Risk‐adjusted CUSUM charts under model error
Publication date
2019-02-05
Document type
Forschungsartikel
Author
Organisational unit
Scopus ID
Publisher
Wiley
Series or journal
Statistics in Medicine
ISSN
Periodical volume
38
Periodical issue
12
First page
2206
Last page
2218
Part of the university bibliography
✅
Language
English
Keyword
average run length to false alarm
binary logistic regression
Markov chain approximation
Parsonnet score
power transformation
Toeplitz matrix
Abstract
In recent years, quality control charts have been increasingly applied in the healthcare environment, for example, to monitor surgical performance. Risk‐adjusted cumulative (CUSUM) charts that utilize risk scores like the Parsonnet score to estimate the probability of death of a patient from an operation turn out to be susceptible to misfitted risk models causing deterioration of the charts' properties, in particular, the false alarm behavior. Our approach considers the application of power transformations in the logistic regression model to improve the fit to the binary outcome data. We propose two different approaches of estimating the power exponent δ . The average run length (ARL) to false alarm is calculated with the popular Markov chain approximation in a more efficient way by utilizing the Toeplitz structure of the transition matrix. A sensitivity analysis of the in‐control ARL against the true value δ shows potential effects of incorrect choice of δ. Depending on the underlying patient mix, the results vary from robustness to severe impact (doubling of false alarm rate).
Version
Published version
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