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Heterogeneity among ER subtypes was evaluated in a case-only analysis, by fitting binary logistic regression models, treating ER status as a dependent variable, with coffee consumption included as a covariate.
Associations between coffee consumption and hormone receptor status were evaluated in a case-only analysis, by fitting binary logistic regression models (for ER and PR status), treating ER or PR status as dependent variables, with coffee consumption included as a covariate.
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One simple method to deal with multiple responses is to fit binary logistic fitting for each pair of these multiple categories.
This study fitted binary logistic regressions to test the established hypotheses.
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.
The summed responses were plotted against the needle-distances resulting in a psychometric function, which was fitted by a binary logistic regression (SPSS; SPSS Inc, Chicago, IL, USA).
We used a retrospective, explanatory, nonexperimental design, and we fit a binary logistic model to the data.
We fitted the binary logistic model for the two categories outcome (anaemia (Hb < 11), and no anaemia (Hb ≥ 11)).
For statistical analysis, we fit a binary logistic regression model to estimate the effect of brain function monitoring on the probability of GOS scores equal to 1.
The summed responses of every session were plotted against the needle distances resulting in a psychometric function plot, which was fitted using binary logistic regression.
We also fit a binary logistic regression model to estimate the effect of monitors on the probability of dying (GOS score = 1) [ 13].
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