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 4: Additional Topics in Regression

- John McGready, PhD, MSAssociate Scientist, Biostatistics

Bloomberg School of Public Health

Greetings everyone.

Â John here again.

Â In this next lecture set, lecture ten, we'll talk briefly about

Â another method that can be used when we want to control for

Â a lot of potential confounders, but we're not particularly interested in

Â their relationships with the outcome after adjustment.

Â What we're really interested in an observational study sense is

Â the relationship between the outcome and a single predictor,

Â like whether a person self-selected to be in an intervention group or control group,

Â but because of the observational nature of the study it's necessary to control for

Â potential differences.

Â And we may have a lot of potential confounders measured and collected on our

Â participants and non-participants in the intervention that we'd like to adjust for,

Â but again, we're not interested in their associations with the outcome.

Â With traditional regression what we do is run a multiple regression where we

Â included our predictor of interest plus all of the potential confounders

Â to adjust.

Â And then we may iterate and pull out some of the potential confounders that did

Â not appear to be associated, statistically speaking, after adjustment for

Â the others, et cetera, but there's a limitation as to

Â how many potential confounders we can include because we only have so

Â much data and can only estimate so many slopes in our regression.

Â So another approach has come along that will allow us to take the information

Â about confounder distributions between persons in the intervention group and

Â persons who self-selected to be in the control group, and

Â take this information across many potential confounders and

Â turn it into a single score, such that persons with single scores closer to

Â each other are more similar in their distribution of confounders.

Â And this approach is called creating something called a propensity score.

Â And then, instead of doing the traditional regression approach,

Â where we regress our outcome on our main predictor of interest and a bunch of

Â other x's to represent our potential confounders, we can reduce the information

Â about the confounder distributions to this single score and adjust for it alone.

Â And that will allow us to estimate potentially a more

Â precise outcome exposure relationship, because we don't have to

Â estimate a bunch of ancillary slopes that we're not interested in

Â by including each of our confounders as individual predictors in the regression.

Â This also will allow us in situations, and

Â we'll talk about reasons why you may want to do this and

Â sometimes its done in the literature, to match people in the intervention and

Â control groups based on their confounder distributions using a single score,

Â such again that people who are closer in their scores are more similar on their

Â distribution of the potential confounders.

Â So in this unit we'll talk about the idea of propensity scores,

Â generally how they're created, and these can really only be used when our

Â predictor of interest is either binary which is the case we'll focus on, but

Â the ideas can be extended to when it's categorical.

Â If the predictor of interest is continuous it either needs to be dichotomized or

Â categorized to do this.

Â But we'll talk about situations on how to estimate the propensity score,

Â what the logic is behind it.

Â We'll compare the results from a study where we adjust the traditional way to

Â adjusting using the propensity score.

Â And we'll look at some examples of propensity score adjustment literature,

Â and then we'll also talk briefly about situations where it may make sense to

Â take everybody who gets the intervention and match them to a subset of people in

Â the control group based on the propensity score distributions.

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