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Regarding variance estimation, Osier (2006, 2009) proposed a variance estimator based on linearization techniques.
In a first step we estimate propensity scores based on a logistic regression, taking into account the complex survey design of the PISA/TREE sample regarding variance estimationg.
Thus, we decided to drop a moment constraint regarding variance of tumour growth velocity α.
To investigate the robustness regarding variance heterogeneity we generated data for n = (40,100,200) with a 1 1 case-control ratio and AUC0 = (0.7,0.8,0.9) (same nine scenarios as above).
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Regarding the variance of the estimators, it can be shown that the optimal combining technique has a variance that is approximately equal to the CRLB at high SNRs (in fact, this is the main reason why this combining technique is called optimal).
Secondly, an inequality regarding the variance was proposed together with a calculation formula, which can greatly simplify the calculation process.
However, the existing researches regarding the variance based on the credibility measure are hardly seen due to the complexity of the calculation process.
Regarding the variance, the inter-calibration source component, σ C F r A 2, was negligible compared to the intra-calibration source component, σ ε 2. The inter-calibration source component is the component of variance that is between sources, and the intra-calibration source component is the variation for the repeated measurements at different days for a single source.
The t-test assumption regarding equal variance was met (Levene's test F = 0.00, P = 1.0).
The natural log of viability was used as the outcome to meet analysis assumptions regarding equal variance.
These distributional violations may have important implications for the inference regarding relative variance components when analysing sum scores (Derks et al. 2004).
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