140.612.81 STATISTICAL REASONING IN PUBLIC HEALTH II
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, p-values, and confidence intervals. Topics include 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.
Upon successfully completing this course, students will be able to:
Interpret the results from simple linear regression to assess the magnitude and significance of the relationship between a continuous outcome variable and a binary, categorical or continuous predictor variable.
Assess the strength of a linear relationship between two continuous variables via the coefficient of determination (R squared) and/or it's counterpart, the correlation coefficient
Interpret the results from simple logistic regression to assess the magnitude and significance of the relationship between a binary outcome variable and a binary, categorical or continuous predictor variable
Interpret the results from simple Cox regression to assess the magnitude and significance of the relationship between a time to event variable and a binary, categorical or continuous predictor variable
Explain the assumption of proportional hazards, and what this mean regarding the interpretation of hazard (incidence rate) ratios from Cox regression models
Explain how most of the hypotheses tests covered in Statistical Reasoning 1 can be expressed as simple regression models
Describe the conditions necessary for an exposure/outcome relationship to be confounded by one or more other variables
Explain how to interpret an adjusted association
Explain the concept of effect modification, and how it differs from confounding
Describe the process for assessing whether an outcome/exposure association is modified by another factor.
Discuss why multiple regression techniques allow for the analysis of the relationship between an outcome and a predictor in the presence of confounding variables
Utilize the results from all regression types covered (linear, logistic and Cox) to assess confounding and effect modification.
Use the results from linear regression models to predict the mean value of a continuous outcome variable for different subgroups of a population defined by different predictor set values.
Use the results from logistic regression models to predict the probability of a binary condition for different subgroups of a population defined by different predictor set values.
Explain what a propensity score is, and how it can be useful for estimated an adjusted outcome/exposure relationship ion the presence of potentially many confounders
Introduction to Online Learning is required prior to participating in any of the School's Internet-based courses..