Process Mapping of Methodology Used to Create Bug-Drug Matrices for Active and Optimal Empirical Antibiotic Choice
REGRESSION
The bug-drug matrices were used to derive 2 binary outcomes for each case’s selected antibiotic against each of the urine isolates summarized in the antibiogram: (1) active or not active and (2) optimal or not optimal. A mixed-effects logistic regression model with selection of an empirical antibiotic active against the organism recovered from urine culture (yes or no) as the outcome, vignette as a covariate, and study group as a fixed effect was created. Each participant was treated as a random effect in this model to account for within-participant association across the vignettes for which they provided an antibiotic choice. A similarly constructed model with selection of an optimal antibiotic agent (yes or no) as the outcome variable was also constructed. In the presence of a detected effect of study group, Tukey-adjusted pairwise comparisons of the 3 groups, averaged across case, were performed to determine which groups differed significantly from each other.To evaluate for possible differences in study group effects between vignette cases, a second set of mixed-effects logistic regression models estimated differences across the study groups in selecting active or optimal antibiotics while considering the case as a possible effect modifier. This was tested by including an interaction between study groups and cases. Respondents (participants) were considered as a random effect in these models as well. In the presence of a detected interaction effect, Tukey-adjusted pairwise comparisons were performed to summarize study group differences by each case. Finally, respondent demographics were considered for inclusion in the model as fixed effects and compared with models without demographics using likelihood ratio χ