Harvard School of Public Health
Health in Numbers: Quantitative Methods in Clinical & Public Health Research
Learning Objectives
At the completion of the course, the student will be able to use the statistical package Stata to
analyze descriptive and graphical data and perform inferential statistical methods. The student
will also be able to describe the most common uses of numerical methods needed to describe
Clinical and Public Health research methods that include the following capabilities:
1) Be a critical consumer of the public health and medical literature by describing the basic
principles of quantitative methods, including disease (outcome) measures, measures of
association, study design options, bias, confounding, and effect modification.
2) Describe the use of random variables, measurement scales, descriptive statistics
(measures of central tendency and variation), probability distributions, and sampling.
3) Interpret descriptive epidemiologic results in order to develop hypotheses about possible
risk factors for a disease.
4) Standardize rates and create and describe life tables.
5) Apply the fundamentals of probability theory including concepts of event outcomes,
mutual exclusivity, and independence.
6) Design valid and efficient studies to address public health and clinical problems.
7) Apply inferential methods, including developing hypotheses, constructing and describing
confidence intervals, defining study outcomes and explanatory factors, and evaluating
errors in measurement.
8) Describe power and sample size calculations.
9) Apply and interpret methods for the analysis of tabular and discrete data (contingency
tables, Mantel-Haenszel methods).
10) Apply and interpret methods for correlation and regression analyses (linear regression,
analysis of variance, and logistic regression) and prediction.
11) Recognize and formulate well defined questions concerning causal effects.
12) Identify the key assumptions for causal inference from observational data, and conduct
simple analyses to estimate causal effects under those assumptions.
13) Use causal diagrams to represent a priori subject-matter knowledge, assumptions, and
epidemiologic biases.
14) Describe the role of subject-matter knowledge in observational research.
15) Evaluate quantitative analytic methods used in papers published in the public health
literature using knowledge of probability and statistical inference.
16) Describe the factors that go into designing public health and medical studies, including
sample size, sampling schemes, and their implications on the final interpretation of a
data analysis.
