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From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: sw command within bstrap |

Date |
Fri, 4 Feb 2005 19:41:23 -0500 |

you would have to do some programming, so that your -bootstrap- command calls the routine you wrote, rather Stata's -sw- command. That would be my guess if -bootstrap- does not work directly with -sw-. Typically, you would do something like bootstrap "logistic outcome stuff" _b _se as shown in the examples. For the stepwise methods, however, you would run into troubles that different boostrap samples may produce different sets of the selected variables. So your wrapper around this would have to indicate explicitly that the values of the coefficient estimates of the variable not included into the model should be set to missing: program define crazy, rclass syntax varlist sw logistic outcome `varlist' , sw options foreach x of `varlist' { cap local b`var' = _b[`var'] if _rc { local b`var' = . } local blist `blist' (b`var') } return local blist `blist' end bootstrap "crazy varlist" `r(blist)' , bootstrap options I am pretty sure it won't work, but it should give you a direction to go. Stas On Fri, 4 Feb 2005 11:45:29 +0000, pra06 <e.thomas@cphc.keele.ac.uk> wrote: > Hi all > > I am embarking on a project to derive a clinical prediction rule (using > logistic regression) and it has been suggested that I should use a > bootstrapping technique to study the internal validity of the final prediction > model in order to derive bias-corrected coefficients/odds ratios. > > Perusing the manuals I came across the bstrap command which seems like a good > place to start. I have been able to use a simple logistic regression in the > bstrap command to calculate bias-corrected coefficients/odds ratios. However, > I have seen articles that have suggested that they have used a stepwise > command within the bootstrapping routine, ie replicating the exact same > procedure that I will used to arrive at the final prediction model in each of > the 1000 samples. I am unable to get bstrap to implement the sw command. > > I know that I can simply derive 1000 samples and apply the sw procedure to > each sample. However, I am unsure as to how to assimilate the data from these > 1000 samples as not only will the magnitude of the coefficients vary in each > sample (simple part!) but the actual variables chosen by each sw procedure may > be different in each sample. > > Any help with programming in Stata or detailed examples in the literature > (many just glibly state that this process was carried out – very helpful!) > would be appreciated > > Best wishes > Elaine Thomas > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > -- Stas Kolenikov http://stas.kolenikov.name * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: sw command within bstrap***From:*pra06 <e.thomas@cphc.keele.ac.uk>

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