Risk-adjusted CUSUM charts under model error.

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).

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Knoth S,Wittenberg P,Gan FF

doi

10.1002/sim.8104

subject

Has Abstract

pub_date

2019-05-30 00:00:00

pages

2206-2218

issue

12

eissn

0277-6715

issn

1097-0258

journal_volume

38

pub_type

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