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

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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!

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

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

we have potentially multiple predictors.

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

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

logistic regression.

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

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

see if they independently,

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

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

expose our relationships with our multiple logistic regression model.

And these will be in the form of leangains ratios,

that we can then exponentiate to get odds ratios.

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

predict the probability or proportion that different subgroups of

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

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

in lecture set two.

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

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

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