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With the sample size available to us, and with the minor allele frequencies of the IDE variants, we had 80% power to detect ORs of at least 1.40, 1.17 and 1.11, or no more than 0.66, 0.85 and 0.90, for variants with MAFs of 1.7%, 10.3% and 35.5%, respectively.
With our sample size of ∼3500 cases and ∼13500 controls, we had ∼80% power to detect ORs of 1.2 with 30% minor allele frequency SNPs.
Based on probability of exposure in controls obtained in our study (0.19), we had 56.9% power to detect ORs of 1.68 at P < 0.05.
Even in our most underpowered subgroup (28 day survival in those with pneumococcal CAP sepsis) we had 80% power to detect ORs of 2.5 or more.
With 55 evaluable participants/group we will have >80% power to detect ORs of 2.0 or greater assuming α = 0.05 (2-sided) [ 66].
Specifically, the study size provided 80% power to detect ORs of 1.5 or 0.7 for MAFs ranging from 0.10 to 0.50, assuming an additive mode of inheritance; and MAFs ranging from 0.20 to 0.35, assuming a dominant inheritance.
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Our study has 80% power to detect OR of 1.13 (at α = 5 × 10−8) for SNPs with minor allele frequency (MAF) >5%, assuming an additive model.
The present study had a statistical power of 0.40 and 0.77 to detect ORs of 1.29 and 1.5, respectively, for carriage of this allele.
With a power of 90% to detect OR of 0.4 for an association between 'good quality care' and LEA in people with diabetes, 107 cases and 321 controls are required.
We consider an estimated OR based on these studies as appropriate, as the UK is our closest neighbour and most similar to ourselves in term of sociodemographics. 4 With a power of 90% to detect OR of 0.4 for an association between 'good quality care' and LEA in people with diabetes, 107 cases and 321 controls are required.
At the 5% level of significance, we had 80% power to detect an ORs of 1.5 if the prevalence of medium-to-high exposure to shiftwork was at least 4%.
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