G652 Introduction to Biostatistics II by zzz22140


									                             G652: Introduction to Biostatistics II
                                          Fall 2006

Sujuan Gao, Ph.D.
RG 4143
Phone: 274-0820
Email: sgao@iupui.edu

Course Description and Objectives
G652 is an advanced biostatistics course designed for students with an interest in the health
sciences. Students are expected to have completed at least one semester course of basic
biostatistics. Knowledge of probability and probability distributions, concepts of estimation and
hypothesis testing are assumed. Topics covered in this course include multiple linear regression,
multi-factor analysis of variance, analysis of covariance, analysis of repeated measures, logistic
regression model, and survival analyses.

Upon completion of the course, students are expected to understand the appropriate statistical
models for various outcomes and be able to interpret results using statistical techniques covered
in this course. Students are also expected to conduct simple analyses using SPSS on personal

Principles of Biostatistics, 2nd Edition, by Pagano and Gauvreau
Lecture notes will be accessible from the following web site:


Meeting Time and Place
Tuesdays, 5:45 – 8:25 pm, Regenstrief 4th floor, Classroom (RG4147).

Office Hours
Thursday, 2:30 – 4pm.

Homework and Grading
There will be 12 homework assignments (10 best grades will be used for final grade), one
midterm test and one comprehensive final examination.

Class materials and homework assignments will be posted on Oncourse website. No late
homework will be accepted. The final course grade will be determined using the following
weighting scheme:
       homework       40%
       Mid term       30%
       Final exam     30%
Schedule of Lectures and Topics

Aug. 29      Review:
                   probability distributions, Estimating, hypothesis testing
                   Simple linear regression

Sept. 5      Multiple linear regression
                    Multiple correlation coefficient and interpretations
                    Inference, confounder bias
                    Polynomial terms, indicator variables, interactions

Sept. 12     Multiple linear regression
                    Model selection
                    Residual plots, outliers
                    Examining the assumptions
                    Transformation of predictors
                    Common misuse of regression models

Sept. 19     Analysis of Variance (ANOVA)
                    One factor ANOVA
                    Multiple comparisons

Sept. 26     Multi-factor ANOVA

Oct. 3       Analysis of Covariance (ANCOVA)
                    indicator variables

Oct. 10      Random effect models and analysis of repeated measures*
                   *excluded from mid-term examination

Oct. 17      Mid-term

Oct. 24      Logistic regression
                    2x2 tables and odds ratio

Oct. 31      Logistic regression
                    Parameter estimation and inference

Nov. 7       Diagnostic Tests
                   sensitivity and specificity
                   ROC curve

Nov. 14      Survival analysis
                 Censoring, survival function, hazard function.
                 Estimation of survival function and hazard function
                 (life-table method and Kaplan-Meier method)

Nov. 21   Comparing survival functions

Nov. 28   Cox’s Proportional hazard model

Dec. 5    Comprehensive review

Dec. 12   Final exam

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