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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.
The approach would be very similar to the one described in this paper: based on biological knowledge, the genes belonging to the same functional group are more likely to be co-expressed, so we can use a hierarchical model to borrow information from and for the genes within the same functional group to improve the estimates of gene expression levels.
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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.
We used a hierarchical model for a single proportion to estimate the proportion of indeterminate index test results.
This was done using a hierarchical model, considering random effects for the parameters of the curve (α i, β i ).
The more realistic simulated data set used a hierarchical model to capture violation of the "one season fits all" assumption.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com