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Table 3 Fit summary model statistic result Source Std Dev.

*It was define to model statistic for each step.

For each we provide a Q-model statistic, denoted as QM df).

A model comparison test statistic, T, is created from the best-fit statistics of each fit; as with all statistics, it is sampled from a probability distribution p(T).

A score statistic was used to enter factors into a model, and a Wald statistic was used to withdraw factors from the model.

As a consequence, we did not base our decision for or against a model solely on this statistic.

A model with a c statistic of 0.5 has no discriminative power at all, for example a coin flip.

The three models for long-term care each had a lower model fit statistic, although still highly significant.

To evaluate the power of the models to discriminate events from nonevents we calculated the area under the receiver operating characteristic (ROC) curve for each of the variables as well as in a multivariate model (C statistic).

A model with a c-statistic of 0.5 would reflect a completely random prediction model, while a model that discriminates perfectly between patients with and without an event would have a c-statistic of 1.0.

For example, in a model with a c-statistic of 0.60 applied to 100 random case/non-case pairs, we would expect an accurate discrimination between the cases and non-cases in 60 of the 100 pairs, or 60%.