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From |
"Paul Walsh" <yousentwhohome@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: R: Can I repeatedly sample with constraints from an unbalanced data set to balance it? |

Date |
Sat, 27 Oct 2007 15:53:36 -0700 |

Thanks Carlo, I ll chew it over Paul On 10/27/07, Carlo Lazzaro <carlo.lazzaro@tin.it> wrote: > > Dear Paul, > > provided that I have figured out correctly your research need, as a > sensitivity analysis of your base case results on effectiveness, you might > find useful to perform a permutation test (see - help permute - ) on the two > samples of patient you are comparing. > > As you are surely aware of, the theorical hypotheses of this random > resampling without reintroduction test are well reported in: > > Efron B, Tibshirani JT. An Introduction to the Bootstrap. New York: > Chapman&Hall 1993: 202-219 (particularly). > > Sorry I cannot be more helpful. > > Kind Regards, > > Carlo > -----Messaggio originale----- > Da: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Paul Walsh > Inviato: sabato 27 ottobre 2007 17.47 > A: statalist@hsphsun2.harvard.edu > Oggetto: st: Can I repeatedly sample with constraints from an unbalanced > data set to balance it? > > I have a 700 subject data set of a clinical trial comparing two > treatments for outcome (hospital admission from an emergency room) for > a particular disease. I am also using a three point ordinal scale of > disease severity that strongly predicts hospital admission regardless > of treatment. Though the trial was designed to balance the two > treatment arms, calculating disease severity is too cumbersome to have > included it to balance each treatment arm with equal numbers of each > severity category in the emergency room. Thus there is an unequal > distribution of severity of cases in the two arms. When I calculate > the unadjusted risk ratio of admission for two treatments I obtain a > low, non-significant crude RR, similar to already published studies > that did not account for severity. When I model the treatments and > include the severity score, the adjusted RR increases and is > significant, demonstrating superiority of one treatment over the > other. > > > > The manuscript reviewers feel that the study should have balanced the > severity scores in both treatment arms instead of including severity > as a variable. I'd like to run jackknife or bootstrap estimations of > unadjusted RR by constraining each jackknife/bootstrap to select equal > numbers of patients receiving each treatment with each severity score. > The goal is to repeatedly select samples from the data set that > produce equal numbers of patients in each of the six groups (two > treatments, three severity classifications). Can someone comment on > the feasibility of doing this in the bootstrap/jack knife context? > Since this is not random sampling from the data set, how would this > procedure affect the interpretation of bootstrapped/jacknifed results? > If feasible and interpretable, can someone suggest some code that > would do this? > > I have a 700 subject data set of a clinical trial comparing two > treatments for outcome (hospital admission from an emergency room) for > a particular disease. I am also using a three point ordinal scale of > disease severity that strongly predicts hospital admission regardless > of treatment. Though the trial was designed to balance the two > treatment arms, calculating disease severity is too cumbersome to have > included it to balance each treatment arm with equal numbers of each > severity category in the emergency room. Thus there is an unequal > distribution of severity of cases in the two arms. When I calculate > the unadjusted risk ratio of admission for two treatments I obtain a > low, non-significant crude RR, similar to already published studies > that did not account for severity. When I model the treatments and > include the severity score, the adjusted RR increases and is > significant, demonstrating superiority of one treatment over the > other. > > The manuscript reviewers feel that the study should have balanced the > severity scores in both treatment arms instead of including severity > as a variable. I'd like to run jackknife or bootstrap estimations of > unadjusted RR by constraining each jackknife/bootstrap to select equal > numbers of patients receiving each treatment with each severity score. > The goal is to repeatedly select samples from the data set that > produce equal numbers of patients in each of the six groups (two > treatments, three severity classifications). Can someone comment on > the feasibility of doing this in the bootstrap/jack knife context? > Since this is not random sampling from the data set, how would this > procedure affect the interpretation of bootstrapped/jacknifed results? > If feasible and interpretable, can someone suggest some code that > would do this or suggest another way of achieving the same goals? > > Paul Walsh > > Bakersfield CA > * > * 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/ > > * > * 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/ > * * 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: Can I repeatedly sample with constraints from an unbalanced data set to balance it?***From:*"Paul Walsh" <yousentwhohome@gmail.com>

**st: R: Can I repeatedly sample with constraints from an unbalanced data set to balance it?***From:*"Carlo Lazzaro" <carlo.lazzaro@tin.it>

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