140.622.02 STATISTICAL METHODS IN PUBLIC HEALTH II

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.

Students who successfully master this course will be able to: 1) Use statistical reasoning to formulate public health questions in quantitative terms [1.1 Distinguish the summary measures of association applicable to retrospective and prospective study designs; 1.2 Distinguish between the appropriate regression models for handling continuous outcomes, binary outcomes and time-to-events; 1.3 Conduct an intent-to-treat statistical analysis of data from a randomized community trial and correctly interpret the findings about the treatment efficacy; 1.4 Conduct a basic analysis of data from a cohort study and correctly interpret the findings about the association between exposure and outcome; 1.5 Conduct a basic analysis of data from a case-control study and correctly interpret the findings about exposure and outcome; 1.6 Use stratification in design and analysis to minimize confounding and identify effect modification]; 2)Design and interpret graphical and tabular displays of statistical information [2.1 Use the statistical analysis package Stata to construct statistical tables and graphs of journal quality]; 3) Use probability models to describe trends and random variation in public health data [3.1 Distinguish among the underlying probability distributions for modeling continuous, categorical, binary and time-to-event data; 3.2 Calculate the sample size necessary for estimating either a continuous or binary outcome in a single group; 3.3 Estimate the sample size necessary for determining a statistically significant difference in either a continuous or binary outcome between two groups; 3.4 Recognize the assumptions required in performing statistical tests assessing relationships between an outcome and a risk factor]; 4) Use statistical methods for inference, including confidence intervals and tests, to draw valid public health inferences from study data [4.1 Estimate two proportions and their difference, and confidence intervals for each. Interpret the interval estimates within a scientific context. Recognize the importance of other sources of uncertainty beyond those captured by the confidence interval; 4.2 Estimate an odds ratio or relative and its associated confidence interval. Explain the difference between the two and when each is appropriate; 4.3 Perform and interpret one-way analysis of variance to test for differences in means among three or more populations. Evaluate whether underlying probability model assumptions are appropriate; 4.4 Contrast mean outcomes among pairwise groups using multiple comparisons procedures; 4.5 Interpret the correlation coefficient as a measure of the strength of a linear association between a continuous response variable and a continuous predictor variable; 4.6 Perform and correctly interpret the results from a simple linear regression analysis to describe the dependence of a continuous response variable on a single predictor variable; 4.7 Use data transformations such as logs and square roots so that regression model assumptions are more nearly satisfied].