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Under plausible assumptions about the net variance reduction achievable through particular features of our study design (e.g. within-physician randomization and repeated measures regression data analysis)[ 33] and about the rate of attrition for enrolled participants after study baseline, a sample size of 230 was targeted as being sufficient to achieve the required effective sample size of 284.
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For example, ethnic minority status was not a significant predictor with the addition of the maternal anxiety about diabetes measure (block 2 of the regression; data not shown).
Data was analysed with a multilevel linear repeated measures regression model.
A study using regression-based measures and data from the 2008 DHS found pro-rich inequities in several key maternal and child health outcomes and interventions [ 5].
The outcomes at each time point will be analyzed as repeated measures data using hierarchical regression methods to account for the nesting within the data.
Linear regression was used to determine associations between BMI and outcome measures, combining data from both TKR groups.
Correlation between measurements was analyzed by linear regression using data from rats in which values for all three measures were available.
Data were analysed within a repeated measures logistic regression framework using NONMEM (ver 7.2).
Then, the performances of the GEP formulas are compared with the measured data and the regression and conventional rheological models (De Kee and Robertson-Stiff) for use in practice.
One thousand sampling iterations were performed and in each case the 28 generated values were combined with the 77 accurately measured data and linear regression performed.
As quantitative assays measure a continuous variable, mean results are often represented by a regression of data (a regression line is a linear average).
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CEO of Professional Science Editing for Scientists @ prosciediting.com