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

[Jordan Hoolachan <jihool3670@gmail.com>]

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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