CGR-CUSUM: a continuous time generalized rapid response cumulative sum chart.

IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biostatistics Pub Date : 2023-12-15 DOI:10.1093/biostatistics/kxac041
Daniel Gomon, Hein Putter, Rob G H H Nelissen, Stéphanie Van Der Pas
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Abstract

Rapidly detecting problems in the quality of care is of utmost importance for the well-being of patients. Without proper inspection schemes, such problems can go undetected for years. Cumulative sum (CUSUM) charts have proven to be useful for quality control, yet available methodology for survival outcomes is limited. The few available continuous time inspection charts usually require the researcher to specify an expected increase in the failure rate in advance, thereby requiring prior knowledge about the problem at hand. Misspecifying parameters can lead to false positive alerts and large detection delays. To solve this problem, we take a more general approach to derive the new Continuous time Generalized Rapid response CUSUM (CGR-CUSUM) chart. We find an expression for the approximate average run length (average time to detection) and illustrate the possible gain in detection speed by using the CGR-CUSUM over other commonly used monitoring schemes on a real-life data set from the Dutch Arthroplasty Register as well as in simulation studies. Besides the inspection of medical procedures, the CGR-CUSUM can also be used for other real-time inspection schemes such as industrial production lines and quality control of services.

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CGR-CUSUM:连续时间广义快速反应累积和图。
迅速发现医疗质量方面的问题对病人的福祉至关重要。如果没有适当的检查计划,这些问题可能多年都不会被发现。累积总和(CUSUM)图表已被证明可用于质量控制,但用于生存结果的可用方法却很有限。为数不多的连续时间检测图表通常要求研究人员提前指定故障率的预期增长,因此需要事先了解手头的问题。参数指定错误会导致误报和严重的检测延迟。为了解决这个问题,我们采用了一种更通用的方法来推导新的连续时间广义快速响应 CUSUM(CGR-CUSUM)图表。我们找到了近似平均运行长度(平均检测时间)的表达式,并在荷兰关节成形术登记册的真实数据集和模拟研究中说明了使用 CGR-CUSUM 相比其他常用监控方案在检测速度上可能获得的提升。除医疗程序检测外,CGR-CUSUM 还可用于其他实时检测方案,如工业生产线和服务质量控制。
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来源期刊
Biostatistics
Biostatistics 生物-数学与计算生物学
CiteScore
5.10
自引率
4.80%
发文量
45
审稿时长
6-12 weeks
期刊介绍: Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public''s health.
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