Low back pain (LBP) is a significant health problem worldwide. Pharmaceutical treatments can help ease this pain but pose some dangers to patients. The study “Hypothesis Tests and Estimation” in Medical Statistics published by Thomas in 2005 reveals that acupuncture and massage are just as good at helping people with chronic low back pain seeking primary care.
The study did not indicate where the population estimate will land. As a result, there was a need to research further the numbers over the past few weeks. The confidence level indicates the range of values around sample statistics (Orsini et al.,2020). A difference in mean pain scores of 8.1, with a 95% confidence interval (1.2,15.0). The 90% confidence interval is more spread (2.3 to 13.9). 9​5% confidence interval is much smaller than 99% confidence interval (-1.0, 17.2).
The results from Thomas’ publication display a big difference between the two groups in how much pain they felt. In some cases, one may find no value of 0 between two ranges. With this, there will be no noticeable change in the average pain scores (Lam et al.,2013). The higher the confidence interval, the larger the difference in pain. In the sixth week of this course, we learned that a confidence interval is a range within which a medical study’s results or measurements can be expected.
The average pain score between the two groups may now be the same as this level of certainty. There is no doubt that pharmacological therapy works to relieve pain. However, it is essential to remember that it may have severe side effects. In this trial, it was shown that treatment that did not involve drugs worked.
References
Lam, M., Galvin, R., & Curry, P. (2013). Effectiveness of acupuncture for nonspecific chronic low back pain: a systematic review and meta-analysis. Spine, 38(24), 2124-2138.
Orsini, L. S., Berger, M., Crown, W., Daniel, G., Eichler, H. G., Goettsch, W., Graff, J., Guerino, J., Jonsson, P., Lederer, N. M., Monz, B., Mullins, C. D., Schneeweiss, S., Van Brunt, D., Wang, S. V., & Willke, R. J. (2020). Improving transparency to build trust in real-world secondary data studies for hypothesis testing—why, what, and how: recommendations and a road map from the real-world evidence transparency initiative. Value in Health, 23(9), 1128-1136.
Thomas, E., 2005. An introduction to medical statistics for health care professionals: Hypothesis tests and estimation. Musculoskeletal Care, 3(2), 102-108.