2.7 Data and Statistical analysis
The data and statistical analysis comply with the recommendations on experimental design and analysis in pharmacology (Curtis et al., 2018). Urinary output is expressed as mean ± SD. All available urine samples were analyzed. Urinary biomarkers were standardized for urinary volume by calculating the total amount excreted in each 24-h period. Differences between groups were assessed using mixed-effects, restricted maximum likelihood estimation regression, with repeated measures occurring over days; measures were repeated at the level of the individual rat (STATA 17.0 BE, StataCorp LLC, College Station, Texas). The primary analysis reports the simple effects from joint tests of drug treatment group within each level of treatment day. Margins were calculated for a full factorial of the variables, i.e. main effects for each variable and interactions. Referent groups were pre-treatment baseline values and saline as a treatment, except where noted. Secondarily, to remain agnostic to outcome variable relationship over time (and assess treatment groups over time), locally weighted scatterplot smoothing (LOWESS) trendlines with 95% confidence intervals were generated. LOWESS graphs were produced in R version 4.2.2 using ggplot2 (”H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.,”). No data were excluded as outliers. Vancomycin kidney concentrations were expressed as median (interquartile range) and compared by the non-parametric Kruskal Wallis test. Multiple Hill-type models were explored to describe the relationship between vancomycin kidney concentrations and urinary KIM-1. All tests were two-tailed, a p<0.05 was required for statistical significance. Graphs were generated using GraphPad version 9.3.1 unless specified otherwise.