Sentence examples for multivariate risk prediction from inspiring English sources

Multivariate risk prediction algorithms such as those developed within FHS allow clinicians to predict short- and long-term CVD risk for patients to help guide medical decision-making.

However, even when a treatment has a consistent relative risk reduction across risk levels, as we found for the Diabetes Prevention Program's lifestyle intervention, the technique used in this study allows clinicians to recognize large variation in a treatment's absolute risk reduction between patients on the basis of multivariate risk prediction.

However, multivariate Cox model also showed that risk prediction based on the 22 miRNA and 5 miRNA prognostic profiles retained independent prognostic significance for recurrence (HR = 2.27, P = 0.115 and HR = 2.40, P = 0.050, respectively).

A multivariate Cox model showed that both risk prediction with the 5 miRNA and the 22 miRNA profile and chemotherapy response retained their independent significance (22 miRNA: HR 2.90, P = 0.036; chemotherapy response: HR 3.82, P = 0.005 and 5 miRNA: HR 2.67, P = 0.026, chemotherapy response: HR 3.70, P = 0.006).

This paper proposes to model stock price volatility and variations in innovation effort using a Multivariate GARCH structure designed to extract information for risk prediction.

Risk prediction was assessed by univariate and multivariate logistic regression analysis.

Statistical techniques such as univariate and multivariate logistic regression analyses have been successfully applied to risk prediction in clinical medicine.

Multivariate logistic regression was used on these data to develop a risk prediction model for mortality.

Recently, Nagata et al reported the superior performance of 3D compared with 2D global longitudinal strain for risk prediction in such patients, also when adjusting for LV mass in multivariate analysis.

Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction.

A multivariate model, such as the one used for calculating deviances in our study, can also be used for risk prediction.