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In this paper, we propose approaches for determining the samples size for each level of a 3-level hierarchical trial design based on ordinary least squares (OLS) estimates for detecting a difference in mean slopes between two intervention groups when the slopes are modeled as random.
Overall, PLINK consistently generated the highest regression power estimates for detecting autozygosity, outperforming GERMLINE and BEAGLE.
The predictive performance of the three GFR estimates for detecting a deterioration in renal function did not differ significantly (p > 0.05 in the X2 test).
The predictive performance of the three GFR estimates for detecting an improvement in renal function did not differ significantly (p > 0.05 in the X2 test).
PLINK consistently outperformed both GERMLINE and BEAGLE, producing higher regression power estimates for detecting autozygosity within 20 and 50 generations, regardless of the genotyping error rate.
Main outcome measures Sensitivity and specificity estimates for detecting Barrett's oesophagus compared with gastroscopy as the ideal method, and patient anxiety (short form Spielberger state trait anxiety inventory, impact of events scale) and acceptability (visual analogue scale) of the test.
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Expected accuracy was estimated for detecting both individual gene variants from all other strains and for detecting homologous gene groups (HGs).
These results provide further evidence that Froh is likely to be the optimal estimate for detecting inbreeding depression in populations, such as humans, that have expanded rapidly in population size.
58 Sample size has been estimated for detecting a 3.25% difference in the Central Augmentation Index (CAIx), half the difference used in the CAFEE study (6.5%), between participants with and without TOD.
If inbreeding depression is caused by homozygosity at rare mutations, as recent evidence indicates, these results suggest that Froh is likely to be the optimal estimate for detecting it, regardless of the level of inbreeding in the population.
We then performed two statistical tests (described below) on these indicators, using (R Core Development Team, 2013), to estimate power for detecting significant temporal changes in these potentially informative indicators.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com