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For part one of the applied models a logistic regression analysis was chosen to predict whether or not patients would utilize resources related to the respective costs figure.
The assessment of each potential design was driven by the need to reduce the uncertainty of the common-link function between the PK and PD parts of the three considered models: a logistic function of CRP levels as a function of OKZ concentration.
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In a first approach, the membrane integrity and esterase activity was modeled with the Gompertz model, a logistic model or a log linear model with or without tailing or shoulder.
The accuracy of the approximations is assessed by simulation for three nonlinear regression models with normal errors, a two-population normal model, a logistic model and a Poisson model.
We also fit a type of generalized linear model, a logistic regression classifier, to the DRB1*0101 training data using only the five physical energy components.
These outcomes were evaluated with three analytical models: an unconditional (standard) logistic regression model, a logistic regression model incorporating the results of propensity score matching and a conditional logistic regression model.
The second model, a logistic regression model, aimed to examine the determinants of severe decline in patients with and without CKD.
The probability of a person for a positive response of an item is in the Rasch model a logistic function of the item difficulty and person ability [ 27].
As a statistical model a logistic regression on individual (not marginal) changes is used to interpret the individual odds ratios (likelihood-ratio test).
The dropout probability was modeled using a logistic model.
Outcome values were simulated from a linear model and a logistic model, each derived from the completed UK700 dataset.
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