Abstract

Pharmaceutical and biotechnology industries manufacture their products in clean rooms, which are designed to minimize levels of particulates (like microorganisms recovered from the air or from the clean room surfaces). Alert and action limits are employed to monitor and control the state of the room, keeping the level of particulates at appropriate levels. Particulate monitoring systems could generate particulate count data with the following characteristics: have repeated counts, have inflated zero or low counts, and could be dispersed and have distributions with long thin tails to the right. In this article, we present comparisons of four statistical modeling techniques for setting alert and action limits (i.e., traditional percentile, parametric bootstrap, nonparametric bootstrap, and Bayesian with informative priors) using simulated environmental monitoring data under controlled experimental conditions, to better understand the strengths and limitations of these techniques.

摘要

制药和生物技术行业在洁净室中制造产品，旨在最大限度地减少颗粒物（如从空气或洁净室表面回收的微生物）水平。警报和行动限制用于监测和控制房间的状态，将微粒水平保持在适当的水平。颗粒物监测系统可以生成具有以下特征的颗粒物计数数据：重复计数、膨胀为零或低计数、可能分散并具有向右长细尾的分布。在本文中，我们比较了四种用于设置警报和行动限制的统计建模技术（即传统百分位数、参数引导程序、非参数引导程序、