Sentence examples for a logistic regression framework from inspiring English sources

Our analysis is formulated in a logistic regression framework, modified to allow for non-linear input output relationships, auxiliary variables, and small sample sizes.

Using the USDA/NASS Cropland Data Layer, this study identifies these characteristics by employing Multivariable Fractional Polynomials within a logistic regression framework.

We compared used (bobcat locations) to available (random) locations in a logistic regression framework where used and random locations were represented as a binary response (1 = bobcat location; 0 = random location).

Data were analyzed using a logistic regression framework where nest fate (survived or failed) on each day was analyzed as a binary response variable (1 = survived; 0 = failed); modeling daily nest fate as a binary response was the basis for estimating the probability of daily nest survival (i.e., DSR of nests).

We used a generalized linear model (GLM) implemented in R (R Core Team 2013) and a use vs. availability resource selection approach to evaluate non-random nest site selection by comparing used (actual nest sites) to available (potential nest sites) in a logistic regression framework where nests were represented as a binary response (1 = actual nest site; 0 = potential nest site).

A logistic regression framework was used to estimate the probability (absolute risk) that a subject reported yes for each dichotomous outcome, with data pooled across the three study sites.

Differences between meal types in the proportion of subjects who had a hypoglycemic event were examined using a logistic regression model within a generalized estimating equation framework.

The relationship between PCR parasite density and detectability by microscopy was modelled using a logistic regression model with a study-level random effect fitted using a Bayesian framework and Stan61.

This analysis is done by fitting a logistic regression model.

Medication discontinuation was estimated using a logistic regression model.

In this model, each participant forms their own stratum analogous to a conditional logistic regression framework for binary outcomes in a case-crossover study.