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Binary logistic regression model was estimated using the Entry method.
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Multivariate logistic regression analysis was then performed using the forced entry method.
Multiple linear regression analysis using the forced entry method was used to identify independent predictors of protocol deviations.
Linear regression analyses for both the daytime and nighttime ABP were performed using the forced entry method to determine the effect of histopathological lesions on the ABP.
Here again, hierarchical regression analyses were computed, but now using the forced entry method of variable inclusion, making the results in the five imputed data sets directly comparable.
All variables were entered using the forced entry method and significances of the variable blocks were assessed by likelihood ratio tests.
Logistic regression analysis was used to assess the relationship of the dependant variables of (1) presence of EM, (2) presence of BE, and (3) presence of BWT with explanatory (independent) variables, using the block entry method.
Table 6 describes the results of multivariate Cox regression analyses using the block entry method for global QoL after controlling for the effects of gender, tumor stage and prior treatment history.
These S1 2 families were recombined using the bulk entry method, whereby each selected progeny is crossed with each other selected progeny and each cross contributes equally to the next cycle of WQS.
Table 5 describes the results of multivariate Cox regression analyses using the block entry method for those QLQ-C30 function and symptom scales that were significant upon univariate analysis after controlling for the effects of gender, tumor stage and prior treatment history.
Data were entered into OpenClinica version beta 3.1.2 clinical trial software (Isovera Inc, Boston, MA, USA) for clinical data management, using the double-entry method by at least two independent researchers for completeness.
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