In order to define the single most relevant parameter for the prediction of outcome, multiple logistic regression analysis using all potential predictor variables in forward likelihood based inclusion was performed.
During each step, 50 matches were randomly selected and reviewed to ensure that the matches were accurate, using all the potential identifying variables: first name, last name, middle name, date of birth, street address, ZIP code, city, state, phone number, sex, street address, name of the provider or physician ordering test, and reporting laboratory identification number.
To be certain that Kuskall-Wallis was the right test to use, all potential continuous independent variables were subjected to the Skewness and Kurtosis test to assess the normality of the variables.
AMOS7.0 is a software package that uses the potential variables based on the variance matrix structure to evaluate the structure model.
Using this initial model as a starting point, the final predictor variables were determined by utilizing a manual stepwise procedure (with p <.05 as the criterion for variables to either enter or stay in the model), using all of the potential predictor variables (including variables not in the initial model).
This strategy was further used to select potential variables contributing to the provided classification.
The results from the descriptive epidemiology of the present study and literature were used to identify potential variables to be included into the model.
Some modifications in assay conditions (Buffered Method) were used to control potential variables across the experiment (including effects of brain pH and other brain components on the reaction).
Classification trees were examined using three sets of potential variables: (1) the four identified factors; (2) adding presence of RF/ACPA; and (3) subsequently adding MHAQ.
First, univariate logistic regression analyses were used to test which potential variables were associated with suicidality among patients with migraine.
