"The rigorous training provided by the ISP stimulates students' ideas for building powerful AI systems for biomedical informatics."
Ye Ye, student
University of Pittsburgh Graduate School of Public Health
Department of Biostatistics
BIOST 2042 Introduction to Statistical Methods II
3 Credit Hours
Spring Term, 2009
Instructor:
John W. Wilson, Ph.D.
Department of Biostatistics and NSABP Biostatistical Center
350 Sterling (201 N. Craig St., Suite 350), 412 / 3831648
A437 Crabtree, 412 / 624  3011
Office Hours: Monday and Wednesday, 9 – 10:30am
www.biostat.pitt.edu/wilson.htm
Teaching Assistant:
Nathan Pugh
A432 Crabtree Hall , 412 / 624  3063
Office Hours: Tuesdays 3 – 4:30 pm and Thursdays 9:30 – 11am
Class Meetings:
Monday and Wednesday, 11:00 – 12:15
G23 Parran Hall
First meeting: 5 January 2009
Last meeting: 22 April 2009 (Final Exam)
Course Summary:
BIOST 2042 continues the introduction to applied biostatistical methods begun in BIOST 2041 (Introduction to Statistical Methods I). The course presents aspects of univariate regression, 1 and 2way analysis of variance, analysis of covariance, categorical analysis, and survival analysis.
Prerequisite:
BIOST 2041 or equivalent introduction to statistical methods.
Teaching / Learning Objectives:
Upon completion of this course, students should
1) understand basic regression analysis, analysis of variance, analysis of covariance, and survival analysis.
2) further their understanding of categorical data analysis.
3) use statistical software to analyze data sets illustrating the above statistical methods.
4) recognize when these methods are and are not applicable.
Text:
Fundamentals of Biostatistics by Bernard Rosner, 6^{th} edition.
Make sure you obtain a copy with a CD!
Supplemental Material:
The CD that comes with the text has supplemental problems with workedout answers. As the course proceeds, I will list appropriate supplemental problems on the course website and I encourage you to look through these problems. They offer additional practice if you’re feeling uncertain of your understanding of a given topic and the problem answer details can help you see how to solve future problems of the same type.
Statistical Software:
The homework assignments will require a statistical package for computations. The “official” package supported in this course is SAS, which is available on the machines at the computer labs on campus. Pitt students, staff, and faculty can obtain a 1year copy of SAS from the software licensing service in Bellefield Hall.
It is not strictly necessary to use SAS for the homework assignments. Stata, SPlus, R, NCSS, SPSS, Systat, and other packages will also perform the required procedures. Be aware, however, that the course instructor is most familiar with Stata and SAS and can be relied on to answer questions about those packages only. So, for instance, if you use SPSS, you will have to find another source of information about that package’s syntax, etc.
Excel and Instat are not recommended because they do not include all the procedures that will be required in BIOS 2042 and many research settings.
Course Requirements and Grading:
There will be midterm and final exams and approximately 8 homework assignments. The contribution of each of these assessments toward the final grade will be as follows:
1/3 Homework
1/3 Midterm Exam
1/3 Final Exam
One homework assignment will be dropped from your homework grade. In other words, if there are 8 assignments, the best 7 will contribute toward your homework grade. This will make it possible not to be penalized if you cannot turn in an assignment when it is due (because of professional meeting, illness, etc.).
We strongly prefer to receive the homework on paper. However, if you are unable to attend class when an assignment is due, you may send the assignment via email.
Exams and homework will cover material presented in class only. Rosner’s text presents far more material than could ever be covered in a onesemester class. Although you should read the sections of Rosner that pertain to a given unit/class (see reading assignments in the tentative class schedule), you are not responsible for Rosner material that was not discussed in lecture.
All “for credit” grades will be letter grades only (A, B, C, D, F).
Course web site:
This course will make use of Blackboard, which is accessible on courseweb.pitt.edu (Log in and click the link to BIOST 2042.). Lecture notes, homework assignments, supplemental materials, and other information will be posted on this website.
Blackboard requires a “pitt.edu” username to log in and any courserelated mail will be sent to this address only. If you wish your course email to be forwarded to another account, open accounts.pitt.edu and set your forwarding address. Otherwise, you will not be alerted to course news, such as corrections of typos in lecture notes or homework, lastminute class cancellation, etc.
Some Concrete Suggestions for Success in the Course:
Accommodation for Students with Disabilities:
If you have a disability for which you may require accommodation, you are encouraged to contact both your instructor and Disability Resources and Services, 216 William Pitt Union (412.648.7890 or TTY 412.383.7355), as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course. A comprehensive description of the services of that office can be obtained at www.drs.pitt.edu.
Academic Integrity:
All students are expected to adhere to the standards of academic honesty. Any work submitted by a student must represent his/her own intellectual contribution and efforts. Any student found to be engaged in cheating, plagiarism, or any other acts of academic dishonesty will be subject to a failing grade in the assignment and/or the course and to further disciplinary action.
Other course policies:
Tentative Schedule of Sessions, Reading Assignments, and Homework Assignments:
Dates (2009) 
Topics and Reading Assignments 
Homework Assignments 
5 January

Unit 1a. Introduction to regression & general concepts (§ 11.1)


5 & 7 January

Unit 1b. Definition and point estimation of regression parameters (§ 11.2 – 11.3)

HW 1 assigned (7 Jan.) 
7 & 12 January

Unit 1c. Interval estimation for intercept and slope and hypothesis testing for 0 slope.


12 January

Unit 1d. Analysis of Variance Table and R^{2}. 

14 January

Unit 1e. Confidence and prediction intervals (§ 11.4 – 11.5).

HW 1 due, HW 2 assigned (14 Feb.) 
14 & 21 January

Unit 1f. Assessing regression assumptions (§ 11.6)

HW 2 due 
21 January

Unit 1g. Outliers and influence. (§ 11.6)


26 January

Unit 1h. Nonparametric regression. 
HW 3 assigned 
26 & 28 January

Unit 1i. Indicator variables in regression 

Tentative Schedule (continued)
Dates (2009) 
Topics and Reading Assignments 
Homework Assignments 
2 & 4 February

Unit 2. Introduction to correlation. (§ 11.7  11.8, 11.12) 
HW 3 due, HW 4 assigned (2 Feb.) 
9 February

Review. 
HW 4 due 
11 February

Midterm Exam


16 February

Unit 3a. Oneway analysis of variance (§ 12.1 – 12.3).


18 & 23 February

Unit 3b. Exploration of group differences. Linear contrasts.

HW 5 assigned (18 Feb.) 
25 February & 2 March

Unit 3c. Multiple comparisons (§ 12.4). 
HW 5 due (25 Feb.) 
4 March

Unit 3d. Randomized Blocks 
HW 6 assigned 
16 & 18 March

Unit 3e. Twoway analysis of variance (§ 12.6). 
HW 6 due (18 March) 
18 & 23 March

Unit 3f. Fixed, random, and mixed effects models. 

25 March

Unit 3g. Assessment of ANOVA assumptions. 
HW 7 assigned 
Tentative Schedule (continued)
Dates (2009) 
Topics and Reading Assignments 
Homework Assignments 
25 March

Unit 3h. ANOVA as regression with indicator variables. 

30 March

Unit 3i. Nonparametric ANOVA (§12.7) 

1 – 6 April

Unit 4a. Stratified categorical data (§ 13.3 – 13.5) 
HW 7 due (1 April) 
8 April

Unit 4b. Ordered categorical data 
HW 8 assigned 
13 April

Unit 5a. Introduction to survival and censored data (§ 14.8).


15 April

Unit 5b. KaplanMeier survival curves and logrank tests (§ 14.9 – 14.10). 
HW 8 due 
20 April

Review 

22 April

Final Exam 

Copyright 2009–2018  Send feedback about this site