RE: st: RE: combining/integrating the results of -stepwise- and -mim- for variable selection after multiple imputation

["Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>]

Update your mim.  
A simple matter should be to issue the adoupdate command.  I went to the help page and looked for the stepwise option, but didn't find it.  I had seen it earlier.  
Maybe the authors can help.  
It seems to be implemented in ice:  Here's an excerpt from the help file


Syntax

        ice [mainvarlist] [if] [in] [weight] [, major_options
            less_used_options]

<snip>
    options                       description
    ------------------------------------------------------------------------
    ice major_options
      clear                       clears the original data from memory and
                                    loads the imputed dataset into memory
      dryrun                      reports the prediction equations - no
                                    imputations are done
      eq(eqlist)                  defines customised prediction equations
      m(#)                        defines the number of imputations
      match(varlist)              prediction matching for each member of
                                    varlist
      passive(passivelist)        passive imputation
      saving(filename [,replace]) imputed and non-imputed variables are
                                    stored to filename
      stepwise                    constructs prediction equations by                                                                                                                                                             stepwise variable selection
      swopts(stepwise_options)    options for stepwise

    ice stepwise_options
      forward                     perform forward-stepwise selection
      group(group_list)           create groups of variables for joint
                                    testing for addition or removal
      lock(varlist)               Variables to be kept in all models
      pe(#)                       significance level for addition to a model
      pr(#)                       significance level for removal from a model
      show                        show each stepwise regression


Tony

Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Jordan Hoolachan
Sent: Thursday, September 09, 2010 12:20 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: RE: combining/integrating the results of -stepwise- and -mim- for variable selection after multiple imputation

Tony,

I'm not sure what option you are referring to.  I've tried the command

xi: mim, cat(combine): stepwise, pr(0.05): logistic ...

but receive the message "prefix stepwise is not allowed after -mim-" .
I also don't see any mention of a stepwise command within the -mim-
help page.

Can you be more specific?

Jordan



On Thu, Sep 9, 2010 at 2:51 PM, Lachenbruch, Peter
<Peter.Lachenbruch@oregonstate.edu> wrote:
> Check out mim.  It has a stepwise option.  Works well.
>
> Tony
>
> Peter A. Lachenbruch
> Department of Public Health
> Oregon State University
> Corvallis, OR 97330
> Phone: 541-737-3832
> FAX: 541-737-4001
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Jordan Hoolachan
> Sent: Thursday, September 09, 2010 11:30 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: st: combining/integrating the results of -stepwise- and -mim- for variable selection after multiple imputation
>
> Dear All,
>
> I am using Stata 11.1 and attempting to perform variable selection
> after multiple imputation.  All 10 imputed datasets are currently
> stacked into one large data set with "_mj" identifying the dataset to
> which an observation belongs and "_mi" identifying observations within
> a data set.
>
> In their paper "How should variable selection be performed with
> multiply imputed data?", Wood et al. (2008) identify a model selection
> approach (the "RR appoach") that utilizes Rubin's rules for estimating
> parameters and standard errors across imputed data sets.
> Specifically, "each model selection step involves fitting the model
> under consideration to all data sets and combining estimates across
> imputed data sets."  The only information that they provide in regards
> to actually doing this in Stata is the following: "For the RR method,
> -stepwise- was modified to use the Wald test statistics from
> -micombine- ."
>
> I am only an intermediate Stata user on my best days so I'm not even
> really sure where to start on this.  It seems like I need to code an
> iterative procedure in which the results of each -logistic- command
> run under -stepwise- are fed to -micombine (or -mim-) which then
> combines the results across the imputed data sets and finally feeds
> the resulting Wald test statistic back to -stepwise- in order for the
> next -logistic- command to be able to run.  Any advice do doing on
> setting up this type of program?
>
> This is the web address of the the Wood et al. paper for your
> reference: http://onlinelibrary.wiley.com/doi/10.1002/sim.3177/abstract
> Unfortunately, access to the full .pdf is only granted if you have a
> subscription.  I couldn't find a location in which it is available to
> everyone.
>
> Thanks for the consideration!
>
> Jordan
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