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Identifying loci conferring susceptibility to osteoarthritis will require large-scale sample sizes and well-defined phenotypes to minimise heterogeneity.
Large-scale sample sizes and well-defined phenotypes will be required to gain a better understanding of this genetic component, possibly leading to optimising treatment, developing efficacious disease-modifying interventions, improving prognosis and tailoring intervention to the individual.
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The future study would perform clinical trials on a larger scale sample size, and as a comparison study among conventional and 3D printed casts to assess whether clinical differences were statistically significant.
Further study in a large scale sample size is needed.
The major concern is that the majority of studies initially screen small sample sizes without validation by large-scale samples.
Additionally, sensitivity analyses according to various exclusion criteria (full-text article, large-scale trials (sample size >100), patients with pre-existing renal impairment or chronic kidney disease, elective or urgent surgery, and only elective surgery) were also conducted.
One has been towards larger and larger sample sizes.
Biomarker study designs involving large-scale clinical samples (for instance, sample sizes in the thousands) are becoming more prevalent [ 3].
However, genotyping at this fine scale requires large sample sizes to provide the statistical power needed to differentiate the candidate causal SNPs from the non-causal SNPs.
So, although the model scales to experiments requiring larger sample sizes, to scale the local cluster, we would have to add more nodes.
Therefore, further larger scale multicenter neurogeriatric surveys with larger sample sizes for neurological subgroups should be performed not only to confirm our observations but also to acquire more extensive knowledge of the effectiveness of preventive measures in patient cohorts with various neurological conditions and different degrees of disability.
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