# 140.622.02 Statistical Methods in Public Health II

Department:
Biostatistics
Term:
2nd term
Credits:
4 credits
2017 - 2018
Location:
East Baltimore
Class Times:
• Tu Th,  10:30 - 11:50am
Lab Times:
• Monday,  1:30 - 3:00pm (01)
• Tuesday,  1:30 - 3:00pm (02)
• Wednesday,  1:30 - 3:00pm (03)
• Thursday,  1:30 - 3:00pm (04)
• Friday,  1:30 - 3:00pm (05)
• Monday,  3:00 - 5:00pm (06)
• Tuesday,  3:00 - 5:00pm (07)
• Wednesday,  3:00 - 5:00pm (08)
• Thursday,  3:00 - 5:00pm (09)
Auditors Allowed:
Yes, with instructor consent
Contact:
Karen Bandeen-Roche
Course Instructors:
Resources:
Prerequisite:

140.621

Description:

Presents use of likelihood functions, confidence intervals, and hypothesis tests to draw scientific inferences from public health data. Discusses null and alternative hypotheses, Type I and II errors, and power. Develops parametric and non-parametric statistical methods for comparing multiple groups (ANOVA). Also introduces measures of association and simple linear regression. Addresses methods for planning a study, including stratification, balance, sampling strategies, and sample size.

Learning Objectives:

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

1. Distinguish the summary measures of association applicable to retrospective and prospective study designs
2. Distinguish between the appropriate regression models for handling different types of public health outcomes
3. Recognize the assumptions required in using regression models and performing statistical tests to assess relationships between an outcome and a risk factor
4. Perform and interpret a one-way analysis of variance to test for differences in means among three or more populations
5. Contrast mean outcomes among pairwise groups using multiple comparisons procedures
6. Interpret the correlation coefficient as a measure of the strength of a linear association between a continuous response variable and a continuous predictor variable
7. Interpret the coefficients, including interaction coefficients, obtained from either a multiple linear regression or multiple logistic regression analysis
8. Calculate the sample size necessary for estimating either a continuous or binary outcome in a single group or difference between two groups
9. Calculate the sample size necessary for determining a statistically significant difference in either a continuous or binary outcome for either one group or between two groups
10. Use the Stata statistical analysis package to perform regression analyses and sample size estimation
Methods of Assessment:

Student evaluation based on problem sets and exams.

Enrollment Restriction:

For PhD, ScM and MHS degree candidates in departments to be determined

Instructor Consent:

Consent required for some students

Consent Note:

Consent required for non-PH students

For consent, contact:

kbandeen@jhsph.edu