Risk‐adjusted CUSUM charts under model error
| Autor: | Sven Knoth, Philipp Wittenberg, Fah Fatt Gan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Likelihood Functions Markov chain Epidemiology Computer science Stochastic matrix CUSUM Logistic regression 01 natural sciences Markov Chains Constant false alarm rate 010104 statistics & probability 03 medical and health sciences Logistic Models 0302 clinical medicine Robustness (computer science) Statistics Humans Errors-in-variables models Computer Simulation Risk Adjustment 030212 general & internal medicine False alarm 0101 mathematics |
| Zdroj: | Statistics in Medicine. 38:2206-2218 |
| ISSN: | 1097-0258 0277-6715 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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