MHS506 SLP 4

NEWS – Information from prof

In this module you will learn:

 

1. Differentiate between logistic and linear regression.

2. Interpret the results from the two models (logistic versus linear) that are provided

In the Case assignment you will:

1. Distinguish between univariate and multivariate analysis.

2. Distinguish between dependent and independent variables.

3. Distinguish between logistic and linear regression.

In The SLP assignment you will:

1. Interpret the results of a regression analysis, both linear and logistic.

2. Discuss the concept of confounding and note potential confounders in a hypothetical study.

3. Assess the merits of matching on confounders versus adjusting for confounders by including them in a regression model.

In the Discussion you will Identify confounders for known diseases.

 

In more details

Case

Using the materials in the module homepage and in the background section, please address the following:

· What is the difference between "univariate" and "multivariate" analyses? (1 page)

· Define and contrast dependent versus independent variables. (1 page)

· Describe the difference between logistic regression and linear regression. What types of variables are used for the dependent variable? (1 page)

 

SLP

Interpret the two models that appear below, and address the following additional questions as they pertain to each.

Diabetes (1 unit) = 1.3 + 2.4 (BMI) + 2.3 (family history diabetes) + 1.7 (gender) + 1.4 (age) + 1.7 (race) + 2.6 (income) + 3.4 (height), p<0.05

Allergies = 4.5 + 3.8 (Family History Allergies) + 2.1 (gender) + 1.4 (age) + 0.8 (race) + 1.5 (weight), p<0.05

· What about confounding? Which of the variables are potential confounders?

· Compare and contrast matching on potential confounders versus including them in a regression model.

 

Discussion:

· Confounders Discussion

Discussion Topic

Actions for 'Confounders Discussion'

Updated

Locate and describe a potential confounder linked with a disease. For instance, what is a potential confounder for obesity and diabetes? For smoking and lung cancer?

· Reflection Discussion

Discussion Topic

Actions for 'Reflection Discussion'

Updated

Given the readings and assignments in the course, identify and briefly discuss two concepts that you believe will be most applicable to the professional discipline you will enter upon the completion of your degree program.

 

 

Required Reading

Barrat, H. & Kirwan, M. (2009) Confounding, interactions, methods for assessment of effect modification. Health Knowledge. Retrieved from http://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/confounding-interactions-methods

Collier, W. Independent & dependent variables. University of North Carolina at Pembroke. Retrieved from http://www.uncp.edu/home/collierw/ivdv.htm

DeLong, E., Li, L., & Cook, A., (2014).  Pairing matching vs.stratification in cluster – Randomized trial. NIH Collaboratory

LaMorte, W.W. & Sullivan, L. (2016). Confounding and effect measure modification. Retrieved from http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704-EP713_Confounding-EM/BS704-EP713_Confounding-EM5.html

Lowry, R. (2016). Simple logistical regression. VassarStats: Website for Statistical Computation. http://www.vassarstats.net/logreg1.html

Ludford, P.J. Linear regression. University of Minnesota, College of Science and Engineering. Retrieved from http://www-users.cs.umn.edu/~ludford/Stat_Guide/Linear_Regression.htm

McDonald, J.H.(2014) Logistic Regression. In Handbook of Biological Statistics.Retrieved from http://www.biostathandbook.com/simplelogistic.html

National Science Digital Library's Computation Science Education Research Desk. (2016) Univariate data and bivariate data. Retrieved from http://www.shodor.org/interactivate/discussions/UnivariateBivariate/

National Science Digital Library's Computation Science Education Research Desk. (2016). Graphing and interpreting bivariate data. Retrieved from http://www.shodor.org/interactivate/discussions/GraphingData/

Penn State. (2016). STAT507 Epidemiological Research Methods: 3.5 - Bias, Confounding, and Effect Modification. Retrieved from https://onlinecourses.science.psu.edu/stat507/node/34

Wunsch, G. (2007). Confounding and control. Demographic Research 16(4). Retrieved from http://www.demographic-research.org/Volumes/Vol16/4/16-4.pdf