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From |
Joseph Coveney <jcoveney@bigplanet.com> |

To |
Statalist <statalist@hsphsun2.harvard.edu> |

Subject |
st: Re: xtlogit and logistic-cluster (REVISED) |

Date |
Wed, 04 Aug 2004 10:19:53 +0900 |

Ricardo Ovaldia wrote: Fri, 30 Jul 2004 My understanding is that the three commands; -clogit-, -xtlogit- and –logistic, cluster- can all be use to performed logistic regression when observations (outcomes) are sampled within groups (i.e. correlated data where the independence assumption is violated). After reading the manual entries for these commands and examining Hosmer and Lemeshow’s book I have been unable to determine under what conditions these model are appropriate, and in frustration I a writing to ask if anyone knows of a document, website, etc that compares and contrasts these methods? Tue, 3 Aug 2004 I am comparing the hospital referral rate of CHD patients by 50 physicians. I would like to model the rate using both patient and physician characteristics. Because patient referrals are most likely not independent within physician, I though to use conditional logistic regression grouping on physician ID to model the outcome. The problem with this approach is that because physician characteristics do not vary within physician they are dropped. I decided, therefore, to use random effects logistic regression, -xtlogit- instead. My concern is that I am not sure that I can correctly justify this approach solely on the above argument. Does anyone have any thoughts or literature I can read regarding this justification, or am I way off track? -------------------------------------------------------------------------------- -logit, cluster()- produces the same results as -xtgee, family(binomial) link(logit) corr(independent) robust- (this came up on the list last month in the context of -mlogit, cluster()-, so I would recommend avoiding that approach in circumstances in which population-averaged GEE would not be ideal. There are those who would say that GEE is never ideal, but even among its adherents, most would caution that, with only 50 physicians, GEE would be a little dicey. -xtlogit, fe- would help see the influence of patient characteristics upon a physician's inclination to refer, while, in a sense, controlling for physician characteristics. (Where the predictor variables for patient characteristics do not vary within a physician, the entire physician's caseload would be dropped.) As you mention, because physician's characteristics do not vary within a physician, -xtlogit, fe- doesn't seem to be the way to go to explore both patient and physician characteristics together. -xtlogit, re- would seem to be the remaining alternative available in Stata, unless I'm overlooking something. Cautions would be similar to the case with GEE. The number of physicians is limited. If there is a substantial correlation between the fixed effects (physician covariates) and the random effect, then the parameters are liable not to be consistently estimated. Joseph Coveney -------------------------------------------------------------------------------- clear set more off local seed = date("2004-08-05", "ymd") set seed `seed' macro drop seed set obs 400 generate int pid = _n generate float mu = uniform() generate byte den = 1 forvalues i = 1/6 { rndbinx mu den rename bnlx dep`i' } compress replace mu = mu + invnorm(uniform()) drop den reshape long dep, i(pid) j(tim) xi: xtgee dep i.tim mu, i(pid) family(binomial) link(logit) /// corr(independent) robust nolog xi: logit dep i.tim mu, cluster(pid) nolog exit * * 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/

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