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140.612.49
Statistical Reasoning in Public Health II

Location

Internet

Term

Summer Institute

Department

Biostatistics

Credit(s)

3

Academic Year

2022 - 2023

Instruction Method

Synchronous Online

Start Date

Wednesday, June 22, 2022

End Date

Friday, July 1, 2022

M, Tu, W, Th, F, 1:30 - 5:00pm

Auditors Allowed

No

Available to Undergraduate

No

Grading Restriction

Letter Grade or Pass/Fail

Course Instructor(s)

Contact Name

Frequency Schedule

Every Year

Resources

Prerequisite

140.611

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. Topics include 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:
- Provide examples of different types of data arising in public health studies
- Explain the basic differences between different study designs for comparing populations and recognize the issue of confounding when interpreting results from non-randomized studies
- Interpret differences in data distributions via visual displays
- Explain the difference between a sample and a population
- Calculate standard normal scores and resulting probabilities
- Calculate and interpret confidence intervals for population means and proportions and incident rates using data from single samples
- 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)
- Compute risk differences, relative risks and odds ratio, and interpret the quantities
- Compute incidence rates and incidence rate ratios
- Construct, and interpret, Kaplan-Meier estimates of the survival function that describes the "survival experience" of a cohort of subjects
- Explain and unify the concept of a confidence interval whether it be for a single population quantity, or a comparison of populations
- Compute confidence intervals for population mean differences, difference in proportions, relative risks, odds ratios and incidence rate ratios
- Explain why computations for ratios are performed on the (natural) log scale
- 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
- 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

This is a hybrid course with both a synchronous online section (140.612.49) and an in-person section (140.612.11). Please choose the modality you need (either online or in-person) when registering in SIS.