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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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 3B: More Multiple Regression Methods

This set of lectures extends the techniques debuted in lecture set 3 to allow for multiple predictors of a time-to-event outcome using a single, multivariable regression model.

- John McGready, PhD, MSAssociate Scientist, Biostatistics

Bloomberg School of Public Health

Greetings. John here again, and

Â again here to talk about multiple logistic regression action.

Â This time, when we're actually regressing time to event data in the presence of

Â the censoring on predictors of interest.

Â So, we're going to use a multiple Cox regression approach,.

Â And, this section will show that the ideas of in terms of

Â interpreting the results from Cox regression.

Â As adjusted measures of association are very parallel to what we did with

Â logistic regression for odd ratios and linear regression for mean differences.

Â We'll also see that we can compare the results form unadjusted Cox regressions,

Â or simple Cox regressions, to the results from,

Â and that, a model includes multiple predictors.

Â To get a sense of whether relationships of interest were confounded by other factors.

Â We'll also see in this sections,

Â while it's not something we could easily do by hand.

Â We could certainly invoke a computer and

Â sometimes you'll see the results of such analyses done in papers where they use

Â the results from multiple Cox regression models.

Â To actually estimate survival curves of an event for

Â different subgroups of the population based on their X values.

Â So, for example separate curves for

Â males of a certain age with a certain condition compared to separate curves for

Â females of the same ages with a different condition, et cetera.

Â And, they can show the estimated survival curves, translate these

Â adjusted hazard ratios into what they mean in terms of the percent surviving or

Â remaining event free across the follow up period.

Â So, at the end of this free lecture trifecta here on using multiple regression

Â for an estimation adjustment, we've now have some sense of how to get adjusted

Â associations using multiple regressions and compare them to their unadjusted

Â associations from simple regressions to look at the degree of confounding.

Â And, also use multiple regression as a tool to be able to

Â better predict our outcome by using more than one predictor at a time.

Â In the next lecture section, lecture nine, we'll briefly talk about how each of

Â these regression approaches can be altered to allow for the investigation.

Â Both of effect modification and also how to estimate,

Â deviations from the linearity assumption without making our cat,

Â continuous predictor categorical by default.

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