BIOSTATS 640
Intermediate Biostatistics
Biostatistics and Epidemiology
UMass Amherst

 

 

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1. Review of BIOSTATS 540
2. Discrete Distributions

3. Introduction to Nonparametrics

4. Categorical Data Analysis

5. Normal Theory Regression
6. Analysis of Variance

7. Logistic Regression
8. Introduction to Survival Analysis
9. (Time permitting) Introduction to Mixed Models

Syllabus




(Download pdf copy of 2021 syllabus)

Instructor:
Carol Bigelow, PhD
Department of Biostatistics & Epidemiology
402 Arnold House
University of Massachusetts
Amherst , MA 01003
tel: 413/545-1319
fax: 413/545-1645
email: cbigelow@schoolph.umass.edu

Teaching Assistant:
Derruck Liu
Graduate Student, Biostatistics
Department of Biostatistics & Epidemiology
University of Massachusetts
Amherst, MA 01003
email: derrickliu@umass.edu





At a Glance
The following is our weekly schedule for Spring 2021
Mondays (beginning 2/8/21) Office Hour 4:30 - 5:30 pm
Wednesdays (beginning 2/3/21) Lecture Presentation 4:00 - 5:30 pm
Fridays (beginning (2/5/21) Discussion and Group Work 4:00 - 5:00 pm

Course Description
Content.  BIOSTATS 640 is the second of a two-semester sequence of introductory biostatistics.  Similar to BIOSTATS 540, the overall goal is statistical literacy and skills in selected, basic, analyses of biological and health data.  Topics include:  review of introductory biostatistics, selected discrete distributions, introduction to nonparametric tests, analysis of epidemiological tables, single and multiple predictor normal theory regression, logistic regression and introduction to survival analysis.   If time allows (this has not happened in recent years) I will also provide an introduction to mixed models.  Software.  We will be using R in illustrations of data analysis.   However, no prior experience with R is required and you will not be required to use R on any examination.  If you are already a SAS or Stata (or other software) user, you are welcome to use that instead. 

ADA Accomodation Policy
The University of Massachusetts Amherst is committed to making reasonable, effective and appropriate accommodations to meet the needs of students with disabilities and help create a barrier-free campus. If you have a disability and require accommodations, please register with Disability Services (161 Whitmore Administration building; phone 413-545-0892) to have an accommodation letter sent to me. Information on services and materials for registering are also available on their website, here:   www.umass.edu/disability.

If, because of a disability, you may require special arrangements in order to meet course requirements, please contact me as soon as possible (email:  cbigelow@schoolph.umass.edu) so that we can develop an accommodation plan that will work for you.

COVID-19 Impact on This Course

In light of the ongoing COVID-19 pandemic and the challenges it presents, here are some key things to know about BIOSTATS 640.

This is a remote only course

You must complete a “one-time” registration to access Zoom.  Our Zoom meetings will be scheduled as recurring meetings that require a one-time registration. 

Attendance is not required but, of course, is strongly encouraged!  

Recordings of the Wednesday lecture presentations and Friday group work will be provided.

The Monday office hours will not be recorded

I will be using your UMass email address only.   All announcements and correspondence with the entire class will be sent to you to your UMass email address only.  Please be aware that I will not be keeping track of any other email addresses (e.g – “gmail”) you might have.

Polices on Classes and Work Missed for Extenuating Circumstances

Per University of Massachusetts Academic Regulations, “Students absent due to extenuating circumstances-including jury duty, military obligations, scheduled activities for other classes, the death of a family member, or verifiable health-related incapacity-remain responsible for meeting all class requirements and contacting the faculty member in a timely fashion about making up missed work. Faculty shall offer such students reasonable assistance in making up missed classes (i.e., making arrangements for attendance at labs or discussion sections which meet at other times; providing makeup exams or labs where feasible or offer mutually agreeable alternatives to make up work).”  

If any extenuating circumstances prevent you from completing any work on time, please contact me as soon as possible (email:  cbigelow@schoolph.umass.edu) so that we can make alternative arrangements.

Getting Started with Remote Learning

Please be sure to familiarize yourself with the following resources and technologies before the first week of class (February 3-5, 2021).

__1.  Public Course Website:  https://people.umass.edu/~biep640w/
__2.  UMass Amherst Blackboard Learn:  https://uma.umassonline.net/webapps/login/#
         Resource for Learning Blackboard Learn:
         (source:  Rowan University, Campbell Library)  Blackboard Tutorials for Students
__3.  UMass Amherst Zoom Login:  https://www.umass.edu/it/zoom
         Resource for Learning Zoom:
         (source:  https://support.zoom.us) Zoom Video Tutorials
         Tip:  Be sure to watch these two tutorials, here:  “Join a Meeting” and “Meeting Controls

Course Units, Objectives, and Outcome Competencies

Course Units.  This course has 8 units (plus a 9th unit, time permitting)

1. Review of BIOSTATS 540, Introductory Biostatistics
2. Discrete Distributions
3.  Introduction to Nonparametrics
4.  Categorical Data Analysis
5.  Normal Theory Regression
6.  Analysis of Variance
7.  Logistic Regression
8.  Introduction to Survival Analysis
9. (time permitting) Introduction to Mixed Models

Objectives. 
By the end of this course, you should be able to perform, interpret, and report the findings of selected simple statistical analyses:  description, hypothesis testing (including nonparametric tests), epidemiologic tables, simple and multiple predictor normal theory regression, analysis of variance, logistic regression, and some simple survival analysis techniques.

Outcome Competencies.
The specific outcome competencies include, but are not limited to, the following:

1.  Explain the conceptual framework of selected, basic methods, of biostatistical analysis.  This is “statistical literacy”.   You will be introduced to the underlying principles, rationale, and relevance.  For example, you will learn that a fitted model is likely to be wrong, but nevertheless useful.  It might yield important insights into the nature and strength of associations that might exist.

2.  Develop a conceptual framework that integrates techniques and methods in biostatistics. You will learn that the principles of biostatistics (and epidemiology, too) are grounded in scientific reasoning (and the goal of causal inference).  You will also learn that the ideas of estimation and statistical hypothesis testing are related to the notion of “signal” and “noise”.

3.  Integrating analysis strategies in biostatistics with principles and issues in epidemiology. The presentation of the topics in this course will highlight their relevance to key issues in epidemiology, including:  confounding, effect modification, discovery of intermediary pathways, and reduction of bias.

4. Apply biostatistical methods to the design of studies in public health.  We will integrate the principles of statistical literacy with those of epidemiological research to gain practice in developing data analysis plans.  And we will see that these vary, depending on the data type and the questions of interest.

5. Use computers to appropriately store, manage, manipulate and process data for a research study using modern software.  This course includes an introduction to the use of R.

6. Apply descriptive techniques commonly used to summarize public health data.  I will emphasize the importance of graphical summaries and the use of R.

7. Describe the basic concepts of probability, random variation and selected, commonly used, probability distributions – You will learn additional concepts of sampling distributions and additional applications of the central limit theorem.  Specifically, you will learn how these ideas are the foundation of modeling, estimation, and hypothesis testing.

8.  Select and perform the appropriate descriptive and inferential statistical methods in selected basic study design settings.  I will provide data sets for you to explore and will encourage you to give them a try!  Specifically, I will encourage you to try your hand at developing your own analysis plan, doing the programming necessary for analysis, interpreting your results (especially with respect to the analysis goals and associated issues of confounding, bias, effect modification, and precision) and generating a report of your findings.  

9. Interpret results and critically evaluate basic statistical aspects of public health research and practice reported in the literature. You will gain practice in being a statistically literate consumer of published examples of data analyses.

10.  Effective communication. The utility of biostatistics rests, ultimately, in its effective communication!  In the weekly homework assignments and in the exams, you will gain practice in the communication of biostatistics work to the lay reader.   Specifically, you will learn how to write the following types of descriptions:  analysis question, rationale, method used, statistical findings, and subject matter relevance.

Textbook, R and Other Resources

There is no required text.  Here are some useful resources:

(1) Course content
Whitlock MC and Schluter D
The Analysis of Biological Data, Third Edition
Macmillan Learning
2020
ISBN: 978 1319 226 237

(2) Course content
Gelman A and Hill J
Data Analysis Using Regression and Multilevel/Hierarchical Models
Cambridge
2007
ISBN: 978 0 521 68689 1

(3)  BIOSTATS 540 – Introductory Biostatistics 
http://people.umass.edu/~biep540w/

(4) R for Beginners - Book
Li, Quan
Oxford University Press, 2018
ISBN:  978-0-1906-5622-5
Using R for Data Analysis in Social Sciences
https://www.oxfordscholarship.com/view/10.1093/oso/9780190656218.001.0001/oso-9780190656218

(5) Quick-R for Beginners – Online Tutorials
Source:  DataCamp
https://www.statmethods.net/index.html

(6) How to Download and Install R and R Studio
Windows Users (pdf, 4 pp)
MAC Users (pdf, 4 pp)



 You will not be tested on R or any other statistical software; and
Use of R or any other statistical software will not be necessary in any exam. 


 


Important Dates to Remember

First Week of Class

 

February 3-5, 2021

 

Last Day to Drop with NO Record

 

Friday February 12, 2021

 

President’s Day
Our Monday office hour WILL BE HELD

 

Monday February 15, 2021

 

Wellness Wednesday
No class – This lecture will be made up on Monday February 22

 

Wednesday February 24, 2021

 

Last Day to Drop with “DR”

 

Monday March 29, 2021

 

Wellness Wednesday
No class – This lecture will be made up on Monday April 12

 

Wednesday April 14, 2021

 

President’s Day
Our Monday office hour WILL BE HELD

 

Monday April 19, 2021

 

Last Meeting

 

Monday May 3, 2021

Final Exam (Test #3) DUE

Friday May 7, 2021

Last date for submissions for credit.

Monday May 10, 2021

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Weekly Schedule

Dates

Schedule

1

February 1-5, 2021

Unit 1:  Review of BIOSTATS 540
Mon:  no meeting    Wed: Lecture (4:00 – 5:30)        Fri:  Group work (4:00 – 5:00)

2

February 8-12, 2021

Unit 2:  Discrete Distributions
Mon: R (4:30 – 5:30)   Wed: Lecture (4:00 – 5:30)    Fri:  Group work (4:00 – 5:00)

3

February 15-19, 2021

Unit 3:  Introduction to Nonparametrics
Mon: R (4:30 – 5:30)   Wed: Lecture (4:00 – 5:30)    Fri:  Group work (4:00 – 5:00)

4

February 22-26, 2021

Unit 4:  Categorical Data Analysis, 1 of 2
Mon: Lecture (4:30 – 5:30)   Wed: DAY OFF    Fri:  Group work & R (4:00 – 5:00)
 

5

March 1-5, 2021

Unit 4:  Categorical Data Analysis, 2 of 2
Mon: R (4:30 – 5:30)   Wed: Lecture (4:00 – 5:30)    Fri:  Group work (4:00 – 5:00)

6

March 8-12, 2021

Unit 5:  Normal Theory Regression, 1 of 3
Mon: R (4:30 – 5:30)   Wed: Lecture (4:00 – 5:30)    Fri:  Group work (4:00 – 5:00)

7

March 15-19, 2021

Unit 5:  Normal Theory Regression, 2 of 3
Mon: R (4:30 – 5:30)   Wed: Lecture (4:00 – 5:30)    Fri:  Group work (4:00 – 5:00)

8

March 22-26, 2021

Revised Unit 5:  Normal Theory Regression, 3 of 3
Mon: R (4:30 – 5:30)   Wed: Lecture (4:00 – 5:30)    Fri:  Group work (4:00 – 5:00)

9

March 29 – April 2, 2021

Revised Unit 6:  Analysis of Variance, 1 of 2
Mon: R (4:30 – 5:30)   Wed: Lecture (4:00 – 5:30)    Fri:  Group work (4:00 – 5:00)

10

April 5-9, 2021

Revised Unit 6: Analysis of Variance, 2 of 2
Mon: R (4:30 – 5:30)   Wed: Lecture (4:00 – 5:30)    Fri:  Group work (4:00 – 5:00)

11

April 12-16, 2021

Revised Unit 7:  Logistic Regression, 1 of 2
Mon: R (4:30 – 5:30)   Wed: DAY OFF    Fri:  Group work (4:00 – 5:00)

12

April 19-23, 2021

Revised Unit 7:  Logistic Regression, 2 of 2
Mon: Lecture (4:30 – 5:30)   Wed: Lecture (4:00 – 5:30)   Fri:  Group work & R (4:00 – 5:00)

13

April 26-30, 2021

Revised Unit 8:  Introduction to Survival Analysis
Mon: R (4:30 – 5:30)   Wed: Lecture (4:00 – 5:30)    Fri:  Group work (4:00 – 5:00)

14

May 3-4, 2021

Monday: Last meeting
Wrap up (Final exam prep, Other content questions, R help (4:30 – 5:30)  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Homework
Schedule

Posting

Due

Topics Covered

 

1

 

Wednesday February 3, 2021

 

Friday February 12, 2021
Last date for submission for credit with (-20 points):  Friday 2/19/21

 

1 – Review of BIOSTATS 540

 

2

 

Wednesday February 10, 2021

 

Friday February 19, 2021
Last date for submission for credit with (-20 points):  Friday 2/26/21

 

2 – Discrete Distributions

 

3

 

Wednesday February 17, 2021

 

Friday February 26, 2021
Last date for submission for credit with (-20 points):  Friday 3/5/21

 

3 – Introduction to Nonparametrics

 

4

 

Wednesday February 24, 2021

 

Friday March 12, 2021
Last date for submission for credit with (-20 points):  Friday 3/19/21

 

4 – Categorical Data Analysis

 

5

 

Wednesday March 10, 2021

 

Friday March 26, 2021
Last date for submission for credit with (-20 points):  Friday 4/2/21

 

5 – Normal Theory Regression

 

6

 

Revised
Wednesday March 31, 2021

 

Revised
Friday April 16, 2021
Last date for submission for credit with (-20 points):  Friday 4/23/21

 

6 – Analysis of Variance

 

7

 

Revised
Wednesday April 14, 2021

 

Revised
Friday April 30, 2021
Last date for submission for credit with (-20 points):  Friday 5/7/21

 

7 -  Logistic Regression

 

8

 

Wednesday April 21, 2021

 

Friday May 7, 2021
Last date for submission for credit with (-10 points):  Monday 5/10/21

 

8 -  Introduction to Survival Analysis

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Exam Schedule

 

Posting

 

Due

 

Topics Covered

 

1

 

Monday February 22, 2021

 

Monday March 8, 2021
Last date for submission for credit with (-20 points):  Monday 3/15/21

 

1 - Review of BIOSTATS 540
2 - Discrete Distributions
3 - Introduction to Nonparametrics

 

2

 

Revised
Monday March 29, 20201

 

Revised
Monday April 12, 2021

Last date for submission for credit with (-20 points):  Monday 4/19/21

 

4  - Categorical Data Analysis
5  - Normal Theory Regression

 

3

 

Revised
Friday April 23, 2021

 

Friday May 7, 2021
Last date for submission for credit with (-10 points):  Monday 5/10/21

 

6  - Analysis of Variance
7  -  Logistic Regression
8  -  Introduction to Survival Analysis

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 Grade Determination

Percent of Grade

Homeworks
BIOSTATS 640 has 8 assignments, one per unit.  Each will be graded pass/fail and the solutions are provided (and you are welcome to consult the solutions as you go along!).  Thus, the homeworks are the participation portion of your grade. 

Late policy.  If you submit your work on the due date or within 48 hours, your score will be 100.   Late homework submitted late, but no later than one week, will be given a score of 80.  Homeworks submitted more than one week late will be given a score of 0. 

To earn full homework credit, you must complete eight (8) assignments.

 

25%, sub-total


Exams (all open book)
There are 3 tests, all open book.  For each test, you are welcome to consult any resource you like, but you are not allowed to consult any person except me

Late policy If you submit your exam on the due date, you will earn full credit for your work.  Exams submitted late but within 48 hours will have 10 points deducted from their score.  Exams submitted late but between 48 hours and 1 week will have 20 points deducted from their score.   Exams submitted more than one week late will be given a score of 0. 

 

75%, sub-total
as follows:

Best test – 40%
2nd best  – 20%

3rd best  – 15%

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Letter Grade determination
A            95 and over
A-           90 - 94
B+          87 - 89
B            83 – 86
B-           80 - 82
C+          77 – 79
C            70 – 76
F          Below 70

Policy on Late Submissions

My policy on late submissions aims to be fair while at the same time accommodating those who, for whatever reason, need an extension.

 

Credit Policy - Homeworks

Grading Policy - Exams

On Time

Full Credit for points Scored

Full Credit for points Scored

Up to 48 hours late

Full Credit for points Scored

Points Scored – 10 points

3-7 days late

Points Scored – 20 points

Points Scored – 20 points

8+ days late

0 points (no credit)

0 points (no credit)

 

 

 

 

 

 

 

 

Policy on Academic Dishonesty

Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst. Academic dishonesty is prohibited in all programs of the University.
All students are expected to adhere to guidelines of University of Massachusetts regarding academic honesty.  These guidelines and additional resources are available online att

https://www.umass.edu/honesty/

Briefly, the University of Massachusetts/Amherst Senate Document 89-026 defines academic dishonesty as including but not limited to:
Cheating
– intentional deceit, trickery, or breach of confidence, used to gain some unfair or dishonest advantagein one’s academic work. Fabrication – intentional falsification or invention of any information or citation in any academic exercise.
Facilitating dishonesty – knowingly helping or attempting to help someone else commit an act of academic dishonesty. Plagiarism – knowingly representing the words or ideas of another as one’s own work in any academic exercise. Submitting in whole or in part, without citation, prewritten term papers of another or the research of another (including but not limited to such materials sold or distributed commercially).

Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent. For more information about what constitutes academic dishonesty, please see the Dean of Students website.

Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty.  Complete details of the procedures and timeline are here.   Instructors should take reasonable steps to address academic misconduct. Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible. Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair. The procedures detailed here are intended to provide an efficient and orderly process by which action may be taken if it appears that academic dishonesty has occurred and by which students may appeal such actions.

Valuing, Recognizing, and Encouraging Diversity

I believe that promoting and valuing diversity in the classroom enriches learning and broadens everyone’s perspectives. I also believe in inclusion, tolerance and respect for others as essential values.  Where possible, I will strive to create a sense of community and promote excellence in the learning environment.  With respect to diversity, I will seek out and honor (1) the variety of life experiences you have had, and (2) the factors that define your “diversity of presence,” including: age, economic circumstances, ethnic identification, disability, gender, geographic origin, race, religion, sexual orientation, social position.

Names and Pronouns

If you have not already indicated your chosen first name and pronouns in SPIRE, please let me know what name and pronouns we should use for you (email:  cbigelow@schoolph.umass.edu).

Title IX Statement

The University of Massachusetts Amherst is committed to fostering a safe, productive learning environment. Title IX and our school policy prohibits discrimination on the basis of sex. Sexual misconduct — including harassment, domestic and dating violence, sexual assault, and stalking — is also prohibited at our school.
UMass Amherst encourages anyone experiencing sexual misconduct to talk to someone about what happened, so they can get the support they need and our school can respond appropriately.
If you wish to speak confidentially about an incident of sexual misconduct, want more information about filing a report, or have questions about school policies and procedures, please contact our Title IX Coordinator, Débora D. Ferreira, Equal Opportunity Office (EO), 413-545- 3464, equalopportunity@admin.umass.edu.

Please be aware.  UMass Amherst is legally obligated to investigate reports of sexual misconduct, and therefore it cannot guarantee the confidentiality of a report, but it will consider a request for confidentiality and respect it to the extent possible. If you want to talk with someone who is not a mandated reporter, you can contact the Center for Women and Community, (https://www.umass.edu/cwc/, 413-545-0883, or 24-hour hotline 413-545-0800), the Center for Counseling and Psychological Help (https://www.umass.edu/counseling/, 413-545-2337), or University Health Services SANE program (https://www.umass.edu/uhs/services/sane, 413-577-5000).   Please also be aware.  As an instructor, I am also required by our school to report incidents of sexual misconduct and thus cannot guarantee confidentiality. I must provide our Title IX coordinator with relevant details such as the names of those involved in the incident.

Copyright Protection

Many of the materials created for this course are my own intellectual property. This includes, but is not limited to the syllabus, lectures, and course notes. Except to the extent not protected by copyright law, any use, distribution or sale of such materials requires my permission. Please be aware that it is a violation of university policy to reproduce, for distribution or sale, class lectures or class notes, unless the faculty member has explicitly waived copyright.

 

 

(Download pdf copy of 2021 syllabus)


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University of Massachusetts at Amherst
Copyright 2021 University of Massachusetts, Amherst.
This is the course web site for BIOSTATS 640 and 640W, Biostatistics Program, Department of Biostatistics and Epidemiology.
Produced and maintained by the School of Public Health and Health Sciences.
Send comments or questions about this web site to cbigelow@schoolph.umass.edu.
Page updated: March 22, 2021