used fractional polynomial regression to assess the association between dexamethasone and cortisol levels, both across the entire dexamethasone range and separately among people with dexamethasone >5.0 nmol/l. We only allowed one power term in the
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Bjørn O Åsvold, Valdemar Grill, Ketil Thorstensen, and Marit R Bjørgaas
Mengting Yin, Qianhui Liu, Qingzhong Wang, Yong He, Haolan Song, Xin Nie, and Guixing Li
, parathyroid hormone. The diagnostic power for the Ca/P ratio We analyzed the diagnostic performance of the Ca/P ratio using ROCs. The cut-off point was calculated using Youden’s index (sensitivity + specificity − 1). The ROC curve showed that
Ann-Cathrin Koschker, Bodo Warrings, Caroline Morbach, Florian Seyfried, Nicole Rickert, Pius Jung, Andreas Geier, Ulrich Dischinger, Maike Krauthausen, Martin J Herrmann, Christine Stier, Stefan Frantz, Uwe Malzahn, Stefan Störk, Martin Fassnacht, and the WAS Study Group
at the time points indicated in Supplementary Table 2. For the documentation of changes in obesity-related conditions, we applied definitions given in Supplementary Table 3. Sample size calculation, power analysis, and adaptation of the
Changwei Liu, Jingwen Wang, Yuanyuan Wan, Xiaona Xia, Jian Pan, Wei Gu, and Mei Li
power and sample size calculators ( http://www.powerandsamplesize.com/ ). Based on the previous study ( 11 ), we estimated the sample size for our study. If the power equaled 0.9, the minimum sample size was about 42. Our study met the minimum sample
Min Yang, Xiangling Deng, Shunan Wang, Bo Zhou, Wenquan Niu, and Zhixin Zhang
to differences in races or ethnicities, genetic underpinnings, study designs, statistical power, as well as the characteristics of study populations. To shed some light on the possible reasons and yield more information for future studies, we
Qiuyu Huang, Hanshen Chen, Fan Xu, Chao Liu, Yafeng Wang, Weifeng Tang, and Liangwan Chen
.000 CC/GG/GG 0 0.00 0 0.00 - - Power calculation Using α = 0.05 as the criterion of the Type I error probability (two-sided), we calculated the power value of this study. For miR-146a rs2910164 SNP, the power value was 0
David S Mathiesen, Jonatan I Bagger, Katrine B Hansen, Anders E Junker, Astrid Plamboeck, Signe Harring, Thomas Idorn, Mads Hornum, Jens J Holst, Anna E Jonsson, Torben Hansen, Tina Vilsbøll, Asger Lund, and Filip K Knop
all statistical analysis, R statistical software version 3.6.1 was used ( www.r-project.org ). Power analysis Standard deviations from a previously published study ( 17 ) was used to calculate the power of our analysis. In the normal glucose
Julia Otten, Andreas Stomby, Maria Waling, Elin Chorell, Mats Ryberg, Michael Svensson, Jens Juul Holst, and Tommy Olsson
.05) with 80% power. The results described in the present paper are secondary outcome measures. Patients were randomized to either the diet group or the diet and exercise group by a statistician not involved in the study, using biased coin minimization with
Michelle J Galvan, Michael J Sanchez, Andrew J McAinch, Jeffrey D Covington, Jason B Boyle, and Sudip Bajpeyi
reliant on whole-body fat oxidation during exercise and are more metabolically flexible ( 4 ). It is possible that our study, despite detecting changes in blood lactate levels, was not appropriately powered to detect changes in energy expenditure and whole
Xiaowen Zhang, Chen Han, Hongwei Wang, Xinghong Sun, Xin Dou, Xueying He, Di Wu, Shanmei Shen, Dalong Zhu, Xinlin Zhang, and Yan Bi
predict their response to treatments. However, it remains unclear to which extent an incremental predictive power of NLR would improve the predictive value of CAS, e.g. in predicting response to therapy. Further prospective studies are needed
