Abstract：
:When analyzing clinical data with binary outcomes, the parameter estimates and consequently the odds ratio estimates of a logistic model sometimes do not converge to finite values. This phenomenon is due to special conditions in a data set and known as 'separation'. Statistical software packages for logistic regression using the maximum likelihood method cannot appropriately deal with this problem. A new procedure to solve the problem has been proposed by Heinze and Schemper (Stat. Med. 21 (2002) pp. 2409-3419). It has been shown that unlike the standard maximum likelihood method, this method always leads to finite parameter estimates. We developed a SAS macro and an SPLUS library to make this method available from within one of these widely used statistical software packages. Our programs are also capable of performing interval estimation based on profile penalized log likelihood (PPL) and of plotting the PPL function as was suggested by Heinze and Schemper (Stat. Med. 21 (2002) pp. 2409-3419).
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Heinze G,Ploner Mdoi
10.1016/s0169-2607(02)00088-3subject
Has Abstractpub_date
2003-06-01 00:00:00pages
181-7issue
2eissn
0169-2607issn
1872-7565pii
S0169260702000883journal_volume
71pub_type
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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