Large Clinical Trial
Clinical trials are investigative research where volunteers get tested for new treatments, tests, or interventions to treat, prevent, or detect diseases and other medical complications (Ritchie, 2018). Some of these investigations look at the manner in which people respond to new interventions or the side effects accompanying these interventions. To do this, the researchers take a sample to represent the total population. For clinical research, statistically significant data are interpreted to be clinically important.
In this case, the total trial was 2243, with mean hemoglobin of 11.8. However, the reported mean, in conclusion, was 12. After performing statistical analysis, the difference emerged to be a statistically significant difference. This raises the question of how to obtain the clinical importance of the study? How the study can be applied to practice?
How to obtain clinical importance of the study?
To determine the critical importance of this data, I will take the difference between the two means (12 and 11.8) and divide by the standard deviation. By fact, the larger the number, the stronger the clinical importance (Sedgwick, 2015). Therefore, the total number of trials (N-2243) is large enough to obtain the clinical importance of the study. This will increase the level of accuracy that might not be proven by p-value.
How the study can be applied to practice?
When a study displays statistical significance with no availability of real clinical significance, its application will depend on the implications it will have on the existing sample size. As suggested by Lefort, clinical significance should show the extent to which it interferes with subject lives, time, and implementation (Boutouyrie & Bruno, 2018). Therefore, since the sample size is large, the statistical significance will be large, implying that the clinical value will be significant; hence, applicable in the study.
References
Boutouyrie, P., & Bruno, R.-M. (2018). The Clinical Significance and Application of Vascular Stiffness Measurements. American Journal of Hypertension, 32(1), 4–11. https://doi.org/10.1093/ajh/hpy145
Ritchie, M. D. (2018). Large-Scale Analysis of Genetic and Clinical Patient Data. Annual Review of Biomedical Data Science, 1(1), 263–274. https://doi.org/10.1146/annurev-biodatasci-080917-013508
Sedgwick, P. (2015). Clinical significance versus statistical significance. BMJ, 348(mar14 11), g2130–g2130. https://doi.org/10.1136/bmj.g2130