Sentence examples for binary outcome model from inspiring English sources

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.

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

The Area Under the ROC Curve (AUC) [ 10] is an important measure in checking classification performance of binary outcome models.

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.

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

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.

We applied six statistical models to binary outcomes, three models to nominal multinomial outcomes and one model to bivariate binary outcomes to estimate the ranking of key attributes of CRC screening tests using data from DCE survey conducted in Hamilton, Ontario, Canada in 2002.

Similar to the random-effects models used for analyzing binary outcomes, this model takes two levels of variance, between-cluster variance and within-cluster variance, into account for clustered or longitudinal nominal responses [ 41, 42].

We used linear mixed-effects models to assess longitudinal variation in fatigue scores and generalized estimating equations for binary outcomes to model predictors of fatigue remission among those fatigued at baseline.

Logistic regression was used for binary outcomes and model based P values, using likelihood ratio tests.