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Human Pharmacodynamics of Anti-infectives 419time > MIC). It is now possible to examine the relationship between exposure andresponse and/or toxicity.In the study cited above, a parameter vector was calculated for each patientby Bayesian estimation. The plasma drug concentrations were then simulated forthe specific times they were obtained, and a predicted versus observed plot wasproduced. Figure 3 displays this analysis. The best-fit line was:Observed ¼ 1.001 · predicted + 0.0054, r 2 ¼ 0.966; p MIC, etc.) to outcomes. For continuous outcomevariables (e.g., viral copy number and CD4 counts), continuous functions such asa traditional sigmoidal E max effect function would be a natural choice (Fig. 1A–F).However, clinical trials frequently have either dichotomous outcome variables(e.g., success/failure and eradication/persistence) or time-to-event end points(e.g., time to death, time to opportunistic infection, and time-to-lesion change incytomegalovirus retinitis). For dichotomous outcome variables, logistic regressionanalysis is a natural choice. For the prospective study examining levofloxacin citedabove, we had an analysis plan that tested 13 covariates univariately (4,22). Modelbuilding then ensued from the covariates that significantly altered the probabilityof a good clinical or microbiological outcome (separate sets of analyses). The finalmodels for clinical and microbiological outcomes are displayed graphically inFigures 4 and 5, respectively.It should also be noted that small boxes on the probability curve denoteindependent variable “breakpoints.” These are arrived at through classificationand regression tree (CART) analysis. These merely indicate that patients whose1.000.900.80Probability0.700.600.500.40Pulmonary InfectionsSkin & Soft Tissue Infections0.300.200.10Patient n = 134; 7 Patients FailedMcFadden’s Rho 2 = 0.337Breakpoint = 12.20.000 10 20 30 40 50Peak/MIC ratioFIGURE 4 Logistic regression relationship between levofloxacin peak/MIC ratio and the probabilityof a good clinical outcome. Abbreviation: MIC, minimum inhibitory concentration.