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首页> 美国卫生研究院文献>Antimicrobial Agents and Chemotherapy >Comparison of Censored Regression and Standard Regression Analyses for Modeling Relationships between Antimicrobial Susceptibility and Patient- and Institution-Specific Variables
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Comparison of Censored Regression and Standard Regression Analyses for Modeling Relationships between Antimicrobial Susceptibility and Patient- and Institution-Specific Variables

机译：抗菌药敏性与患者和机构特定变量之间建模关系的删失回归与标准回归分析的比较

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摘要

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 ≤ 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 ≤ 2 mg/literL [2L] or MICs > 2 mg/literR [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 2L − 1 or 2R + 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 ≥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.
机译：为了确定可能感染抗药性细菌病原体的患者，可以将诸如标准回归（SR）之类的分析方法应用于监视数据，以确定预测MIC升高的患者和机构特定因素。但是，MIC数据的审查性质（例如MIC≤0.5 mg / L或MIC> 8 mg / L）对SR的使用施加了一定的限制。为了研究这些限制的性质，进行了模拟以比较针对审查数据量身定制的回归（审查回归[CR]）和针对SR量身定制的回归。通过使用将哌拉西林-他唑巴坦MIC与肠杆菌属细菌相关的模型。根据患者年龄和病床容量，对500个分离株进行了200次模拟。通过使用26对左或右删减（L，R）对（即MIC≤2 mg / L L [2 L ]或MIC> 2 mg / L R [2 R ]。数据由CR和SR拟合，对于这些数据，被检查的MIC被排除（i），（ii）用2 L 或2 R 替换，或（iii）用2 L − 1 或2 R +1 。 26对对的总检查范围为7％至86％。通过CR，对于26对中的每对，所有参数的平均参数估计值与真实参数值的偏差均小于0.10 log2（mg / L）。通过SR，对于26个对中的至少18个，除一个参数外，这些偏差均> 0.10 log2（mg / L）。对于所有SR方法，单个参数的两个标准错误置信区间仅占病例的0％，而对于CR方法，则占≥91.5％。对受检MIC数据进行建模时，CR可以减少或消除SR获得的偏差参数估计。

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