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Multivariate analysis was performed using a hierarchical model of stepwise logistic regression that included possible explanatory variables, calculating the adjusted risk of PMPS from the odds ratio (OR) and the 95% CI at a significance level of 5%.
Selected variables were further introduced in multivariable analyses using a hierarchical model with two random effects.
Gene and breastfeeding main effects were assessed using a hierarchical model.
This is done using a hierarchical model, i.e. by assuming that the frailties represent random effects drawn from some distribution.
We combined the estimated curves across cities using a hierarchical model that allows for heterogeneity.
Using a hierarchical model, we treat a group of time-series data coming from single cells in a common framework.
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We used a hierarchical model for a single proportion to estimate the proportion of indeterminate index test results.
The more realistic simulated data set used a hierarchical model to capture violation of the "one season fits all" assumption.
The method uses a hierarchical model with three levels: an observation model, a transmission and importation model, and a prior model.
We introduced a new statistic for error detection that uses a hierarchical model for the stepwise process of first selecting a subset of reads from a genome and then introducing errors during sequencing.
To account for this, we use a hierarchical model, assuming that θanc is normally distributed across loci on the log10-scale with mean μ θ anc and standard deviation σ θ anc.
More suggestions(15)
use a hierarchical model
using a hierarchical particle
using a hierarchical sampling
using a hierarchical proteome
using a hierarchical flowchart
using a statistical model
using a hierarchical grid
using a mechanistic model
using a mathematical model
using a hierarchical classification
using a polygenic model
using a hierarchical approach
using a mixed model
using a hierarchical logit
using a replicable model
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