Evidence map›Paper›PMID 36124984›Full record

ArticleBiostatistics (Oxford, England)2023

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

Daniel Gomon, Hein Putter, Rob G H H Nelissen, Stéphanie Van Der Pas

Abstract read
In one paragraph

Article in Biostatistics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Daniel GomonDepartment of Statistics, Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333CA Leiden, The Netherlands.ORCID 0000-0001-9011-3743
Hein PutterDepartment of Biomedical Data Sciences, Leiden University Medical Centre, Einthovenweg 20, 2333ZC Leiden, The Netherlands.ORCID 0000-0001-5395-1422
Rob G H H NelissenDepartment of Orthopaedic Surgery, Leiden University Medical Centre, Leiden, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.
Stéphanie Van Der PasDepartment of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1089A, 1081HV Amsterdam, The Netherlands.

Funding

Dutch Research Council
6 · The paper itself

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.

Indexed as

Computer SimulationHospital Rapid Response TeamQuality ControlStatistics as TopicBenchmarkingContinuous timeControl chartsCUSUMGeneralized likelihood ratioQuality of careSurvival analysis

Identifiers

PMID36124984
PMCPMC10939399

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.