title: Comparison of censored regression and standard regression analyses for modeling relationships between antimicrobial susceptibility and patient- and institution-specific variables.
abstract: In order to identify patients likely to be infected with resistant bacterial pathogens, analytic methods such as standard regression (SR) may be applied to surveillance data to determine patient- and institution-specific factors predictive of an increased MIC. However, the censored nature of MIC data (e.g., MIC < or = 0.5 mg/liter or MIC > 8 mg/liter) imposes certain limitations on the use of SR. In order to investigate the nature of these limitations, simulations were performed to compare a regression tailored for censored data (censored regression [CR]) and one tailored for an SR. By using a model relating piperacillin-tazobactam MICs against Enterobacter spp. to patient age and hospital bed capacity, 200 simulations of 500 isolates were performed. Various MIC censoring patterns were imposed by using 26 left- or right-censored (L,R) pairs (i.e., MICs < or = 2 mg/liter(L) [2L] or MICs > 2 mg/liter(R) [2R], respectively). Data were fit by CR and SR for which censored MICs were either (i) excluded, (ii) replaced by 2L or 2R, or (iii) replaced by 2(L - 1) or 2(R + 1). Total censoring for the 26 pairs ranged from 7 to 86%. By CR, deviations of average parameter estimates from the true parameter values were <0.10 log2 (mg/liter) for all parameters for each of the 26 pairs. By SR, these deviations were >0.10 log2 (mg/liter) for at least 18 of the 26 pairs for all but one parameter. Two-standard-error confidence intervals for individual parameters contained as little as 0% of cases for all SR approaches but > or = 91.5% of cases for the CR approach. When censored MIC data are modeled, CR may reduce or eliminate biased parameter estimates obtained by SR.
authors.affiliation: Cognigen Corporation, Buffalo, New York, USA.
authors.forename: Jeffrey P
authors.initials: JP
authors.lastname: Hammel
authors.forename: Sujata M
authors.initials: SM
authors.lastname: Bhavnani
authors.forename: Ronald N
authors.initials: RN
authors.lastname: Jones
authors.forename: Alan
authors.initials: A
authors.lastname: Forrest
authors.forename: Paul G
authors.initials: PG
authors.lastname: Ambrose
mesh_terms: D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D000890:Anti-Infective Agents / Q000494:pharmacology; D003198:Computer Simulation; D024881:Drug Resistance, Bacterial / Q000502:physiology; D004754:Enterobacter / Q000187:drug effects; D006801:Humans; D016014:Linear Models; D008826:Microbial Sensitivity Tests / Q000592:standards / Q000706:statistics & numerical data*; D008954:Models, Biological; D008957:Models, Genetic; D011237:Predictive Value of Tests; D012044:Regression Analysis; D012107:Research Design
chemical_list: D000900:Anti-Bacterial Agents; D000890:Anti-Infective Agents