2022
DOI: 10.1097/prs.0000000000009293
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How to Report Data on Bilateral Procedures and Other Issues with Clustered Data: The CLUDA Reporting Guidelines

Abstract: lastic surgery is often conducted on bilateral organs, which can result in clustering of data. [1][2][3] Clustering is when some observations within a data set are internally related and, therefore, are more similar when compared with other observations in the data set. An example is patients undergoing bilateral breast surgery.The two breasts of one individual are related and, therefore, are expected to be more alike than the breasts of two individuals. Clustered data can be a challenge when reporting and ana… Show more

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Cited by 10 publications
(4 citation statements)
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“…Baseline, intraoperative, and outcomes data were evaluated for significant differences between the 4 randomized study groups. To account for non-independence of patients due to the performance of bilateral procedures, random-effects linear regression was used to determine differences in continuous variables and random-effects logistic regression was used to determine differences in dichotomous variables [5][6][7] .…”
Section: Discussionmentioning
confidence: 99%
“…Baseline, intraoperative, and outcomes data were evaluated for significant differences between the 4 randomized study groups. To account for non-independence of patients due to the performance of bilateral procedures, random-effects linear regression was used to determine differences in continuous variables and random-effects logistic regression was used to determine differences in dichotomous variables [5][6][7] .…”
Section: Discussionmentioning
confidence: 99%
“…Binary outcomes were described as simple proportions, and continuous outcomes were described as mean (SD) values or median (IQR) values, depending on the distribution. For each antibiotic, the concentration over time was modeled as a 3-parameter Weibull dose-response function with a fixed lower limit of zero and with a robust covariance matrix estimation to account for multiple measurements per patient . Model selection was performed as a comparison of Weibull, log-logistic, quadratic, and cubic models, and the final model was chosen based on the lowest value of the Akaike information criterion.…”
Section: Methodsmentioning
confidence: 99%
“…For each antibiotic, the concentration over time was modeled as a 3-parameter Weibull dose-response function with a fixed lower limit of zero and with a robust covariance matrix estimation to account for multiple measurements per patient. 29 Model selection was performed as a comparison of Weibull, log-logistic, quadratic, and cubic models, and the final model was chosen based on the lowest value of the Akaike information criterion. The following parameters were calculated for each curve: peak concentration, antibiotic half-life, and time above the MIC.…”
Section: Methodsmentioning
confidence: 99%
“…The correlation between the 2 breasts (that 2 breasts within the same patient are alike) was accounted for with robust covariance matrix estimation. 13 Statistical comparisons of baseline characteristics between the 3 irrigation groups were conducted according to variable type and data distribution. The unadjusted risk of implant infection in the 3 groups was analyzed using a univariable logistic regression.…”
Section: Statistical Analysesmentioning
confidence: 99%