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Sampling performed particularly well when indicators were calculated in the full dataset while differences in estimates and sample distribution were more variable when analysed at site level.
The individual study estimates and sample sizes are shown in figure 1.
By estimating the confidence intervals around the point estimate, we are able to visually explore the extent of data convergence or divergence when the point estimates and sample sizes differ in the qualitative and quantitative strands.
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We applied bootstrap weights for variance estimates and sampling weights for point estimates to account for the complex survey design.
Hierarchical models have the advantage of yielding accurate parameter estimates and sampling variances in the presence of correlated errors [ 18].
Sampling weights were used to calculate population estimates, and sampling strata and clustering within primary sampling units were accounted for to estimate variances and test for significant differences.
In all analyses, bootstrap weights for variance estimates and sampling weights for point estimates were applied to account for the complex survey design.
There were no associations between TIN-estimates and sample characteristics in any of the other datasets (characteristics listed in Table 1).> -wrap-foot> aPatient samples analyzed in-house bTwo samples from the GEO entry (GSM333256 and GSM333270) were excluded due to failure of reading the raw data files.
A confirmatory factor analysis (CFA) model determines the true population variance, covariance, estimated population variance, sample covariance, and estimated covariance, as well as the related overall difference and the approximated, estimated, and sample differences.
Bootstrapping has been previously used for assigning measures of accuracy to sample estimates and the sample distribution [ 18, 36].
In this paper, we build on a recent development for estimating and sampling from probabilities concentrated on a diffusion manifold.
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