Sentence examples for multivariate prediction of from inspiring English sources

Strengths of our study include the large sample size, which allows precise estimation and multivariate prediction of patient preference.

Hierarchical linear regression models were employed for multivariate prediction of the Endorsement of Coercive Policies index, and the Intent to Discriminate against PLHA index.

Multivariate prediction of electronic questionnaire preference showed that lower age (p < 0.001), male gender (p < 0.001), higher completed level of schooling (p < 0.001) and higher BMI (p = 0.004) independently predicted preference of electronic questionnaires in THR patients (Table V).

In TKR patients, multivariate prediction of electronic questionnaire preference showed that lower age (p < 0.001) and higher completed level of schooling (p < 0.001) independently predicted preference of electronic questionnaires (Table V).

Apart from these practical constraints, many feel reluctant to interpret the implications of subgroup analyses let alone multivariate prediction of treatment effect, because over-accuracy and chance findings may occur.

For the multivariate prediction of postpartum depressive symptoms, relevant variables were entered step by step into a hierarchic regression equation (method enter), which was intended to account for the different contribution of distress and relation variables.

Furthermore, like other nearest neighbor techniques, k-NN allows for both univariate and multivariate predictions of continuous and categorical variables [25, 26].

Furthermore, like other nearest neighbor techniques, k-NN allows for both univariate and multivariate predictions of continuous and categorical variables [ 25, 26].

The aim of this systematic review of multivariate prediction models is to report their performance in new patients, and compare study methods against current recommendations.

Unfortunately, the human mind is not well suited for rapid and reproducible calculation of multivariate prediction models.

It is well known that variable selection can be a useful tool for optimising multivariate prediction models, since many of the detected variables (in this case, metabolites) carry no systematic information about the health status of the individual [ 28]; that is, they are the same in RA patients as in controls and only introduce noise in the model.