Sentence examples for logistic formulations from inspiring English sources

The conditional Poisson model, like the unconditional Poisson and conditional logistic formulations, can incorporate potentially confounding covariates not homogeneous within strata for example temperature (if air pollution is the focus).

Xu [ 12] presents an approach to fit conditional Poisson models in SAS, but as this is effectively by re-formulating as a conditional logistic model we class this a conditional logistic formulation (discussed below).

When the logistic formulation is adopted, the interpretation of the risk parameters θ k changes.

The conditional logistic formulation does not easily allow any of these extensions apart from the incorporation of covariates.

Programming was simpler for the Poisson models than for the conditional logistic formulation because no data expansion was necessary (Additional file 1).

Thus, the logistic formulation well captures the density-dependent cell growth in cell culture as we previously showed in Fukuhara et al. (2013).

The former method combines a logistic formulation for the probability of occurrence of an event with a proportional hazards specification for the time of occurrence of the event [ 7- 13], so that the effects of the treatment and other factors can be interpreted separately into those on the proportion of cured patients and the failure time of uncured patients.

The conditional Poisson model was faster than the unconditional Poisson or conditional logistic formulation, though times for the latter were not prohibitively long unless the numbers of strata were very large indeed, or fitting the model is embedded in an iterative algorithm, for example in a Bayesian model fit by MCMC [ 16, 17].

In this paper, to analyse data including such a long-term censored relapse-free time, we discuss a semi-parametric cure regression (Cox cure regression), which combines a logistic formulation for the probability of occurrence of an event with a Cox proportional hazards specification for the time of occurrence of the event.

The log-logistic formulation was found to have the best fit with the observed data, using the Akaike information criterion (AIC) indicator to compare models.

The log-logistic formulation is as follows: fleft {t}_{ij}right)=frac{1}{1+ exp left(a+b ln {t}_{ij}right)} (6).