Whereas median (and lower and upper quartiles) sample size of empirical studies in Table 1 was 1127 (710, 2913), such sample sizes can only explicitly prove a difference from the prediction of R = 1.90 at a margin of error 5%.
The third scenario (S3) assumes having the median, first quartile, third quartile, and sample size (S3 = { xQ1, xmed, xQ3, n}).
Another alternative method was proposed by Bland [ 3] estimating these quantities based on the minimum, first quartile, median, third quartile, maximum, and sample size.
There is relatively little variation in medians and inter-quartile ranges of sample size across different types of intervention comparison.
Finally, we must interpret the results with caution, because statistically significant interactions were not noted despite consistent findings for racial/ethnic differences, likely due to increased degrees of freedom when phthalate exposures were assessed in quartiles and limited sample size in minority groups.
We denote the sample summary statistics as follows: minimum (xmin), first quartile (xQ1), median (xmed), third quartile (xQ3), maximum (xmax), and sample size (n).
Throughout the paper, we define the following summary statistics: a= the minimum value, q1= the first quartile, m= the median, q3= the third quartile, b= the maximum value, n= the sample size.
Although the sample size in these quartiles was small (Supplementary Table S1 online), there appeared to be a trend toward lower response rates in heavier patients, with PGA response exhibiting an overall higher responder rate for the lowest weight quartile and PASI75 displaying a similar trend for the tofacitinib 5- and 15-mg groups.
Moreover, small sample size in subgroups in quartiles 1 4 of glycemic control or blood pressure control in Table 3 could affect statistical significance.
For quantitative variables means, medians, standard deviations, minimum, maximum, and quartiles, as well as the respective available sample size and the number of missing values were calculated.
