First we utilize the logistic regression model to distinguish the most important symptoms of influenza infection by fitting a logistic regression model to the binary influenza infection outcome in the sample, using binary indicators of the influenza-like symptoms as predictors.
Data from four distinct sources were utilized in the logistic regression models (Table 1, Figure 1).
To mimic the sequence of events as would occur in practice, the logistic regression models utilized in stochastic curtailment were first fitted to the dataset from the COMM's initial validation study.
Furthermore, the definition for normal weight (BMI is greater than or equal to 20th- but less than 85th percentile) was more exclusive than our definition and the logistic regression model utilized this category for comparison purposes.
Tables 5 and 6 depict the results of the logistic regression analyses, which were utilized to predict the quartile exhibiting most improvement or beneficial change.
The logistic regression was a stepwise approach.
We evaluated the association of each polymorphism with SLE, adjusting for the admixture proportion utilizing logistic regression under the multiplicative genetic model for minor alleles by combined analysis.
Multivariable analysis utilizing stepwise logistic regression was used to identify independent predictors of death.
The MGA is based on the algorithm of the MG and utilizes logistic regression models of the GC content and the di-codon frequencies (di-codon models).
