Sentence examples for logistic regression one from inspiring English sources

The power for a test of the null hypothesis (logistic regression, one continuous predictor) was calculated as follows.

In the Web Appendix, we describe two simulation studies of logistic regression, one with an imputed outcome and one with an imputed covariate.

Importantly, with logistic regression, one can increase the number of comparisons for each gene model by using sliding and revolving windows of early death and long survival cases, providing additional evidence in support of the prognostic gene list.

However, in this context it should be noted that standard logistic regression (one patient – one record) does not model time to event and might not appropriately include time-dependent risk factors.

In this regression method the constructed classes play the role of categories of dependent variable and similar as ordinary logistic regression one of the categories (classes) is chosen as the reference category and the covariates coefficients can be interpreted as odds or log odds ratios of being an individual in a specific category instead of reference class.

Three case-case comparisons were initially performed using standard logistic regression: one comparing confirmed positive RHT herds to unconfirmed positive RHT herds, one comparing confirmed positive RHT herds to unconfirmed inconclusive RHT herds, and one comparing unconfirmed positive RHT herds to unconfirmed inconclusive RHT herds.

To compare the discriminability of the marker CRP alone to the discriminability of the marker CRP in combination with the conventionally used marker LDH, we conducted two logistic regressions: one on CRP alone and one on CRP in combination with LDH.

To investigate MBL and ficolin-2 as potential risk factors for SSc, matched univariate analysis was performed by running conditional logistic regression on one variable at a time with SSc as the dependent variable.

Table  3 shows results of the ordinal logistic regressions, one regression for each of the five outcome variables described above.

These three separate outcomes will be analyzed together by multinomial logistic regression rather than by separate binomial logistic regressions (one for each outcome).

Provided that at least a 20% level of significance was achieved, these variables were considered for one of two multiple logistic regressions: one concerning risk factors and another concerning protective factors.