Sentence examples for multivariate risk model from inspiring English sources
In this paper we assume a multivariate risk model has been developed for a portfolio and its capital derived as a homogeneous risk measure.
These at-risk populations reflect key individual risk factors (age, race, serum PSA [free/total]; serum IGF-1/IGF binding protein (IGFBP 3; 1, 25(OH)2 D3; family history of PCa; carriers of PCa susceptibility genes [ELAC2, CYP3A4, SRD5A2, etc.]; and histology such as atypia and HGPIN) that could be combined into a multivariate risk model for PCa.
Expected results could be the establishment of a multivariate risk model based on traditional clinical factors and assessment of cfDNA signatures as well as the development of companion diagnostics for targeted therapies.
Here we present a subgroup analysis of NHL patients from the INC-EU prospective study with the aim of establishing a multivariate risk model of FN occurrence in the first cycle of chemotherapy.
In summary, this study describes a prospective multivariate risk model that was able to identify clinically relevant factors that were predictive or protective of cycle 1 FN and correctly identify a high proportion of patients at risk of first cycle FN.
Further work is needed to generate multivariate risk models that can reliably predict lifetime risk for CHD.
Multivariate risk models require careful attention to accurate measurements, a priori selection of the primary outcomes and risk factors of interest, specification of data collection including any missing values, and assessment of collinearity and any confounding or effect modification.
They also reported results of multivariate risk modelling involving a range of risk factors for lung cancer.
Computation of diabetes risk based on multivariate risk models is useful in the context of targeting prevention interventions to high-risk groups.
Furthermore, prior case control studies have typically matched on race (precluding analysis of race in multivariate risk models) and have not stratified their analyses of BMI and esophageal carcinoma risk by race.
General strengths of our study include the population-based, prospective design, the ability to control for multiple potential confounding factors in multivariate risk models and the molecular pathological epidemiology approach linking life style exposures to different tumour markers [ 14].
