Use of risk-adjusted CUSUM and RSPRTcharts for monitoring in medical contexts

In this paper we discuss the use of charts derived from the sequential probability ratio test (SPRT): the cumulative sum (CUSUM) chart, RSPRT (resetting SPRT), and FIR (fast initial response) CUSUM. The theoretical development of the methods is described and some considerations one might address when designing a chart, explored, including the approximation of average run lengths (ARLs), the importance of detecting improvements in a process as well as detecting deterioration and estimation of the process parameter following a signal. Two examples are used to demonstrate the practical issues of quality control in the medical setting, the first a running example and the second a fully worked example at the end of the paper. The first example relates to 30-day mortality for patients of a single cardiac surgeon over the period 1994-1998, the second to patient deaths in the practice of a single GP, Harold Shipman. The charts’ performances relative to each other are shown to be sensitive to the definition of the ‘out of control’ state of the process being monitored. In light of this, it is stressed that a suitable means by which to compare charts is chosen in any specific application.

[1]  R J Cook,et al.  Monitoring paired binary surgical outcomes using cumulative sum charts. , 1999, Statistics in medicine.

[2]  Elisabeth J. Umble,et al.  Cumulative Sum Charts and Charting for Quality Improvement , 2001, Technometrics.

[3]  G. Moustakides Optimal stopping times for detecting changes in distributions , 1986 .

[4]  A A Tsiatis,et al.  Exact confidence intervals following a group sequential test. , 1984, Biometrics.

[5]  J. Andel Sequential Analysis , 2022, The SAGE Encyclopedia of Research Design.

[6]  D. A. Evans,et al.  An approach to the probability distribution of cusum run length , 1972 .

[7]  Chris Sherlaw-Johnson,et al.  Monitoring the results of cardiac surgery by variable life-adjusted display , 1997, The Lancet.

[8]  James M. Lucas,et al.  Fast Initial Response for CUSUM Quality-Control Schemes: Give Your CUSUM A Head Start , 2000, Technometrics.

[9]  M. Chang,et al.  Confidence intervals for a normal mean following a group sequential test. , 1989, Biometrics.

[10]  George O. Wesolowsky,et al.  Grouped data-sequential probability ratio tests and cumulative sum control charts , 1996 .

[11]  G. Barnard Control Charts and Stochastic Processes , 1959 .

[12]  M R de Leval,et al.  Analysis of a cluster of surgical failures. Application to a series of neonatal arterial switch operations. , 1994, The Journal of thoracic and cardiovascular surgery.

[13]  P Littlejohns,et al.  Cumulative risk adjusted mortality chart for detecting changes in death rate: observational study of heart surgery , 1998, BMJ.

[14]  E. S. Page CONTINUOUS INSPECTION SCHEMES , 1954 .

[15]  K W Kemp,et al.  Internal quality control in clinical chemistry: a teaching review. , 1987, Statistics in medicine.

[16]  J Lovegrove,et al.  Monitoring the performance of cardiac surgeons , 1999, J. Oper. Res. Soc..

[17]  G Gallus,et al.  On surveillance methods for congenital malformations. , 1986, Statistics in medicine.

[18]  R. Khan,et al.  Sequential Tests of Statistical Hypotheses. , 1972 .

[19]  A. Goel,et al.  Determination of A.R.L. and a Contour Nomogram for Cusum Charts to Control Normal Mean , 1971 .

[20]  Rasul A. Khan On Cumulative Sum Procedures and the SPRT with Applications , 1984 .

[21]  S. Steiner,et al.  Monitoring surgical performance using risk-adjusted cumulative sum charts. , 2000, Biostatistics.

[22]  E. Yashchin On a unified approach to the analysis of two-sided cumulative sum control schemes with headstarts , 1985, Advances in Applied Probability.