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The discriminatory performance of the composite logistic model demonstrated a good c-index of 0.701 955% CI, 0.624 to 0.779) for agitation.

Reliability was measured by estimating the intracluster correlation coefficient (ICC) for each STOPP indicator and the composite indicator in a two level random intercept logistic model with no explanatory variables ("empty model").

two-parameter logistic model.

For α=β=1, we obtain the logistic model.

The calibrated logistic model only minimally improved the prediction accuracy of the AP model.

Four single-model learners (CART, k-nearest neighbor, multinomial logistic regression, and logistic model tree) and five ensemble-model learners (CART with bagging, k-nearest neighbor with bagging, multinomial logistic regression with bagging, logistic model trees with bagging, and Random Forest) were compared.

The second modeling structure is the sequential binary logistic model.

We test our hypotheses using a clustered robust standard errors logistic model.

Where M is the type of IRT model which could be one parameter logistic model (1PL), two parameter logistic model (2PL), or three parameter logistic model (3PL).

The logistic model gave a correct prediction in 73%to83%3%.

Logistic model also tested for the non linear regression approach.