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Tests to compare nonnested models are available, but not by using CONTRAST statements as discussed above. See for discussion and examples of using the Vuong and Clarke tests to compare nonnested models. For a more detailed definition of nested and nonnested models, see the Clarke (2001) reference cited in the sample program.

I have chosen two heroes to compare and contrast based on cultural aspect and what they act.

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The exponentiated contrast estimate, 0.83, is not really an odds ratio. After exponentiating, the denominator is not just a simple odds, but rather a geometric mean of the treatment odds. The result, while not strictly an odds ratio, is useful as a comparison of the odds of treatment A to the "average" odds of the treatments. However, this is something that cannot be estimated with the ODDSRATIO statement which only compares odds of levels of a specified variable. The LSMEANS, LSMESTIMATE, and SLICE statements cannot be used with effects coding.

We saw that the first component of the hypothesis, log(OddsOA) = μ + d + t1 + g1. You use model 3e to expand the average treatment effect:


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The nested term uses the same degrees of freedom as the treatment and interaction terms in the previous model. The design variables that are generated for the nested term are the same as those generated by the interaction term previously. But the nested term makes it more obvious that you are contrasting levels of treatment within each level of diagnosis. See the "Parameterization of PROC GLM Models" section in the for some important details on how the design variables are created. As before, it is vital to know the order of the design variables that are created for an effect so that you properly order the contrast coefficients in the CONTRAST statement.

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Notice that this simple contrast is exactly the same contrast that is estimated for a main effect parameter — a comparison of the level's effect versus the effect of the last (reference) level. Therefore, this contrast is also estimated by the parameter for treatment A within the complicated diagnosis in the nested effect. The following statements fit the nested model and compute the contrast. The Analysis of Maximum Likelihood Estimates table confirms the ordering of design variables in model 3d. The first three parameters of the nested effect are the effects of treatments within the complicated diagnosis. The second three parameters are the effects of the treatments within the uncomplicated diagnosis. The EXPB option adds a column in the parameter estimates table that contains exponentiated values of the corresponding parameter estimates.

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As in Example 1, you can also use the LSMEANS, LSMESTIMATE, and SLICE statements in PROC LOGISTIC, PROC GENMOD, and PROC GLIMMIX when dummy coding (PARAM=GLM) is used. The ILINK option in the LSMEANS statement provides estimates of the probabilities of cure for each combination of treatment and diagnosis. The DIFF option estimates and tests each pairwise difference of log odds. The EXP option exponentiates each difference providing odds ratio estimates for each pair. The LSMESTIMATE statement allows you to request specific comparisons. Since treatment A and treatment C are the first and third in the LSMEANS list, the contrast in the LSMESTIMATE statement estimates and tests their difference. The EXP option provides the odds ratio estimate by exponentiating the difference. Similarly, the SLICEBY, DIFF, and EXP options in the SLICE statement estimate and test differences and odds ratios in the complicated diagnosis.

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STRUCTURALISM: The idea in sociology, anthropology, literary theory, or linguistics that the best way to understand a cultural artifact (like family units, religious rites, or human language) is not to define each component individually, as its own unique element, but rather to define each component by its relationship to other parts of the same structure. To give a rough example, consider a concept like "father" in American society. If we were attempting to define this concept and how the role functions in American society or in a traditional family from the 1950s, a nonstructuralist might define a father as "a male adult figure who provides income for the family and who serves as an authority figure or protector." Such a definition seeks to define the role based on what it does or what it is, per se. In contrast, a structuralist might instead seek to define a "father" by showing the relationship that figure would have in the larger structure of the family, i.e., a "father corresponds to a mother, but is of opposite gender, and the two together may have children, forming a larger structure called a family, and within that family the father traditionally protects the children and labors outside the household while the mother nutures them within the home." For the structuralist, it makes no sense to define a father without considering the other parts of the family structure and explaining the father's role in relationship to those other parts. The role of father cannot exist if the roles of mother and children do not exist. They are interdependent in ontology.