並列摘要
Stratification of data may be formed in design stage or post hoc stage. In model review, conditional logistic models and stratified Cox models are built for stratified data. In this study, stratified data in factor analysis is investigated; the stratum mean-corrected method is proposed. And, an alternative normal-subgroup mean-corrected method is used for extracted factors result connecting with binary outcome. Two corrected methods with uncorrected method are compared from the centered value of data deviated from. The stratum mean-corrected method completely removes the effect in the analyzed data in an additive form. In an example of fetal data, in which the extracted factors were combined with binary outcome variables for model tree built, the classified accuracy rate was 95% for normal subgroup mean-corrected data comparing 92% for uncorrected data when based on the same number of cutting points in their trees. Two corrected methods provide two adjusted algorithms separately for binary outcome involved or not. Their contribution is worth noting when the analyzed data are varied with an extra variable and the factor analysis is applied.
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