NATIONAL CHIAO TUNG UNIVERSITY

INSTITUTE OF STATISTICS

 

BIOSTATISTICS

Fall 2019

 

 

 


Instructor:

Guan-Hua Huang, Ph.D.

 

Office: 423 Joint Education Hall

 

Phone: 03-513-1334

 

Email: ghuang@stat.nctu.edu.tw

Class meetings:

Monday 9:00 – 12:00 at 406 Joint Education Hall

Office hours:

By appointment

Class website:

http://ghuang.stat.nctu.edu.tw/course/biostat19/

Credit:

Three (3) credits

 

COURSE SUMMARY

 

This course will cover statistical methods for design, conduct, and analysis of epidemiologic studies such as case-control and cohort studies. Topics include overview of designs for research studies; epidemiologic study design; rates and risk; measures of association; classical contingency table methods; logistic, polytomous logistic, proportional odds and Poisson regression; and proportional hazard regression. Examples from the literature and data from a number of well-known epidemiologic studies will be used for illustration of the concepts involved.

 

HANDOUTS AND TEXTBOOKS

 

Handouts corresponding to each lecture will be available on the class website before each class. There is no required textbook for this course. Following books are recommended for further reading:

 

Nicholas P. Jewell (2003). Statistics for Epidemiology. CRC Press.

 

Breslow NE, Day NE (1980). Statistical Methods in Cancer Research, Vol. I, The Analysis of Case-Control Studies. International Agency for Research on Cancer, Lyon.

 

Breslow NE, Day NE (1987). Statistical Methods in Cancer Research, Vol. II, The Design and Analysis of Cohort Studies. International Agency for Research on Cancer, Lyon.

 

Hosmer DW, Lemeshow S (1989). Applied Logistic Regression, John Wiley & Sons.

 

McCullagh P, Nelder JA (1989). Generalized Linear Models, 2nd edition, Chapman and Hall.

 

PREREQUISITES

 

Students are expected to be familiar with basic epidemiologic concepts.

 

METHOD OF STUDENT EVALUATION

 

The course grade will be based on four homework assignments (40%), one midterm exam (25%), and one final exam (35%).

 

COURSE OUTLINE

 

Readings refer to:

Nicholas P. Jewell (2003): Statistics for Epidemiology (Jewell),

Breslow NE, Day NE (1980): Statistical Methods in Cancer Research, Vol. I (Breslow & Day I), Breslow NE, Day NE (1987): Statistical Methods in Cancer Research, Vol. II (Breslow & Day II), Hosmer DW, Lemeshow S (1989): Applied Logistic Regression (Hosmer & Lemeshow),

McCullagh P, Nelder JA (1989): Generalized Linear Models, 2nd edition (McCullagh & Nelder).

 

Module

Topic

Reading

1

Introduction and overview of goals of epidemiologic research and epidemiologic study designs

Jewell

Chapter 1, 5

Breslow & Day I

Chapter 1

Breslow & Day II

Chapter 1

2

Measures of disease occurrence and association:

a.    Different rates

b.    Inferences for rates

c.    Comparing two rates

d.    2×2 table

e.    c2 test and Fisher’s exact test

f.     R×C table

g.    Relative risk and odds ratio

h.    Inferences for relative risk and odds ratio

Jewell

Chapters 2, 4, 6, 7

Breslow & Day I

Chapter 2, 4

3

Control of extraneous factors:

a.     Confounding and interaction

b.     Mantel-Haenszel method

c.     Analysis of matched data- McNemar’s test

d.     Measure of agreement- Kappa statistic

Jewell

Chapter 8, 9, 10, 11

4

Logistic regression:

a.     Concepts

b.     Model and assumption

c.     Interpretation of regression coefficients

d.     Inferences

e.     Likelihood ratio test

f.      Goodness-of-fit test

g.     Residual plot

Jewell

Chapter 12, 13

Hosmer & Lemeshow

Chapter 1, 2

5

Confounding and interaction in logistic regression:

a.     Modeling

b.     Assumption

c.     Interpretation

Jewel

Chapter 14

6

Logistic regression for contingency tables:

a.     Modeling of 2×2 table

b.     Modeling of 2×2×2 table

c.     Modeling of K×2 table

d.     Modeling of M×2×2 table

No reading

7

Logistic regression for case-control and matched data:

a.     Risk and odds ratio in case-control studies

b.     Logistic regression for case-control data

c.     When there are many strata with few observations in each

d.     Conditional logistic regression for matched data

Jewell

Chapter 16

Hosmer & Lemeshow

Chapter 7

8

Analysis of polytomous data:

a.     Model for nominal scales (polytomous logistic regression)

b.     Model for ordinal scales (proportional odds model)

McCullagh & Nelder

Chapter 5

9

Poisson regression for count data:

c.     Introduction to generalized linear model

d.     Poisson regression

e.     Application to cohort study

f.      Log linear model for contingency table

Breslow & Day II

Chapter 4

10

Analysis of survival data:

a.     Examples of survival data

b.     Survival, hazard and cumulative hazard functions

c.     Censoring and truncation

d.     Kaplan-Meier estimator

Breslow & Day II

Chapter 5

11

Proportional hazards model:

a.     Assumption of the model

b.     Testing and estimation

c.     Computing programs and interpreting results

d.     Time dependent covariates

Breslow & Day II

Chapter 5, 6