Sentence examples for binary outcome models were from inspiring English sources

Binary outcome models were examined for goodness of fit using the Hosmer-Lemeshow method and there was no evidence of a poor fit.

As described by Fitzmaurice, Laird, and Ware [ 18] and others, binary outcome models can be described equivalently in two ways.

WLSMV, a robust weighted least squares estimator, which is the default estimator for binary outcome models in MPlus, was used for all SEM models.

As demonstrated by MacKinnon et al., to obtain comparable estimates of the indirect effect in binary outcome models, the total effect must be appropriately scaled before the difference is taken.

The use of ROC curves is a common way for assessing binary outcome models [ 17].

The first part of the two-part model is a binary outcome model that describes the distinction between non-users (zero cost) and users of services (non-zero cost), while the second part is a linear regression that describes the distribution of log total health care cost for patients who used services.

Six binary logistic models were run, with AMO as the outcome.

For binary outcomes log-binomial regression models were fitted to calculate risk ratios (RR) and were estimated using generalised estimating equations (GEE) with an exchangeable working correlation structure.

Multivariable logistic regression was used to explore determinants of mobility using 'residential move' as the binary outcome; variables were entered into the model simultaneously.

Models for the six binary outcome measures were re-estimated on the remaining subset of variables.

The effect estimates for binary outcome variables were obtained from logistic regression models and are presented as odds ratios with 95% confidence intervals.