Computational protocol: Comparison of registry and government evaluation data to ascertain severe trauma cases in Japan

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Protocol publication

[…] The hospitals’ characteristics were reported based on the annual evaluation data, which provided information regarding the case volumes, numbers of deaths, and evaluation scores. The characteristics of patients who were registered in the JTDB by the eligible hospitals were reported, including the patients’ demographic characteristics, injury mechanisms, and injury severities.We compared the median hospital‐level outcome values between the two data sources using the Wilcoxon test (the data were paired for each hospital), because the outcome variables were not normally distributed. The Wilcoxon test is equivalent to the paired t‐test, which tests a null hypothesis that the mean difference is zero. According to the methods described by Bland and Altman, we calculated the intrahospital differences in the outcome variables between the two sources (evaluation data subtracted from the registry data at each hospital) and the average of the two values to evaluate the variability in the intrahospital differences. We plotted the differences against the average on a graph, and the average values were categorized into quartiles. We did not perform a scatter plot to avoid the possibility that the analyzed hospitals could be identified based on their values, as hospitals with extreme values would be easily identifiable. These analyses did not require adjustment for the case mix or hospital characteristics, as the hospitals were compared to themselves (intrahospital comparisons).Using the Wilcoxon test, we calculated the effect size using the method described by Kerby, as well as the P‐values from null‐hypothesis significance testing (P < 0.05 was considered significant). The effect sizes indicate the magnitude of differences or associations, and effect sizes of >0.2 are usually considered significant. The sample size was defined as the number of hospitals in the datasets, and the effect size that could be detected based on a sample of 136 hospitals was calculated using G*Power software. With α = 0.05 (two‐sided) and β = 0.2, this sample allows a paired Wilcoxon test to detect a standardized effect size of 0.25. All other analyses were carried out using IBM spss software (version 23; IBM, Armonk, NY, USA). […]

Pipeline specifications

Software tools G*Power, SPSS
Application Miscellaneous