25 Aug Vavken, Heinrich, Koppelhuber, Rois, and Dorotka (2009) conducted
Vavken, Heinrich, Koppelhuber, Rois, and Dorotka (2009) conducted a search study published as “The Use of Confidence Intervals in Reporting Orthopedic Research Findings” by the Clinical Orthopaedics and Related Research academic journal. This research study discussed the importance of utilizing confidence intervals in academic research studies, particularly in the field of orthopedic medicine (2009). The study looked closely at 48 different orthopedic medical journals and assessed the prevalence of the utilization of statistical tools such p value, significant result, standard deviation, participating methodologist, and calculated average effect size (2009). In assessing these journals prevalence of usage of these methods, confidence intervals were utilized to help establish the most realistic perspective on the actual size effect of orthopedic journals utilization of these statistical tools, particularly confidence intervals themselves.
Vayken, Heinrich, Koppelhuber, Rois, and Dorotka (2009) had a very interesting purpose for this study. They claim that “Conflict between clinical importance and statistical significance is an important problem in medical research. Although clinical importance is best described by asking for the effect size or how much, statistical significance can only suggest whether there is any difference” (p.3334). In other words, medical success is gauged on the size affect in relation to the patient’s personally subjective view of increased quality of life, significant pain reduction, and not whether there is a statistically significant correlation found, regardless of being arbitrarily small and possibly meaningless. The researchers pointed how only about %22 with a confidence interval of 95% between (13.7%-31.98%) that utilized confidence intervals. Our researchers closed by saying “Our data suggest that frequency of Cis in reporting orthopaedic research findings is low, although it would be a useful instrument in clearly presenting research findings in context and helping to avoid clinically false-positives, ie, findings with statistical significance but with no or only little clinical relevance or importance (Vayken, et al., 2009).
Reference
Vavken, P., Heinrich, K. M., Koppelhuber, C., Rois, S., & Dorotka, R. (2009). The Use of Confidence Intervals in Reporting Orthopaedic Research Findings. Clinical Orthopaedics and Related Research®, 467(12), 3334-3339. doi:10.1007/s11999-009-0817-7
Respond to…
Locate an example of a research study that uses effect sizes and confidence intervals in its analysis. Explain what these have allowed the researchers to accomplish and/or conclude in the study.
There was a study conducted in regards to the deaths and readmissions after a patient hospital discharge during the December holiday period compared to other discharge times. The research was conducted in Canada throughout the beginning of April 2002 to end of January 2016. a “The results: for all outcome comparisons we report unadjusted and adjusted odds ratios (with 95% confidence intervals. 217 305 (32.4%) patients discharged during the holiday period and 453 641 (67.6%) discharged during control periods had similar baseline characteristics and previous healthcare utilization. Patients who were discharged during the holiday period were lesslikely to have follow-up with a physician within seven days (36.3% v 47.8%, adjusted odds ratio 0.61, 95% confidence interval 0.60 to 0.62) and 14 days (59.5% v 68.7%, 0.65, 0.64 to 0.66) after discharge. Patients discharged during the holiday period were also at higher risk of 30-day death or readmission (25.9% v 24.7%, 1.09, 1.07 to 1.10). This relative increase was also seen at seven days (13.2% v 11.7%, 1.16, 1.14 to 1.18) and 14 days (18.6% v 17.0%, 1.14, 1.12 to 1.15).Per 100 000 patients, there were 2999 fewer follow-up appointments within 14 days, 26 excess deaths, 188 excess hospital admissions, and 483 excess emergency department visit attributable to hospital discharge during the holiday period (Lapointe-Shaw, L., Austin, P., Ivers, N., & et al., 2018).” A Kaplan-Meier curve of time to composite the 30-day death or readmission after hospital discharge. A visual representation using the forest plot of the patient group and the odds ration using the 95% CI. The research study also had visuals of the proportions of death or readmission within seven days compared to the proportion of patients discharged with a physician follow-up with the seven days. The research conducted, data, and visuals help form the conclusion that the patients who are discharged during the holiday period in December are less likely to have a follow-up as an outpatient and are also at a higher risk for death or readmission back to the hospital within the 30 days of being discharged (Lapointe-Shaw, L., Austin, P., Ivers, N., & et al., 2018).
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