Fixing the nonconvergence bug in logistic regression with SPLUS and SAS

被引：59

作者：

Heinze, G ^{[1
]}

Ploner, M ^{[1
]}

机构：

[1] Univ Vienna, Dept Med Comp Sci, A-1090 Vienna, Austria

来源：

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE | 2003年 / 71卷 / 02期

关键词：

monotone likelihood; nonexistence of parameter estimates; penalized likelihood; separation;

D O I：

10.1016/S0169-2607(02)00088-3

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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). (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

引用

页码：181 / 187

页数：7