[R-sig-ME] Combination of glmnet-like covariate selection with mixed modeling

Ben Bolker bbolker at gmail.com
Sat Jun 18 03:14:30 CEST 2016

  Maybe check out the glmmLasso package?

On 16-06-17 06:40 PM, Hunsicker, Lawrence wrote:
> Greetings, listserv members:
> I am involved in the analysis of factors predictive of whether a
> person that dies in a hospital becomes a transplant organ donor.  To
> do this analysis, with the help of the NCHS we have linked the list
> of all organ donors over a seven year period with information of all
> US deaths over this period obtained from death certificates.  As you
> might imagine, this is a rather "big data" analysis, with nearly
> 40,000 donors among about 2,500,000 deaths.
> There is also a very large number of ICD-9 codes (and other
> information) listed in the death certificates.  We anticipate that we
> will need to reduce the dimensionality of the problem for it to
> become practical, let alone intelligible, and we are planning to use
> the grpreg (in R) package to do a two level selection of the most
> relevant covariates.  But our data also have a nested structure in
> terms of the US geographical areas of interest -- US counties within
> the designated service areas of the OPOs (organ procurement
> organization).   I am not aware of a package that deals
> simultaneously with covariate selection (a la glmnet or similar
> packages) and mixed modeling.  I am addressing this e-mail to you all
> as folks that are expert in the issue of mixed models.
> I have read that in fitting a mixed model, one fits first the fixed
> effects, and then looks for additional explanatory structure among
> the random effects.  This has suggested to me that one could approach
> the above problem in a two step manner, first reducing the
> dimensionality of the problem and deriving coefficients from the
> glmnet-type analysis, and then doing a mixed model analysis on the
> residuals from the above.
> So the basic question is whether something along the above lines
> makes sense.  I would deeply appreciate any suggestions or pointers
> to relevant literature that I could use to understand all this
> better.
> Many thanks in advance for your help.
> Larry Hunsicker L. G. Hunsicker, M.D., Professor (Emeritus) of
> Internal Medicine U. Iowa College of Medicine 319-621-3576 (Voice) 
> lawrence-hunsicker at uiowa.edu<mailto:lawrence-hunsicker at uiowa.edu>

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