Course Descriptions

HPA 472 - CLINICAL RESEARCH METHODS I

This course reviews experimental and quasi-experimental study designs and introduces descriptive statistics. Specific topics will include types of study designs, confounding, bias and evaluating study designs. The introduction to descriptive statistics will include basic statistical tests, p-values, confidence intervals, tests of significance, type I and II error, and probability distributions. 

Learning Objectives:

• Demonstrate an understanding of the role that statistics play in carrying out the core functions of public health
• Demonstrate knowledge about descriptive statistics and ways they are used to summarize data
• Generate descriptive statistics using data analysis software
• Implement basic methods of statistical estimation (point estimates, confidence intervals)
• Write programs to create SAS datasets from raw data files
• Assess the advantages and disadvantages of various study designs
• Describe elementary concepts of probability theory and apply them to random sampling and the normal and binomial distributions
• Calculate the probability of observed differences under the null hypothesis, the confidence interval and the probability of Type I and II errors
• Perform a basic sample size and power calculation

Topics Covered:

1. Study Designs

• Study design
• Causal relationship and confounding
• Chance and statistical significance
• Internal and external validity
• Generalizability
• Criteria for evaluating study designs
• Randomized Double Blinded Trials: gold standard for causality but lengthy, expensive and limited generalizability
• Quasi-experimental
• Case-Control studies
• Cohort studies
• Historical controls
• Cross sectional
• Exploratory
• Observational and natural history
• Retrospective and prospective studies
• Choosing a study design for a study

 2. Introduction to Statistics

• Overview PC-SPSS
• Data management principles
• Programs to create SPSS datasets from raw data files
• Creating and recoding variables
• Major secondary data sets (e.g. NHANES, NHIS)
• Using SPSS for descriptive statistics
• Probability and probability distributions
• Measures of central tendency and measures of variability
• Plots
• Skewedness and higher orders
• Binomial and normal distributions
• Parametric and non-parametric
• Sample vs. sample or vs. population differences
• T-tests
• Standard scores
• Confidence interval
• Type I & II error
• Sensitivity and specificity
• Power and sample size
• Bivariate analysis
• Cross Tabs
• Correlation
• Spearman rank
• Pearson
• Chi-square independence
• Chi-square observed vs. expected
• Mantel-Haenszel

Prerequisites:
Graduate or professional standing and approval of the department