ASA Connect

Need a statistical modeling tune-up or to keep up to date with modern methods for developing and validating predictive models? Contrast regression with machine learning?


Regression Modeling Strategies Virtual Short Course 2021
Frank E. Harrell, Jr., Ph.D.
May 11-14 2021, R Workshop May 10
Course web site: https://hbiostat.org/doc/rms/4day.html

The course includes statistical methodology, case studies, and use of
the R statistical computing language. Emphasis is on developing predictive models, model validation, and quantifying predictive accuracy, plus many more topics including navigating the choice of statistical models vs. machine learning, dealing with missing data, unsupervised learning (data reduction) and more.

Please email questions to fh@fharrell.com

I highly recommend this course. I've taken it a couple of times and it has greatly improved my work.

A recent example is covid-19 work. Solid regression methods helped to get a better grip on our situation (a large hospital system in California).

What I learned in Harrell's course allowed us to do good work with small samples. We obviously couldn't afford to wait to for bigger samples needed in other approaches (e.g., machine learning) for stable model validation (van der Ploeg, 2014). 

Harrell's course will help you to make the right decisions at the right time.

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Stephen Zuniga
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Original Message:
Sent: 03-22-2021 12:43
From: Frank Harrell
Subject: Regression Modeling Strategies Virtual Short Course 2021

Need a statistical modeling tune-up or to keep up to date with modern methods for developing and validating predictive models? Contrast regression with machine learning?


Regression Modeling Strategies Virtual Short Course 2021
Frank E. Harrell, Jr., Ph.D.
May 11-14 2021, R Workshop May 10
Course web site: https://hbiostat.org/doc/rms/4day.html

The course includes statistical methodology, case studies, and use of
the R statistical computing language. Emphasis is on developing predictive models, model validation, and quantifying predictive accuracy, plus many more topics including navigating the choice of statistical models vs. machine learning, dealing with missing data, unsupervised learning (data reduction) and more.

Please email questions to fh@fharrell.com

I second the positive comments on Dr. Harrell's short course. It definitely influenced how I think about regression and associated modeling strategies in a very overarching and influential way.

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Douglas Landsittel
Professor of Biomedical Informatics
University of Pittsburgh
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Original Message:
Sent: 03-23-2021 14:16
From: Stephen Zuniga
Subject: Regression Modeling Strategies Virtual Short Course 2021

I highly recommend this course. I've taken it a couple of times and it has greatly improved my work.

A recent example is covid-19 work. Solid regression methods helped to get a better grip on our situation (a large hospital system in California).

What I learned in Harrell's course allowed us to do good work with small samples. We obviously couldn't afford to wait to for bigger samples needed in other approaches (e.g., machine learning) for stable model validation (van der Ploeg, 2014).

Harrell's course will help you to make the right decisions at the right time.

------------------------------
Stephen Zuniga

Original Message:
Sent: 03-22-2021 12:43
From: Frank Harrell
Subject: Regression Modeling Strategies Virtual Short Course 2021

Need a statistical modeling tune-up or to keep up to date with modern methods for developing and validating predictive models? Contrast regression with machine learning?


Regression Modeling Strategies Virtual Short Course 2021
Frank E. Harrell, Jr., Ph.D.
May 11-14 2021, R Workshop May 10
Course web site: https://hbiostat.org/doc/rms/4day.html

The course includes statistical methodology, case studies, and use of
the R statistical computing language. Emphasis is on developing predictive models, model validation, and quantifying predictive accuracy, plus many more topics including navigating the choice of statistical models vs. machine learning, dealing with missing data, unsupervised learning (data reduction) and more.

Please email questions to fh@fharrell.com