Sentence examples for model prevalence from inspiring English sources

In the multivariate analysis, variables with P-values <0.25 were initially introduced into the model (prevalence of anti-PfMSP-1 antibodies, gender, age, haemoglobin concentration at day 0, multiplicity of infection, presence of Pfmdr-1 codon mutation at position 86 in isolates from day 0 and day of recurrence and at position 184 in isolates from day of recurrence).

When potential confounding variables were added to the model, prevalence ratios were substantially reduced and no longer significant, except for the association for 4-y-olds in the 1993 cohort, in which the adjusted prevalence ratio for boys was 2.03.

Assigning non-zero weight to distributions which have support outside of the data quartiles gives rise to an overall model prevalence distribution with a significant tail beyond the quartiles, as seen in Figure 1a.

Based on a linear regression model, prevalence of 46% using POC-CCA corresponded to a prevalence of 10% using Kato-Katz, and a prevalence of 72% by POC-CCA corresponded to a prevalence of 50% by Kato-Katz testing.

Given the diversity of the South African population, the use of a single estimate from a non-representative population sub-group to model prevalence is unlikely to provide reliable estimates.

The final fit of seroprevalence against time was significant (OR 1.24, 95% CI 1.221 1.264, p<0.001), and the linearized difference in model prevalence (and binomial confidence limits) over the 17-year study equated to a mean prevalence increase of –ln(1 – 0.321)/16 = 2.4%95%CI1.61%.61%–3.51%) per year.

This information is needed to decide on the type of BIA model (prevalence-based BIA model, incidence-based BIA model or a BIA model based on both prevalence and incidence) and its characteristics (need of health states, pathways, follow-up period) and effectiveness measure(s).

However, this "problem" with using the prevalence ratio is more imaginary than real because standard methods can be used to model prevalences, just as they can be used to model risks (both prevalence and risk are expressed as a proportion; Tables 1 and 2).

Modelling prevalence assuming an identical host response in iWT animals as in iCT animals does not fit the observed data well (Figure 1).

Our study followed their recommendation and performed both regression models; prevalence and severity of OHIP-14.

These differences are accounted by modelling prevalence as a farm specific parameter.