independent groups: z-test, Chi-square test, Fisher’s exact test. 

One-way ANOVA

One-way analysis of variance; F-test; adjusting for multiple post-hoc comparisons 

Two-way ANOVA

Additive and factorial two-way ANOVA; model selection 


Biostatistics II : Florin Vaida, Ph.D.

Objectives: the students will conduct more advanced regression-based statistical analyses, including: simple linear regression and correlation analysis; multiple linear regression; logistic regression; Cox proportional hazards models.  Issues of model diagnostics and analysis of residuals, model comparison and model building, and strategies for univariable and multivariable analyses will be discussed.  The analyses will be conducted in SPSS.

Course Content:



Correlation and simple linear

Correlation and simple linear regression

Inference for simple linear

Model formulation and estimation in simple linear regression; examination of residuals and model assumptions; prediction and explained variation

Multiple Linear Regression

multiple linear regression: model assumptions and interpretation; diagnostics;

Model building

model comparison and model building in multiple linear regression; polynomial regression; ANCOVA and ANOVA as multiple linear regression

Regression for proportions

Logistic regression for binary outcomes; model interpretation; comparing several groups

Logistic regression

Multiple logistic regression; model building and model comparison

Survival analysis

Kaplan-Meier curves and log-rank test

Cox proportional hazards

Regression methods for survival data: Cox proportional hazards model


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:




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

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 Manangment & Infromatics: Joe W. Ramsdell, 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:


  • 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