 # 140.612.11Statistical Reasoning in Public Health II

Location:
East Baltimore
Term:
Summer Inst. term
Department:
Biostatistics
Credits:
3 credits
2022 - 2023
Instruction Method:
In-person
Dates:
Wed 06/22/2022 - Fri 07/01/2022
Class Times:
• M Tu W Th F,  1:30 - 5:00pm
Auditors Allowed:
No
Course Instructor:
• Daniel Obeng
Contact:
Ayesha Khan
Resources:
Prerequisite:

140.611

Description:

Provides a broad overview of biostatistical methods and concepts used in the public health sciences, emphasizing interpretation and concepts rather than calculations or mathematical details. Develops ability to read the scientific literature to critically evaluate study designs and methods of data analysis. Introduces basic concepts of statistical inference, including hypothesis testing, p-values, and confidence intervals. Includes topics: comparisons of means and proportions; the normal distribution; regression and correlation; confounding; concepts of study design, including randomization, sample size, and power considerations; logistic regression; and an overview of some methods in survival analysis. Draws examples of the use and abuse of statistical methods from the current biomedical literature.

Learning Objectives:

Upon successfully completing this course, students will be able to:

1. Provide examples of different types of data arising in public health studies
2. Explain the basic differences between different study designs for comparing populations and recognize the issue of confounding when interpreting results from non-randomized studies
3. Interpret differences in data distributions via visual displays
4. Explain the difference between a sample and a population
5. Calculate standard normal scores and resulting probabilities
6. Calculate and interpret confidence intervals for population means and proportions and incident rates using data from single samples
7. Compute the mean difference and explain why a mean difference can be used to quantify differences in a continuous measure between two samples (and ultimately two populations)
8. Compute risk differences, relative risks and odds ratio, and interpret the quantities
9. Compute incidence rates and incidence rate ratios
10. Construct, and interpret, Kaplan-Meier estimates of the survival function that describes the "survival experience" of a cohort of subjects
11. Explain and unify the concept of a confidence interval whether it be for a single population quantity, or a comparison of populations
12. Compute confidence intervals for population mean differences, difference in proportions, relative risks, odds ratios and incidence rate ratios
13. Explain why computations for ratios are performed on the (natural) log scale
14. Perform hypothesis tests for comparisons of more than two populations: - Interpret p-values from t-tests and analysis of variance (ANOVA) for mean differences between populations - Interpret p-values from z-tests, chi-square tests and Fisher’s Exact test for comparing proportions between populations - Interpret p-values from z-tests, and log-rank tests for comparing time-to-event outcomes between populations
15. Explain the role of sample size in determining margin of error (confidence interval width) and compute the necessary sample size(s) to obtain a desired margin of error
Methods of Assessment:

This course is evaluated as follows:

• 40% Homework
• 60% Final Exam

Instructor Consent:

No consent required