SYLLABUS
PHE: 515 Introduction to Biostatistics
SCHOOL OF COMMUNITY HEALTH
Professor Alexis Dinno
(503) 725-3076
alexis.dinno@pdx.edu
WINTER QUARTER, 2016
Office:
Office Hours:
Classroom:
Class Meetings:
450D Urban Center
Tuesdays & Wednesdays 10:00–11:50
Neuberger Hall 59
Mondays & Wednesdays, 14:00–15:50
COURSE DESCRIPTION
This course covers a broad range of basic statistical methods used in the health sciences. The course begins by covering methods
of summarizing data through graphical displays and numerical measures. Basic probability concepts will be explored to establish
the basis for statistical inference. Confidence intervals and hypothesis testing will be studied with emphasis on applying these
methods to relevant situations. Both normal theory and nonparametric approaches will be studied including one- and twosample tests of population means and tests of independence for two-way tables. Students will be introduced to one-way analysis
of variance (ANOVA), correlation, and simple linear regression. We focus on understanding when to use basic statistical
methods, how to compute test statistics and how to interpret and communicate the results. We require software (Stata) as part
of the course to introduce you to basic data management, reading output from statistics packages, interpreting and summarizing
results.
LEARNING COMPETENCIES (SEE APPENDED COURSE COMPETENCY MATRIX)
1) Select and generate graphical and numerical summaries of data.
2) Use principles of statistical inference to make conclusions about populations from samples.
3) Communicate statistical findings to others.
4) Use computer software to conduct simple statistical analysis.
PHE 515 Introduction to Biostatistics is restricted to students in the Oregon Master of Public Health degree
program. Conversely, the core Biostatistics requirement can be fulfilled only by this course or an equivalent Master of Public
Health biostatistics course at OHSU. This course stresses biostatistical methods with example data and problems
appropriate to public health (including the identification and access of public health data sets), explicit emphasis on analysis of
data from epidemiologic study designs, inference about epidemiologic measures (e.g. relative risks, odds ratios), tests for
equivalence alongside tests for difference, and stresses the links between statistical inference for public health concepts (e.g. the
population perspective, the vulnerabilities perspective, etc.).
TEXTS AND READING ASSIGNMENTS
Pagano M, Gauvreau K. Principles of Biostatistics, 2nd Edition, Duxbury Press, Pacific Grove, CA, 2000. The book also
includes a CD containing data needed for the exercises. The book is available at the PSU bookstore. All additional materials,
including lecture slides (available after each lecture), are available only via my course website
(http://web.pdx.edu/~adinno/index.html#PHE510) Used copies from many book vendors are listed here:
http://www.fetchbook.info/compare.do?search=9780534229023.
You should do the reading for a particular lecture before the lecture. You will have more pertinent questions, pick up the
material more quickly, and while you can ask me a question about the last reading pretty successfully, you’ll get much less
helpful answers if you ask the book a question about the last lecture. I intend optional readings to give you insight into
alternatives to, histories of and applications of the methods that we cover in class: read or skim them, but don’t study them.
(But do read them! )
PHE 510: INTRODUCTION TO BIOSTATISTICS
WINTER QUARTER, 2016
STATISTICAL COMPUTING
We will be using the statistical computing package Stata™ for this class. You may, at your option use another statistical package
(R, SPSS™ or its open source equivalent, PSPP, SAS™, etc.), but will receive assistance for computing-related question for Stata
only. The course materials include DINNO’S STATA CHEAT SHEET which offers both specific tips for using Stata, and links to
online resources for using it.
Stata (http://www.stata.com)
R (http://cran.r-project.org/)
PSPP (https://www.gnu.org/software/pspp/)
SPSS (PSU maintains a site license through Self Service Software: http://www.pdx.edu/oit/self-service-software)
SAS (http://www.sas.com/)
METHODS OF EVALUATION
Homework: (35% of grade)
Note: Unless you contact me before the due date with a valid excuse, late homework will not be accepted. Except for the last
assignment, homework will be assigned is due on paper—not electronically—one week from the date of assignment.
In-class mid-term: (30% of grade) Monday, February 9.
In-class final: (35% of grade) Wednesday, March 18
Note: my exams are open book/open note, but I do not permit networked digital devices such as laptops, tablets, e-readers, etc.,
so factor this into your textbook decisions.
Extra credit: (Approximately 0–10% of course grade) Students will have the opportunity to complete extra credit assignments
and participate in competitions for extra credit throughout the course. These opportunities are entirely optional, although
pursuing them will learning opportunities.
Grading Policy:
Homework
Exam I
Exam II
35%
30%
35%
Grading Scale Thresholds:
≥90%: A
≥84%: B+
≥80%: B
≥74%: C+
≥70%: C
≥64%: D+
≥60%: D
<57%: F
≥87%:
≥77%:
≥67%:
≥57%:
A–
B–
C–
D–
PSU DISABILITY RESOURCE CENTER (DRC)
Accommodations are collaborative efforts between students, faculty, and the Disability Resource Center
(http://drc.pdx.edu/). Students with accommodations approved through the DRC are responsible for contacting the family
member in charge of the course, prior to or during the first week of the term, to discuss accommodations. Students who
believe they are eligible for accommodations but who have not yet obtained approval through the DRC should immediately
contact the DRC, at 503-725-4150.
SAFE CAMPUS MODULE
If you have not done so already, please complete the Safe Campus Module in d21. The module should take approximately 30 to
40 minutes to complete and contains important information and resources. If you or someone you know was been harassed or
assaulted, you can find the appropriate resources on PSU’s Enrollment Management and Student Affairs: Sexual Prevention and
Response website at http://www.pdx.edu/sexual-assault/. PSU’s Student Code of Conduct makes It clear that violence and
harassment based on sex and gender are strictly prohibited and offenses are subject to the full realm of sanctions, up to and
including suspension and expulsion.
PHE 510: INTRODUCTION TO BIOSTATISTICS
CLASS SCHEDULE
LECTURE #
1
2
DATE
M Jan 5
Required reading:
W Jan 7
Required reading:
Optional reading:
3
M Jan 12
Required reading:
Optional reading:
4
W Jan 14
Required reading:
M Jan 19
TOPIC
WINTER QUARTER, 2016
HOMEWORK DUE (PAGE COUNT)
Overview of statistics; data presentation; obtaining public health data
• Pagano & Gauvreau Sections 2.1–2.3
(17)
Numerical summaries; rates, population health measures;
population pyramids; direct and indirect age standardization
• Pagano & Gauvreau Sections 3.1–3.3, 4.1–4.2
• Gould, S. J. (1985). The median isn’t the message. Discover, 6(6):42–44.
(33)
(3)
Visual distribution summaries; probability; risk; relative risk;
risk difference; odds ratio
• Pagano & Gauvreau Sections 6.1–6.5
• Burnham et al. (2006). The Lancet, 368(9545):1421–1428.
• Correspondence over Burnham’s article included for optional reading
(25)
(8)
(4)
Theoretical probability distributions; discrete and continuous distributions
population versus sample quantities; PMFs and PDFs
• Pagano & Gauvreau Sections 7.1–7.5
HW 1
(30)
NO CLASS: Martin Luther King Day
5
W Jan 21
Required reading:
The z-score; sample distribution of the mean; standard error of the mean
• Pagano & Gauvreau Chapters 8.1–8.3
HW 2
(8)
6
M Jan 26
Required reading:
Statistical inference; confidence intervals; the t-distribution
• Pagano & Gauvreau Chapters 9.1–9.3
HW 3
(11)
7
W Jan 28
Required reading:
8
M Feb 2
Required reading:
Optional reading:
Hypothesis testing; tests for difference (positivist hypotheses); prelude to
tests for equivalence (negativist hypotheses)
• Pagano & Gauvreau Chapters 10.1–10.6
Comparison of two means; confidence intervals versus hypothesis testing;
statistical power; equivalence hypothesis testing; relevance tests
• Pagano & Gauvreau Chapters 11.1–11.2
• Dinno “Applying t tests of equivalence using two one-sided tests”
•
Dinno, A. (2014). Comment on “The Effect of Same-Sex Marriage Laws on
Different-Sex Marriage: Evidence From the Netherlands”. Demography,
51(6):2343–2347.
•
Cumming, G. (2009). Inference by eye: reading the overlap of independent
confidence intervals. Statistics In Medicine, 28(2):205–220.
Schuirmann, D. A. (1987). A comparison of the two one-sided tests
procedure and the power approach for assessing the equivalence of average
bioavailability. Pharmacometrics, 15(6):657–680.
Westlake, W. J. (1976). Symmetric confidence intervals for bioequivalence
trials. Biometrics, 32:741–744.
•
•
W Feb 4
Review session for the mid-term
M Feb 9
Mid-term exam
(18)
HW 4
(14)
(5)
(5)
(16)
(24)
(4)
PHE 510: INTRODUCTION TO BIOSTATISTICS
CLASS SCHEDULE (CONTINUED)
LECTURE #
9
10
DATE
W Feb 11
Required reading:
Homework will require:
M Feb 16
Required reading:
Optional reading:
11
W Feb 18
Required reading:
Optional reading
12
M Feb 23
TOPIC
WINTER QUARTER, 2016
HOMEWORK DUE (PAGE COUNT)
Analysis of Variance (ANOVA) & F-test; repeated measures ANOVA
• Pagano & Gauvreau Chapters 12.1–12.2
• Glantz (2005) primer of biostatistics. 6th edition. 347–350
ANOVA & F-test; multiple comparisons; family-wise error rate;
false discovery rate; introducing nonparametric methods
• Pagano & Gauvreau Chapters 12.1–12.2
• Shaffer J. P. (1995). Multiple Hypothesis Testing. Annual Review of
Psychology. 46:561–584.
• Benjamini Y. & Hochberg Y. (2000) On the Adaptive Control of the False
Discovery Rate in Multiple Testing with Independent Statistics. Journal of
Educational and Behavioral Statistics. 25:60–83.
Nonparametric tests: sign, sign rank, rank sum, Kruskal-Wallis &
Dunn’s post hoc test for difference; equivalence and relevance
• Pagano & Gauvreau Chapters 13.1–13.4
• Kruskal, W. H. and Wallis, A. (1952). Use of ranks in one-criterion
variance analysis. Journal of the American Statistical Association,
47(260):583–621.
• Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics,
6(3):241–252.
HW 5
(10)
(4)
HW 6
(10)
(24)
(24)
(11)
(39)
(12)
Confidence intervals for proportions; tests for proportion difference;
equivalence and relevance; introducing contingency tables;
testing relative risks
• Pagano & Gauvreau Chapters 14.1–14.6
• Agresti, A. and Caffo, B. (2000). Simple and effective confidence intervals
for proportions and differences of proportions result from adding two
successes and two failures. The American Statistician, 54(4):280–288.
• Cochran, W. G. (1950). The comparison of percentages. Biometrika,
37(3/4):256–266.
HW 7
HW 8
Required reading:
Categorical data analysis (contingency tables); McNemar’s tests for
difference, equivalence and relevance; Cochran’s test
• Pagano & Gauvreau Chapters 15.1–15.3
14
M Mar 2
Required reading:
Correlation; prelude to linear regression
• Pagano & Gauvreau Chapters 17.1–17.3
HW 9
(10)
15
W Mar 4
Required reading:
Optional reading:
Linear regression
• Pagano & Gauvreau Chapters 18.1–18.3
• Reshef, D., et al. (2011). Detecting novel associations in large data sets.
Science, 334(6062):1518–1524.
HW 10
(24)
(7)
16
M Mar 9
Linear regression and inference: tests of parameter difference
equivalence, and relevance; introduction to survival analysis
• Pagano & Gauvreau Chapters 18.1–18.3, 21.1–21.2
HW 11
W Mar 11
Review session for the final exam
HW 12
W Mar 18
Final exam: NOTE DIFFERENT TIME! (12:30–14:20)
Required reading:
Optional reading:
13
W Feb 25
Required reading:
(13)
(9)
(11)
(16)
(36)
PHE 510: INTRODUCTION TO BIOSTATISTICS
HOMEWORK ASSIGNMENTS
HW #
MATERIAL COVERED
1
Chapter 2, Chapter 3
2
Chapter 4
3
Chapter 6, Chapter 7
4
Chapter 8, Chapter 9
5
Chapter 10
6
Chapter 11, Dinno handout and article
7
Chapter 12, Shaffer, Benjamini, Glantz, lecture
8
Chapter 13 plus Kruskal-Wallis from lecture
9
Chapter 14, Agresti,
10
Chapter 15
11
Chapter 17
12
Chapter 18, Chapter 21
WINTER QUARTER, 2016
DUE IN CLASS
Lecture 4
Lecture 5
Lecture 6
Lecture 8
Lecture 9
Lecture 10
Lecture 12
Lecture 13
Lecture 14
Lecture 15
Lecture 16
Lecture 17
TOTAL POINTS (%)
51 (11)
46 (10)
48 (11)
35 (8)
34 (8)
35 (8)
43 (10)
30 (7)
41 (9)
29 (7)
27 (6)
36 (8)
Students are strongly urged to study in groups every week and to co-teach, to collaborate on working through the pencil and
paper homework, as well as the computer assignments. However, each student must turn in her or his own homework.
PHE 510: INTRODUCTION TO BIOSTATISTICS
WINTER QUARTER, 2015
Core Course: Introduction to Biostatistics
PHE 510 (PSU), PHPM 524 (SOM, OHSU), CPHN 530 (SON, OHSU)
Credits: 4 credits
COURSE COMPETENCY MATRIX
Competencies
1. Select and generate
graphical and numerical
summaries of data
2. Use principles of
statistical
inference to make
conclusions
about populations from
samples.
3. Communicate statistical
findings to others
Related Components
Use graphical methods to display features of
data.
2. Compute numerical summaries to
summarize features of data.
3. Interpret graphical and numerical summaries
to describe data.
Competency Demonstrations
Menu of Options
 Quizzes/Exam(s)
 Homework
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Learning Activities
Menu of Options
Utilize web sources
Example and case study
Statistical software examples
End-of-unit exercise
Class session
Computer lab session
Written class note or/and present
Class session
Reading
Case study
Using statistical software(s)
Public questions – practice exercises
End-of-unit exercise
Application self-tests
Computer lab session
Guided practice and feedback
Case study
Individual or team project
End-of-unit exercises
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Computer lab session
Utilize internet resources
Case study
Guided practice and feedback
1.
Apply principles of probability
laws/distributions, interval estimation, and
hypothesis testing.
2. Select and perform statistical procedures
based on type of data and assumptions for
approaches used.
3. Construct and interpret point and interval
estimates for population parameters using
sample data.
1.
Provide a written state or verbally present:
1. Statistical methods used
2. Results obtained
3. Conclusions drawn
4. Limitations of conclusions related to study
design and analysis
4. Use computer software to 1. Enter and read data into a statistical
software package
conduct simple statistical
2.
Manipulate and transform data elements
analysis
3. Use program to perform statistical analysis.
4. Interpret the output from statistical
software.
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Quizzes/Exam(s)
Homework
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Quizzes/Exam(s)
Homework
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Homework