Sentence examples for multivariable prediction research from inspiring English sources

As pointed out by Horton et al (2010), all missing data methods rely on inherently untestable assumptions, and we cannot draw general conclusions on the most appropriate method of handling missing data in multivariable prediction research under all circumstances.

First prognostic tool in low back pain to follow, a priori, the Prognosis Research Strategy (PROGRESS) framework, and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis TRIPODD) reporting guidelines for prognostic research.

Studies developing or validating a multivariable prediction model share specific challenges for researchers.

Studies developing or validating a multivariable prediction model share specific challenges for researchers (Steyerberg et al, 2013).

Using the results as a platform, we aim to delineate the distinction between etiologic and prediction research, issues of confounding accompanying these research aims and how a multivariable model deals with confounding.

Although there is abundant literature to help researchers perform this type of research [ 1- 5], there is still no widely agreed approach to building a multivariable prediction model [ 6].

The multivariable prediction equations derived in the present study can be used to predict the total anterior facial height and annual growth rate of the mandible.

The results of this validation study were reported in adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) guideline (see appendix table C). 29 30 No patients were involved in setting the research question or the outcome measures; nor were they involved in recruitment or in the design and implementation of the study.

transparent reporting of a multivariable prediction model for individual prognosis or diagnosis.

Table 4 illustrates our multivariable prediction model and shows the covariates selected by the multivariable LASSO logistic regression together with the estimated ORs.

Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century.