Statistical Methods in Biomedical Engineering
BME 423
Syllabus - 2005 Fall Semester
1. Basic Information
Course:
Place and time:
Faculty:
Office:
Telephone
Email:
Office Hours:
TA
Grader
Final Exam:
Prerequisite:
Class web page:
Statistical Methods in Biomedical Engineering, BME 423, 3 units
ZHS 252, Fri: 2:00 am-4:45 pm (15min break ~ 3:15 pm)
David Z. D’Argenio, Ph.D.
Professor, BME
Denny Research Center, Suite 140, Room 156
740-0341
dargenio@bmsr.usc.edu
Th 3:00 to 5:00
Limei Cheng, DRB 140, limeiche@usc.edu
T 12:30 to 2:00; Th 11:00 to 12:30
Lisong Ai, DRB 140, lisongai@usc.edu
W 3:30 to 4:30
Friday December 9, 2:00-3:30 in ZHS 252
BME 210
http://totale.usc.edu/ (login and select BME 423)
2. Course Goals, Learning Objectives, and relationship to Program Outcomes
2.1. Goals: The overall goal of BME 223 is to introduce statistical methods for making
inferences in engineering, biology and medicine. You will learn a number of essential statistical
techniques for use in analyzing data from different types of experiments and you will apply them
to data from biological experiments and clinical studies. In the course you will learn about:
descriptive statistics; elementary probability; discrete and continuous random variables and their
distributions; hypothesis testing involving continuous and categorical (nominal and ordinal)
variables, two and more treatments; linear regression; analysis of survival data; design of clinical
trials.
2.2 Learning objectives and relationship to program outcomes: After successfully completing
this course, you should be able to:
 explain and apply descriptive statistics (including measures of central tendency and
dispersion) to summarize experiment data (BME Program Outcomes 1 and 3 - see last page);
 know what a random variable is (discrete and continuous), some common probability
distributions and how to calculate expected values (Outcomes 1 and 3)
 explain and implement methods for testing differences between treatment groups when the
measurements are continuous, including: F-test; unpaired and paired t-test; analysis of
variance; repeated measures ANOVA (Outcomes 1 and 3)
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 explain and implement methods for testing differences between treatment groups when the
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measurements are categorical, including: Chi square analysis; Mann Whitney test; KruskalWallis statistic; Wilcoxin signed-rank test; McNemar’s test (Outcomes 1 and 3).
describe and implement methods for assessing the relation between two variables using linear
regression analysis (Outcomes 1 and 3);
explain and implement analysis of survival data and tests for evaluating the effects of
treatments using survival data (Outcomes 1 and 9);
select and implement the appropriate statistical procedure needed for a particular experiment
paradigm, data collected and question to be answered (Outcomes 1 and 9);
design a clinical trial to test whether a treatment produces an effect (type of experiment,
number of subjects, etc.), considering ethical issues of human trials (Outcomes 2 and 4);
use the GLANTZ software to implement and perform the statistical tests presented in class
using biomedical (laboratory and clinical trial) data sets (Outcomes 1, 3 and 9);
prepare written reports that document solutions to homework projects and present
simulations results and analyses (Outcome 5);
supplement through independent study of reference readings, the statistical concepts
presented in class and their biomedical applications (Outcome 7).
3. Course Plan
The course plan detailed below reflects the course goals and the learning objectives. The lectures
emphasize different statistical methods, while the homework projects require an understanding of
the appropriate statistical procedure required as well as the procedure to perform the particular
test, given the experiment data and question posed. The class material is covered in the following
order:
Aug. 26:
Sept. 2:
Sept. 9:
Sept. 16:
Sept. 23:
Sept. 30:
Oct. 7:
Oct. 14:
Oct. 21:
Oct. 28:
Nov. 4:
Nov. 11:
Nov. 18:
Nov. 25:
Dec. 2:
Dec. 9:
Introduction; Descriptive Statistics
Basic Probability; Random Variables (HW#1 assigned)
Hypothesis Testing – Differences Between Groups
Hypothesis Testing – Special Case of Two Groups
Methods for Proportions & Nominal Variables (HW#1 due, HW#2 assigned)
Methods for Proportions & Nominal Variables
Confidence Intervals (HW#2 due)
Midterm Exam
Linear Regression (HW#3 assigned)
Linear Regression
Analysis of Variance for Multiple Treatments
Nonparamteric Methods (HW#3 due, HW#4 assigned)
Survival Analysis
Thanksgiving Vacation
Ethical Issues in Clinical Trail Design (HW#4 due)
Final Exam (2:00 - 3:30. ZHS 252)
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4. Textbooks:
Required:
Primer of Biostatistics (with software). Stanton A. Glantz, McGraw Hill,
5th or 6th edition, 2002/2005.
5. Grading:
Midterm Exam
Final Exam
Homework (4)
40%
40%
20%
All homework assignments are to be turned in at the beginning of class on the day they are due.
For every day (or portion of a day) an assignment is late, 25% will be subtracted from its
maximum point total. You may discuss strategies for solving the homework with your
classmates; however, outright plagiarism of other’s work will be treated following the
University’s guidelines for violations of academic integrity.
6. Web Page:
This course uses the USC TOTALe system (aka Blackboard) and can be accessed via
http://totale.usc.edu. To get started with TOTALe, point your browser to http://totale.usc.edu and
then select BME 423. Be sure to CHANGE YOUR EMAIL to the one you use most frequently
as we will send out e-mail messages during the semester using your e-mail address that is
associated with TOTALe.
7. Special Accommodations:
Any student requesting academic accommodations based on a disability is required to register
with Disability Services and Programs (DSP) each semester. A letter of verification for approved
accommodations can be obtained from DSP. Please be sure the letter is delivered to me (or to
TA) as early in the semester as possible. DSP is located in STU 301 and is open 8:30 a.m. – 5:00
p.m., Monday through Friday. The phone number for DSP is (213) 740-0776.
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BME Program Outcomes
1. Apply knowledge of mathematics, physical sciences, life sciences and engineering to
formulate and study or solve engineering problems, including problems at the interface of
engineering, medicine, and biology
2. Plan and conduct experiments as well as analyze and interpret experimental
measurements collected on physical systems and living systems
3. Design electronic, mechanical and/or computer-based devices and software for
applications including medical instrumentation, physiological measurement and signal
processing, prosthesis development, and engineering simulation of living systems
4. Understand the professional, ethical and societal responsibilities pertinent to the practice
of engineering
5. Communicate effectively using appropriate technology and information resources to
document work, analyze engineering problems and solutions, and present project results
6. Lead a team of student engineers performing a laboratory exercise or a class project;
participate in various roles to the team and understand the contribution of each role to the
team’s effort
7. Be independent learners who can master new knowledge and technologies
8. Utilize their broad liberal education to explore and analyze the impact of engineering and
technology solutions on society and health care
9. Select and use modern engineering tools for analysis, design, experimentation and testing
10. Successfully engage in further education in engineering, medicine and biomedical
sciences
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