PHE 510: Introduction to Biostatistics Winter Quarter, 2016
Professor Alexis Dinno Office: 450D Urban Center
(503) 725-3076 Office Hours: Tuesdays & Wednesdays 10:00–11:50
Classroom: Neuberger Hall 59
Class Meetings: 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 two-sample 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! J)
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.
PHE 510: Introduction to Biostatistics Winter Quarter, 2016
Grading Policy:
Homework 35%
Exam I 30%
Exam II 35%
Grading Scale Thresholds:
≥90%: A ≥87%: A–
≥84%: B+ ≥80%: B ≥77%: B–
≥74%: C+ ≥70%: C ≥67%: C–
≥64%: D+ ≥60%: D ≥57%: D–
<57%: F
PHE 510: Introduction to Biostatistics Winter Quarter, 2016
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.
Class Schedule
Lecture # Date Topic Homework Due (page count)
1 M Jan 5 Overview of statistics; data presentation; obtaining public health data
Required reading: • Pagano & Gauvreau Sections 2.1–2.3 (17)
2 W Jan 7 Numerical summaries; rates, population health measures;
population pyramids; direct and indirect age standardization
Required reading: • Pagano & Gauvreau Sections 3.1–3.3, 4.1–4.2 (33)
Optional reading: • Gould, S. J. (1985). The median isn’t the message. Discover, 6(6):42–44. (3)
3 M Jan 12 Visual distribution summaries; probability; risk; relative risk;
risk difference; odds ratio
Required reading: • Pagano & Gauvreau Sections 6.1–6.5 (25)
• Burnham et al. (2006). The Lancet, 368(9545):1421–1428. (8)
Optional reading: • Correspondence over Burnham’s article included for optional reading (4)
4 W Jan 14 Theoretical probability distributions; discrete and continuous distributions
population versus sample quantities; PMFs and PDFs HW 1
Required reading: • Pagano & Gauvreau Sections 7.1–7.5 (30)
M Jan 19 NO CLASS: Martin Luther King Day
5 W Jan 21 The z-score; sample distribution of the mean; standard error of the mean HW 2
Required reading: • Pagano & Gauvreau Chapters 8.1–8.3 (8)
6 M Jan 26 Statistical inference; confidence intervals; the t-distribution HW 3
Required reading: • Pagano & Gauvreau Chapters 9.1–9.3 (11)
7 W Jan 28 Hypothesis testing; tests for difference (positivist hypotheses); prelude to
tests for equivalence (negativist hypotheses)
Required reading: • Pagano & Gauvreau Chapters 10.1–10.6 (18)
8 M Feb 2 Comparison of two means; confidence intervals versus hypothesis testing;
statistical power; equivalence hypothesis testing; relevance tests HW 4
Required reading: • Pagano & Gauvreau Chapters 11.1–11.2 (14)
• Dinno “Applying t tests of equivalence using two one-sided tests” (5)
• Dinno, A. (2014). Comment on “The Effect of Same-Sex Marriage Laws on (5)
Different-Sex Marriage: Evidence From the Netherlands”. Demography,
51(6):2343–2347.
Optional reading: • Cumming, G. (2009). Inference by eye: reading the overlap of independent (16)
confidence intervals. Statistics In Medicine, 28(2):205–220.
• Schuirmann, D. A. (1987). A comparison of the two one-sided tests (24)
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 (4)
trials. Biometrics, 32:741–744.
W Feb 4 Review session for the mid-term
M Feb 9 Mid-term exam
Class Schedule (continued)
Lecture # Date Topic Homework Due (page count)
9 W Feb 11 Analysis of Variance (ANOVA) & F-test; repeated measures ANOVA HW 5
Required reading: • Pagano & Gauvreau Chapters 12.1–12.2 (10)
Homework will require: • Glantz (2005) primer of biostatistics. 6th edition. 347–350 (4)
10 M Feb 16 ANOVA & F-test; multiple comparisons; family-wise error rate;
false discovery rate; introducing nonparametric methods HW 6
Required reading: • Pagano & Gauvreau Chapters 12.1–12.2 (10)
Optional reading: • Shaffer J. P. (1995). Multiple Hypothesis Testing. Annual Review of
Psychology. 46:561–584. (24)
• Benjamini Y. & Hochberg Y. (2000) On the Adaptive Control of the False (24)
Discovery Rate in Multiple Testing with Independent Statistics. Journal of
Educational and Behavioral Statistics. 25:60–83.
11 W Feb 18 Nonparametric tests: sign, sign rank, rank sum, Kruskal-Wallis &
Dunn’s post hoc test for difference; equivalence and relevance
Required reading: • Pagano & Gauvreau Chapters 13.1–13.4 (11)
Optional reading • Kruskal, W. H. and Wallis, A. (1952). Use of ranks in one-criterion (39)
variance analysis. Journal of the American Statistical Association,
47(260):583–621.
• Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, (12)
6(3):241–252.
12 M Feb 23 Confidence intervals for proportions; tests for proportion difference; HW 7
equivalence and relevance; introducing contingency tables;
testing relative risks
Required reading: • Pagano & Gauvreau Chapters 14.1–14.6 (13)
Optional reading: • Agresti, A. and Caffo, B. (2000). Simple and effective confidence intervals (9)
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, (11)
37(3/4):256–266.
13 W Feb 25 Categorical data analysis (contingency tables); McNemar’s tests for HW 8
difference, equivalence and relevance; Cochran’s test
Required reading: • Pagano & Gauvreau Chapters 15.1–15.3 (16)
14 M Mar 2 Correlation; prelude to linear regression HW 9
Required reading: • Pagano & Gauvreau Chapters 17.1–17.3 (10)
15 W Mar 4 Linear regression HW 10
Required reading: • Pagano & Gauvreau Chapters 18.1–18.3 (24)
Optional reading: • Reshef, D., et al. (2011). Detecting novel associations in large data sets. (7)
Science, 334(6062):1518–1524.
16 M Mar 9 Linear regression and inference: tests of parameter difference HW 11
equivalence, and relevance; introduction to survival analysis
Required reading: • Pagano & Gauvreau Chapters 18.1–18.3, 21.1–21.2 (36)
W Mar 11 Review session for the final exam HW 12
W Mar 18 Final exam: NOTE DIFFERENT TIME! (12:30–14:20)
Homework Assignments
HW # Material covered Due in class Total points (%)
1 Chapter 2, Chapter 3 Lecture 4 51 (11)
2 Chapter 4 Lecture 5 46 (10)
3 Chapter 6, Chapter 7 Lecture 6 48 (11)
4 Chapter 8, Chapter 9 Lecture 8 35 (8)
5 Chapter 10 Lecture 9 34 (8)
6 Chapter 11, Dinno handout and article Lecture 10 35 (8)
7 Chapter 12, Shaffer, Benjamini, Glantz, lecture Lecture 12 43 (10)
8 Chapter 13 plus Kruskal-Wallis from lecture Lecture 13 30 (7)
9 Chapter 14, Agresti, Lecture 14 41 (9)
10 Chapter 15 Lecture 15 29 (7)
11 Chapter 17 Lecture 16 27 (6)
12 Chapter 18, Chapter 21 Lecture 17 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 / Related Components / Learning Activities / Competency Demonstrations1. Select and generate graphical and numerical summaries of data / 1. 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. / Menu of Options
· Utilize web sources
· Example and case study
· Statistical software examples
· End-of-unit exercise
· Class session
· Computer lab session / Menu of Options
· Quizzes/Exam(s)
· Homework
2. Use principles of statistical
inference to make conclusions
about populations from samples. / 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. / · 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 / · Quizzes/Exam(s)
· Homework
3. Communicate statistical findings to others / 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 / · Case study
· Individual or team project
· End-of-unit exercises / · Quizzes/Exam(s)
· Homework
4. Use computer software to conduct simple statistical analysis / 1. Enter and read data into a statistical software package
2. Manipulate and transform data elements
3. Use program to perform statistical analysis.
4. Interpret the output from statistical software. / · Computer lab session
· Utilize internet resources
· Case study
· Guided practice and feedback / · Homework