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Among the five heterogeneous variances MTC model, the informed variances model based on frequentist approximate distributions produced variance estimates closest to the frequentist ones and had the posterior variance distributions with the highest precision.
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The two commonly used approaches for producing variance estimates for estimated regression coefficients are, the analytical technique [ 9, 10] and a replication approach [ 9, 11].
If not, then the process that produced the variance is scale free and has no characteristic scale or quantity of variance.
This sensitivity analysis showed that the weighted data produced less variance than the unweighted datasets and that the costing assumptions used in the main analysis also produced the lowest variance compared with the additional varied assumptions.
Trialling a number of different priors produced similar variance component estimates, with low autocorrelation among iterations in most runs.
The ULOQ was the highest standard that produced ≤40% variance and an average intraexperimental error of 9.3% CV.
The self-adjusting scale was effective to deal with differential assessor expansiveness, produced homogeneous variances but its data showed gross deviations from normality.
The two methods produced additive variances (VA) that were highly correlated (ρ = 1.00, N = 8, P<0.001) and did not differ significantly in magnitude (ZW = 0.14, N = 8, P = 0.89; Table 2).
However, to the extent other factors than the ones explored by Turner et al. determine the likely degree and distribution of heterogeneity variance, the approach may not produce optimal variance estimation.
The exchangeable variance model and the empirically informed variances model also produced seemingly reliable variance estimates.
This produces the variance within (intra) a tumor line, the variance between (inter) tumor lines and the total (intra + inter) variance.
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