十分的欣赏兄台钻研，发现和分析问题的热情。

因为比较感兴趣，我对这个问题研究了约3个小时。我现在的想法是模型中的参数系数是针对所有的数据采用最大似然法获得的。

但计算oddsratio的时候针对每对两两比较，采取的仅仅是这2组的数据，如果没有记错的话，如果是连续型变量会自动取所有数据的均值，而分类变量的话就只会取比较的层。

所以应该是没有办法让P和oddsratio对应上的，oddsration有oddsratio的P值。

如果您在这个问题上有新的发现，请也告诉我，十分感谢。

已经找到答案，在class语句的选项中加上param=ref后就一致了。具体程序如下：

logistic回归class语句的param选项的说明如下：
PARAM=keyword specifies the parameterization method for the classification variable or variables. You can specify any of the keywords shown in the following table; the default is PARAM=EFFECT. Design matrix columns are created from CLASS variables according to the corresponding coding schemes:

Value of PARAM=	Coding
EFFECT	Effect coding
GLM	Less-than-full-rank reference cell coding (this keyword can be used only in a global option)
ORDINAL THERMOMETER	Cumulative parameterization for an ordinal CLASS variable
POLYNOMIAL POLY	Polynomial coding
REFERENCE REF	Reference cell coding
ORTHEFFECT	Orthogonalizes PARAM=EFFECT coding
ORTHORDINAL ORTHOTHERM	Orthogonalizes PARAM=ORDINAL coding
ORTHPOLY	Orthogonalizes PARAM=POLYNOMIAL coding
ORTHREF	Orthogonalizes PARAM=REFERENCE coding

All parameterizations are full rank, except for the GLM parameterization. The REF= option in the CLASS statement determines the reference level for EFFECT and REFERENCE coding and for their orthogonal parameterizations. It also indirectly determines the reference level for a singular GLM parameterization through the order of levels.
If PARAM=ORTHPOLY or PARAM=POLY and the classification variable is numeric, then the ORDER= option in the CLASS statement is ignored, and the internal unformatted values are used. See the section Other Parameterizations of Chapter 19: Shared Concepts and Topics, for further details.

十分感谢你的新信息！！！

虽然问题已经解决了，还是附上比较直观的理解方式如下。希望对朋友们有所帮助。
http://www.ats.ucla.edu/stat/sas/faq/proc_logistic_coding.htm