Biostatistics (SLP 4)

Babyyodaluv

UNIVARIATE VS. BIVARIATE ANALYSES AND REGRESSION

Assignment
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

What about confounding? Which of the variables are potential confounders?
Compare and contrast matching on potential confounders versus including them in a regression model.

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.

Length: At least 2 pages in length, APA format, scholarly sources, use subheadings.

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

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

MarinStatsLectures. (2018). One Way ANOVA (Analysis of Variance): Introduction | Statistics Tutorial #25. https://www.youtube.com/watch?v=_VFLX7xJuqk

Public Health Action Support Team (PHAST). (2020). Role of chance, bias and confounding in epidemiological studies. https://www.healthknowledge.org.uk/e-learning/epidemiology/practitioners/chance-bias-confounding

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