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It is recommended to adopt sufficiently large samples (based on power calculations) because of its obvious advantages.
It is possible to quantify uncertainty in complex calculations in health research, as is commonly done for non-sample-based calculations in business or engineering.
The number of subjects was based on a priori sample size calculation based on a previous study evaluating kinematics and kinetics in runners with lateral hip pain [ 15].
These samples were accounted for with sample size calculations based on the 1167 samples received.
Sample size calculations based upon EQ-5D showed similar results [ 17].
We first describe the D statistic, and then present sample size calculations based on D for use in prognostic studies.
Sample size calculations based on a 0.95 confidence interval suggested we needed 600 study participants for a reliable statistical analysis.
The statistical analysis was performed by a sample size calculation based on a priori assumption of p = 0.05.
The sample size calculation based on CAL is the same as the calculation using PD.
However, we made a careful sample size calculation based on retrospective data from our institution.
A sample size calculation based on the RMDQ yields approximately the same result.
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