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

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

Subject |
Re: st: Re: Analysis of binary cluster data with missing values |

Date |
Fri, 14 Nov 2003 01:22:33 +0900 |

Louise Linsell wrote (excerpted): -------------------------------------------------------------------------------- I have a dataset that looks something like this - each person has observations on 2 hips and 2 knees and I wish to calcuate the odds ratio of hips to knees, taking account of the clustering within person using robust standard errors. id hip knee row 1 1 0 1 1 1 0 2 (1=replaced, 2=not replaced) 2 . 1 3 2 0 1 4 3 1 0 5 3 0 . 6 4 1 0 7 4 . . 8 I have tried all of the following commands logistic hip knee, cluster(id) xtlogit hip knee, i(id) or svymean hip, by(knee) but all of them drop any record (line) with a missing value (.), so in the above dataset, rows 3, 6 and 8 would be dropped, despite there being a non-missing observation in the other group that should be included in the analysis. . . . Any ideas or suggestions on how to calculate the correct odds ratio and robust s.e. would be much appreciated. -------------------------------------------------------------------------------- Louise mentions in a later posting that there is no left- or right-side pairing of the joints in a record in the dataset, so the joints are only clustered within patient and can really be represented as Joint A (hips) and Joint B (knees) for a patient. To accommodate this, Louise might wish to consider reshaping long, calling the hip "joint 0" and the knee "joint 1" (or vice versa) to obtain the odds ratio for replacement (odds of replacement of Joint 1 to those of Joint 0) using random-effects logistic regression. This method will make full use of available data. The do-file below illustrates this with an artificial dataset designed to mimic Louise's. Much of the do-file is just to create the fictitious dataset for illustration. Joseph Coveney -------------------------------------------------------------------------------- clear set obs 2000 set seed 20031113 generate int pid = mod(_n, 2) replace pid = sum(pid) generate float order = uniform() sort order // No particular correlation generate byte hip = _n <= 400 replace order = uniform() sort order drop order generate knee = _n <= 600 replace hip = . if uniform() > 0.95 // 5% MCAR for both joints replace knee = . if uniform() > 0.95 // Louise can begin here sort pid by pid: generate byte side = _n rename hip replaced0 rename knee replaced1 reshape long replaced, i(pid side) j(joint) drop side xtlogit replaced joint, i(pid) re or 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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