140.612.11
Statistical Reasoning in Public Health II
 Location:
 East Baltimore
 Term:
 Summer Inst. term
 Department:
 Biostatistics
 Credits:
 3 credits
 Academic Year:
 2022  2023
 Instruction Method:
 Inperson
 Dates:
 Wed 06/22/2022  Fri 07/01/2022
 Class Times:

 M Tu W Th F, 1:30  5:00pm
 Auditors Allowed:
 No
 Grading Restriction:
 Letter Grade or Pass/Fail
 Course Instructor:

 Daniel Obeng
 Contact:
 Ayesha Khan
 Resources:
 Prerequisite:
 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, pvalues, 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:
 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 nonrandomized 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, KaplanMeier 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 pvalues from ttests and analysis of variance (ANOVA) for mean differences between populations  Interpret pvalues from ztests, chisquare tests and Fisherâ€™s Exact test for comparing proportions between populations  Interpret pvalues from ztests, and logrank tests for comparing timetoevent 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
 Instructor Consent:
No consent required
 Special Comments:
This is a hybrid course with both an inperson section (140.612.11) and a synchronous online section (140.612.49). Please choose the modality you need (either inperson or online) when registering in SIS.