Studies on the impact of the FHCI are limited and have either been descriptive in design or restrictive in sample sizes, thus limiting the strength of the findings.
For example, safety information derived from comptive randomised trials may fail to detect safety concerns because of limited sample size and restrictive inclusion and exclusion criteria.
Thus our sample size might be too restrictive for further analyses, for example, based on structural equation modeling.
However, traditional parametric sample size estimations depend upon restrictive assumptions that often do not hold in real data.
We considered but did not use the CMS definition for CHF readmission [ 31], because this more restrictive definition would have reduced the available sample size for the analysis.
Only three trials evaluated the safety of a restrictive versus routine CXR strategy; the combined sample size of these trials would only have sufficient power to detect an implausibly large difference in our primary outcome.
Obstructive lung disease: defined as having FEV1 <80%andd FEV1/FVC <70%; restrictive lung disease: defined as having FEV1 <80%andd FEV1/FVC >70%, sample size reduced due to missing/incomplete information; a composite variable containing both obstructive and restrictive lung function was created.
As this calculation is currently debated in terms of being the lowest limit and given the severity of the disease, the restrictive access criteria, and the limited recruitment options, a sample size of N = 200 subjects was envisaged to guarantee a statistically robust estimate [ 44].
Many of these designs assign certain restrictive functional forms to the design elements of stage 2, such as sample size, critical value and conditional power functions.
Because of limitations in sample size, we collapsed these categories to normal and abnormal (restrictive, obstructive, and mixed).
