BIOSTATS 640
Intermediate Biostatistics
Biostatistics and Epidemiology
UMass Amherst

 

 

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1. Review of BIOSTATS 540
2. Regression and Correlation
3. Discrete Distributions
4. Categorical Data Analysis
5. Logistic Regression
6. Introduction to Survival Analysis
7. Analysis of Variance
8. Repeated Measures Analysis
9. Nonparametrics

Syllabus
Updated 3-31-2016



(Download pdf copy of 2017 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:
Meilan Chen
Graduate Student, Biostatistics
Department of Biostatistics & Epidemiology
University of Massachusetts
Amherst, MA 01003





There is NO Required Text

Resources for Those Wishing Them (but not required)

(1) R for Beginners
Horton NJ, Pruim R, Kaplan DT
A Student’s Guide to R: Project Mosaic
https://cran.r-project.org/doc/contrib/Horton+Pruim+Kaplan_MOSAIC-StudentGuide.pdf

(2) R and R Studio for BIOSTATS 640
Horton NJ, and Kleinman K.
Using R and R Studio for Data Management, Statistical Analysis and Graphics, Second Edition
CRC Press, Taylor & Francis Group.
ISBN: 13: 978-1-4822-3737-5
https://englianhu.files.wordpress.com/2016/01/using-r-and-rstudio-for-data-management-statistical-analysis-and-graphics-2nd-edit.pdf

(3) STATA for Beginners
Institute for Digital Research and Education, UCLA
Resources to Help you Learn and Use Stata
http://www.ats.ucla.edu/stat/stata/

(4) Stata for BIOSTATS 640
Acock, Alan C.
A Gentle Introduction to Stata, Fifth Edition. (Note – older editions are just fine!)
Stata Press, 2016.
ISBN-13: 978-1-59718-185-3


 (5) Recommended Resource 
Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE
Regression Methods in Biostatistics – Linear, Logistic, Survival and Repeated Measures Models,
Second Edition
Springer
2012
ISBN 978-1-4614-1352-3
e-ISBN 978-1-4614-1353-0



Statistical Software

This course will provide introductions to two statistical software packages:  R and Stata.  You are welcome to choose whichever software you like. Majors in Biostatistics or Epidemiology or other quantitative fields might want to consider R.   Non-majors might want to consider Stata; its learning curve is a little less steep!  If you’re not sure which to choose, please be sure to talk to me or Meilan (our 2017 Teaching Assistant!). 


How to Download and Install R and R-Studio

                       (How to Download and Install R and R-Studio, pdf 8 pp)

 

How to Obtain Stata Version 14
Stata Corp. offers student discounts on the purchase of Stata through what is called GradPlan.   The cost varies, depending on the size of Stata you want (maximum number of variables, lease versus perpetual license).  

                        (How to Obtain Stata version 14, pdf 3 pp)

 





Course Description

BIOSTATS 640 is the second of a two-semester sequence of introductory biostatistics.  The overall objective is the development of basic statistical literacy and skills in the analysis of biological and health data.  Use of the computer (R and Stata) and the analysis of data sets are included.  Topics include::  simple linear regression, multivariable regression, analysis of proportions and rates, logistic regression, survival analysis, analysis of variance.  Time permitting, repeated measurements analysis and nonparametric analyses are also covered. 



Course Objectives

By the end of this course, you should be able to perform, interpret and report the findings of selected simple staistical analyses: description, hypothesis testing, simple linear regression, some multivariable regression analyses, some analyses of proportions and rates, and some analyses of variance. Time permitting, you may also be able to perform some simple survival analyses, repeated measurements analyses, and nonparametric analyses.


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 and Stata.

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

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 work 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.

 



Class Time and Location:
Section 01 (Amherst): Wednesdays 4:00-6:30, Lederle 147
Section 02 (Worcester): S2-351.
Section 03 (Online): Use your UMass NetID to log into Blackboard Learn (here)

Office Hours:
Section 01 (Amherst): To be decided and by appointment
Section 02 (Worcester): 4:00-5:00 Mondays, in the UMass Medical School hospital cafeteria and by appointment.
Section 03 (Online): By appointment. Feel free to contact me via email, or via Blackboard or by telephone (413-545-1319)


This course has 9 units

1. Review of BIOSTATS 540, Introductory Biostatistics
2. Regression and Correlation
3. Discrete Distributions
4. Categorical Data Analysis
5. Logistic Regression
6. Survival Analysis
7. Analysis of Variance
8. Repeated Measures Analyss(time permitting)
9. Nonparametrics (time permitting)


Grading Policy

Your course grade will be based on the completion of 10 homework assignments and three (3) "take home" open book examinations, as follows:

  Percent of Course Grade
Homework Assignments (10 sets)
25%
Examination I (required)
25%
Examination II (required)
25%
Examination III (required)
25%

 

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

 

Important Dates to Remember

First Week of Class
January 23-27, 2017
Last day to drop with no record
Monday February 6, 2017
Holiday, President's Day
Monday February 20, 2017
NO Worcester section class
We will work ONLINE this week
Last day to drop with "DR"
Monday March 6, 2017
Spring break recess - Enjoy the week off!
March 13-17, 2017
Holiday, Patriot's Day
Monday April 17, 2017
NO Worcester section class
We will work ONLINE this week
Last (13th) Week of Class
April 24-28, 2017
Last Exam (Exam III) due
Monday May 8, 2017



Course Schedule (# meetings or weeks online = 13)
Please check course website page, THIS WEEK for updates!

Week

Dates

Schedule

 

1

 

 

January 23-27, 2017

 

Unit 1 (Review of PubHlth 540)

 

 

2

 

 

January 30-February 3, 2017

 

Unit 2 (Regression and Correlation) – Part 1 of 2

 

3

 

 

February 6-10, 2017

 

Unit 2 (Regression and Correlation) – Part 2 of 2

 

 

4

 

 

February 13-17, 2017

 

Statistics in Practice - R and Stata Lab
Monday February 13, 2017 - Exam I posted

 

 

5

 

February 20-24, 2017

 

NO Worcester Meeting - We will work online this week
Unit 3 (Discrete Distributions)

 

 

6

 

 

February 27 - March 3, 2017

 

Unit 4 (Categorical Data Analysis)
Monday February 27, 2017 – Exam I due

 


 

7

 

 

March 6-10, 2017

 

Unit 5 (Logistic Regression) – Part 1 of 2

 

 

-

 

 

March 13-17, 2017

 

Spring Break

 

 

8

 

March 20-24, 2017

 

Unit 5 (Logistic Regression) – Part 2 of 2

 

 

9

 

 

March 27-31, 2017

 

Unit 6 (Introduction to Survival Analysis)
Monday March 27, 2017 - Exam II posted

 

 

10

 

 

April 3-7, 2017

 

Unit 7 (Analysis of Variance) - Part 1 of 2

 

 

11

 

 

April 10-14, 2017

Unit 7 (Analysis of Variance) – Part 2 of 2
Monday April 10, 2017 – Exam II due

 

 

12

 

 

April 17-21, 2017

 

Statistics in Practice - R and Stata Lab
NO Worcester Meeting - We will work online this week

 

 

13

 

 

April 24-28, 2017

 

Unit 8 (Introduction to Repeated Measurements)
Monday April 24, 2017 - Final (Exam III) posted

 

 

-

 

 

May 8, 2017

 

Monday May 8, 2017 – Final Exam (Exam III) due

 

 

 

 


Examination Schedule

Exam
Posting
Due Date
Topics Covered
1
Monday February 13, 2017
Monday February 27, 2017
1- Review of BIOSTATS 540
2 - Regression and Correlation
2
Monday March 27, 2017
Monday April 10, 2017
3- Discrete Distributions
4 - Categorical Data Analysis
5 - Logistic Regression
3
Monday April 24, 2017
Monday May 8, 2017
7- Analysis of Variance

Note - There will be no examinations of units 6 (Survival Analysis), 8 (Repeated Measurements) or 9 (Nonparametrics).

Late Submissions Policy
Yes. You can submit an exam late. I will accept late submissions up to one week late. Please be aware, however, that in consideration of your classmates, a lat submission carries a 20 point penalty. Thus, if you know you cannot make a due date, your best bes is to use hte full week grace period.

  Credit Policy
On time
Full credit for points scored
1-7 days late
Points scored - 20 points
8+ days late
0

 

 

ADA Accommodation Policy

 

Any student who, because of a disability, may require special arrangements in order to meet course requirements should contact me as soon as possible to make necessary arrangements.

Carol Bigelow, PhD

tel: 413/545-1319

fax: 413/545-1645

email: cbigelow@schoolph.umass.edu

 


Policy on Academic Dishonesty

 

All students are expected to adhere to guidelines of University of Massachusetts regarding academic honesty. A copy of these guidelines is available online at

www.umass.edu/dean_students/code_conduct/acad_honest.htm

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 advantage in 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).

 

 

 

(Download pdf copy of 2017 syllabus)

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University of Massachusetts at Amherst
Copyright 2017 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: January 12, 2017