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


From   "Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: combining/integrating the results of -stepwise- and -mim- for variable selection after multiple imputation
Date   Fri, 10 Sep 2010 09:19:15 -0700

I think you are correct.  I did get an experimental version called mimsw from Patrick Royston and I thought he'd updated mim with that.  He may be able to let us know if it's part of mim. 

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: Friday, September 10, 2010 9:07 AM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: RE: combining/integrating the results of -stepwise- and -mim- for variable selection after multiple imputation

Hey, Tony

I'll look into -ice- further but I believe the stepwise option in the
-ice- command allows for stepwise variable selection of the prediction
models used within the imputation.  I'm looking for a way to perform
stepwise regression on previously imputed data in which the Wald test
statistics used in the stepwise regression a generated via
-micombine-.

Jordan



On Thu, Sep 9, 2010 at 5:47 PM, Lachenbruch, Peter
<Peter.Lachenbruch@oregonstate.edu> wrote:
> 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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