A practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction.

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From the course by Johns Hopkins University

Statistical Reasoning for Public Health 2: Regression Methods

51 ratings

Johns Hopkins University

51 ratings

A practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction.

From the lesson

Module 3A: Multiple Regression Methods

This module extends linear and logistic methods to allow for the inclusion of multiple predictors in a single regression model.

- John McGready, PhD, MSAssociate Scientist, Biostatistics

Bloomberg School of Public Health

>> Greetings!

Â John here, again, back at you with some more multiple regression action.

Â In this lecture set, we'll take on the situation where outcome is binary, and

Â we have potentially multiple predictors.

Â In other words, we'll be doing multiple logistic regression.

Â And it will be a hopefully logical extension of what we set up with simple

Â logistic regression.

Â And we'll see, just like we could do with multiple linear regression,

Â we can look at the predictive power of multiple factors on an outcome at once and

Â see if they independently,

Â above and beyond each other, contribute information to the outcome.

Â And we can also easily get adjusted estimates of the binary outcome,

Â expose our relationships with our multiple logistic regression model.

Â And these will be in the form of leangains ratios,

Â that we can then exponentiate to get odds ratios.

Â We'll also see we can actually use the results of multiple logistic regression to

Â predict the probability or proportion that different subgroups of

Â a population based on their x values have the binary outcome.

Â So hopefully, this will all be a nice extension of what we did

Â in lecture set two.

Â And then, when we get to lecture set nine, we'll delve into multiple regression

Â models and exploring effect modification, including that with logistic regression.

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