Sentence examples for model for prevalence from inspiring English sources

[ 34] (Title) "Prediction model for prevalence and incidence of advanced age-related macular degeneration based on genetic, demographic, and environmental variables".

A model for prevalence P is (2) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$\text{logitt}~ P = \beta_{0}+ \beta_{A}(A) + \beta_{P}(P) +\beta_{C}(C).

The underlying mathematical models for prevalence trends included in EPP have evolved substantially as needs have changed.

Some health outcomes, such as prevalence and incident cases of lateral epicondylalgia, are binary variables and will be analyzed using logistic regression models for prevalence and proportional hazards regression models for incidence.

Adjusting for census block-level income in the Poisson regression models for prevalence and incidence (model 2 in Tables 4 and 5) imparted minimal changes to the age- and sex-adjusted point estimates (model 1).

Table 3 Low prevalence reaction time regression results   t p value β High prevalence performance 7.68 <0.001 0.53 K 1.89 0.06 0.13 Attentional control 2.01 0.04 0.14 Extraversion –2.02 0.04 –0.14 t, p, and β values for each predictor in the final regression model for low prevalence target-absent reaction time.

Table 2 Low prevalence accuracy regression results   t p value β High prevalence performance 9.99 <0.001 0.60 K 2.27 0.03 0.14 Vigilance 2.05 0.04 0.13 Attentional control 2.59 0.03 0.14 Extraversion –2.63 0.009 –0.15 t, p, and β values for each predictor in the final regression model for low prevalence accuracy.

We call these the S+T formulations of the model for true prevalence, scale parameter and shape parameter.

Figure  2 shows the estimated odds ratio surface according to generalized additive model for the prevalence of total anti-HAV antibodies in Assis Brasil for 2011.

The model for diabetes prevalence used in this study was created using data from the NHANES III (1988 1994), and then fitted to data from the NHANES 1999 2002 as a validity check of the accuracy of the model's projections.

Parameter estimates for the final, most parsimonious models of prevalence for FMDV and BTV are given in Table 5.