To create industry awareness, we demonstrate two examples out of an OEM driven study on mechanical induced parametric deviations which relate to corresponding product verification/validation issues that can end up in real, but which are mostly classified as no fault found (NFF) issues.
Appropriate parametric (for normally distributed data) and non-parametric (deviation from normality in data distribution) approaches will be used to ascertain clinically meaningful group differences at each time point.
This non-parametric statistic is approximately equivalent to the parametric standard deviation, and can be used to show areas where there is greater or less variation between GCMs.
Continuous data are reported as medians and percentiles (nonparametric data) or as means and standard deviation (parametric data), and categorical data as numbers with percentages.
Categorical variables were expressed as percentages; continuous variables were expressed as means ± standard deviation (parametric variables) or median and interquartile range (IQR) (nonparametric variables).
Distributions were checked and as all variables approximated normality with relatively small deviations, parametric tests were used.
For continuous data (e.g. age), means and standard deviations (parametric data) or medians and percentiles (nonparametric data) will be calculated.
All measurements are presented with mean ± standard deviation for parametric tests and median (range) for non-parametric tests.
For continuous data, the mean and standard deviation (SD) (parametric data) or medians and interquartile ranges (non-parametric) data were calculated.
Further specific informed consent was obtained from the patient whose images are depicted in Figures 1 and 2. Descriptive analysis was performed as appropriate (mean and standard deviation for parametric data and median and range for non-parametric data).
