Logistic regression
Logistic regression
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DOI：
10.1161/circulationaha.106.682658
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发表时间：
2008-05-06
期刊：
影响因子：
37.8
通讯作者：
LaValley, Michael P.
中科院分区：
文献类型：
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作者：
LaValley, Michael P.
当需要调整值时，可以在上述回归方程的右侧添加更多的预测因子，以及相应的回归系数（）。在这种情况下，X的比值比值将根据模型中的其他预测因子进行调整。上面的等式100（赔率比1）可以解释为在保持所有其他预测因子不变的情况下，X增加1个单位所对应的赔率变化百分比。选择适当的预测因子以减少混杂和提高估计精度的方法与逻辑回归和线性回归相似；可以在许多统计教科书中找到指导方针。1,2,12与线性回归不同，逻辑回归的估计没有公式。找到最佳估计需要反复改进近似估计，直到达到稳定性。这在计算机上很容易完成，并且有许多执行逻辑回归的统计软件包，但这使得逻辑回归不太容易理解，并且对许多研究人员来说更像是一种“黑箱”方法。
When adjusted values are needed, more predictors can be added to the right side of the regression equation above, along with corresponding regression coefficients (). In this case, the odds ratio value for X would be adjusted for the other predictors in the model. The equation above, 100 (odds ratio1), would then be interpreted as the percent change in the odds corresponding to a 1-unit increase in X while holding all other predictors fixed. The selection of appropriate predictors to reduce confounding and to improve the precision of estimates is done similarly for logistic regression and for linear regression; guidelines can be found in many statistical textbooks. 1, 2, 12Unlike linear regression, there is no formula for the estimates of for logistic regression. Finding the best estimates requires repeatedly improving approximate estimates until stability is reached. This is done easily on a computer, and there are many statistical software packages that perform logistic regression, but it makes logistic regression less understandable and more of a “black box” approach for many researchers.