To select only the undifferentiated ES cells from each well, the fluorescence values of antibodies gathered from the RW4 cells and STO cells in all the control wells were used to construct a logistic regression model, which gives the probability of a cell being an ES cell.
We constructed a logistic regression model for each dependent variable for each of the six sites.
We constructed a logistic regression model, adjusted for age and sex, to test the association between B12 and albuminuria.
To perform a mediating effects analysis, we constructed a logistic regression base model containing group assignment and APACHE IV predicted hospital mortality.
We then constructed a logistic regression model that assessed the effect of age, system, location, and farm type on having a positive influenza result.
Subsequently, we used the risk factors with p ≤ 0.05 (Table 1), which were independently associated with the development of a Gram-negative bacteremia, to construct a logistic regression model to analyze potential interactions.
We also constructed a logistic regression model to evaluate the contribution of traditional risk factors and RA-related risk factors to the presence of severe subclinical atherosclerosis detected by US.
To identify risk factors for MDR TB, we constructed a logistic regression model that included age, sex, sputum smear test result, previous TB treatment, known household contact with MDR TB patients, and HIV infection status as potential predictors.
Categorical data were compared by the chi-square test or the fisher exact test, when expected cell sizes (n) were smaller than 5. Two models were constructed in a logistic regression analysis using clusters and anti-TB drug resistance as the respective outcome variables.
This is necessary for constructing a distributed logistic regression model that can provide useful information in biomedical research.
