Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: Implications of the data generating process

BMC Medical Research Methodology

Volumn 13, Issue 1, 2013, Pages

  Moran, John L ^a   Solomon, Patricia J ^b  

^a QUEEN ELIZABETH HOSPITAL   (Australia)

^b UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Autocorrelation; Autoregressive moving average models; Exponentially weighted moving average smoothing; GARCH models; Seasonality; Statistical process control; Time series; Volatility

Indexed keywords

ADULT; ARTICLE; AUSTRALIA; FACTUAL DATABASE; HUMAN; INTENSIVE CARE UNIT; MORTALITY; NEW ZEALAND; RISK ASSESSMENT; STATISTICAL MODEL;

ADULT; AUSTRALIA; DATABASES, FACTUAL; HOSPITAL MORTALITY; HUMANS; INTENSIVE CARE UNITS; MODELS, STATISTICAL; NEW ZEALAND; RISK ADJUSTMENT;

EID: 84878006596     PISSN: None     EISSN: 14712288     Source Type: Journal    
DOI: 10.1186/1471-2288-13-66     Document Type: Article

References (76)

1. Quality Improvement in the Modern Business Environment. Montgomery DC, Introduction ot Statistical Quality Control Hoboken, NJ: John Wiley & Sons, Inc, Montgomery DC, 7 2013 3 47
2. The Use of control charts in health-care and public-health surveillance. Woodall WH, J Qual Technol 2006 38 89 104
3. Statistical process control as a tool for research and healthcare improvement. Benneyan JC, Lloyd RC, Plsek PE, Qual Saf Health Care 2003 12 458 464 10.1136/qhc.12.6.458 14645763 (Pubitemid 37542026)
4. Plotting basic control charts: tutorial notes for healthcare practitioners. Mohammed MA, Worthington P, Woodall WH, Qual Saf Health Care 2008 17 137 145 10.1136/qshc.2004.012047 18385409
5. Application of statistical process control in healthcare improvement: systematic review. Thor J, Lundberg J, Ask J, Olsson J, Carli C, Haerenstam KP, Brommels M, Qual Saf Health Care 2007 16 387 399 10.1136/qshc.2006.022194 17913782 (Pubitemid 47612679)
6. Control chart methods for monitoring surgical performance: a case study from gastro-oesophageal surgery. Collins G, Jibawi A, McCulloch P, Ejso 2011 37 473 480 10.1016/j.ejso.2010.10.008 21195577
7. Monitoring the evolutionary process of quality: risk-adjusted charting to track outcomes in intensive care. Cook DA, Steiner SH, Cook RJ, Farewell VT, Morton AP, Crit Care Med 2003 31 1676 1682 10.1097/01.CCM.0000065273.63224.A8 12794403 (Pubitemid 36714220)
8. Crude versus case-mix-adjusted control charts for safety monitoring in thyroid surgery. Duclos A, Voirin N, Touzet S, Soardo P, Schott AM, Colin C, Peix JL, Lifante JC, Qual Saf Health Care 2010 19 1 4
9. The use of statistical process control for monitoring institutional performance in trauma care. Kirkham JJ, Bouamra O, J Trauma 2008 65 1494 1501 10.1097/TA.0b013e31815ebabf 19077648
10. Control chart methods for monitoring cardiac surgical performance and their interpretation. Rogers CA, Reeves BC, Caputo M, Ganesh JS, Bonser RS, Angelini GD, J Thorac Cardiovasc Surg 2004 128 811 819 10.1016/j.jtcvs.2004.03. 011 15573063 (Pubitemid 39575284)
11. Winkel P, Zhang NF, Statistical development of Quality in Medicine Chichester, West Sussex: John Wiley & Sons Ltd 2007
12. Other univariate statistical process monitoring and control techniques. Montgomery DC, Introduction ot Statistical Quality Control Hoboken, NJ: Wiley, Montgomery DC, 7 2013 448 508
13. Effects of autocorrelation on control chart performance. Alwan LC, Commun Stat Theory Methods 1992 21 1025 1049 10.1080/03610929208830829
14. Monitoring sewage-treatment plants-some quality-control aspects. Berthouex PM, Hunter WG, Pallesen L, J Qual Technol 1978 10 139 149 (Pubitemid 9411824)
15. Some statistical process-control methods for autocorrelated data. Montgomery DC, Mastrangelo CM, J Qual Technol 1991 23 179 193
16. Control charts in the presence of data correlation. Wardell DG, Moskowitz H, Plante RD, Manag Sci 1992 38 1084 1105 10.1287/mnsc.38.8.1084
17. Serial correlation of quality control data-on the use of proper control charts. Winkel P, Zhang NF, Scand J Clin Lab Invest 2004 64 195 203 10.1080/00365510410005442 15222629 (Pubitemid 38803103)
18. Control charts for autocorrelated data. Winkel P, Zhang NF, Statistical development of Quality in Medicine Chichester, West Sussex: Wiley, Winkel P, Zhang NF, 2007 92 110
19. Conventional and advanced time series estimation: application to the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database, 1993-2006. Moran JL, Solomon PJ, Adult Database Management Committee (ADMC) of the Australian and New Zealand Intensive Care Society (ANZICS), J Eval Clin Pract 2011 17 45 60 10.1111/j.1365-2753.2010.01368.x 20807296
20. Mortality and Intensive Care volume in ventilated patients, 1995-2009, in the Australian and New Zealand bi-national adult patient intensive care database. Moran J, Solomon P, Crit Care Med 2012 40 800 812 10.1097/CCM. 0b013e318236f2af 22080640
21. Development and implementation of a high-quality clinical database: the Australian and New Zealand Intensive Care Society adult patient database. Stow PJ, Hart GK, Higlett T, George C, Herkes R, McWilliam D, Bellomo R, J Crit Care 2006 21 133 141 10.1016/j.jcrc.2005.11.010 16769456 (Pubitemid 43850185)
22. ANZICS Centre for Outcome and Resource Evaluation (CORE) of the Australian and New Zealand Intensive Care Society (ANZICS), APD Data Dictionary: Version 3.2.1 Updated February 2012 [ http://www.anzics.com.au/core/data- collection-tools ], Accessed June 30th 2012
23. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano A, Chest 1991 100 1619 1636 10.1378/chest.100.6.1619 1959406
24. Rabe-Hesketh S, Skrondal A, Multilevel and Longitudinal Modeling Using Stata College Station, TX: Stata Press 2 2008
25. Exponentially weighted moving average charts to compare observed and expected values for monitoring risk-adjusted hospital indicators. Cook DA, Coory M, Webster RA, BMJ Qual Saf 2011 20 469 474 10.1136/bmjqs.2008.031831 21209145
26. Decomposition of economic time series. Shiskin J, Science 1958 128 1539 1546 10.1126/science.128.3338.1539 17831815
27. R Development Core Team, R: A Language and Environment for Statistical Computing Vienna, Austria: R Foundation for Statistical Computing 2012 [ http://www.R-project.org ]
28. Stoffer D, Applied Statistical Time Series Analysis ("astsa"): R package (V 1.1) [ http://www.stat.pitt.edu/stoffer/tsa3/ ], Accessed 12th August 2012 23785448
29. Cumulative sum and exponentially weighted moving averaged control charts. Montgomery DC, Introduction ot Statistical Quality Control Hoboken, NJ: Wiley, Montgomery DC, 7 2013 413 447
30. StataCorp, Time Series Manual: Release 12 College Station, TX: StataCorp LP 2011
31. Comparisons of some exponentially weighted moving average control charts for monitoring the process mean and variance. Reynolds MR, Stoumbos ZG, Technometrics 2006 48 550 567 10.1198/004017006000000255 (Pubitemid 44865396)
32. Methods and Philosophy of Statistical Process Control. Montgomery DC, Introduction ot Statistical Quality Control Hoboken, NJ: Wiley, Montgomery DC, 7 2013 187 233
33. Statistical control for autocorrelated data. Zhang NF, Proc Soc Photo Opt Instrum Eng 1999 3742 65 70
34. StatPoint Technologies Inc, STATGRAPHICS Centurion XVI.1 2012 Warrenton, Virginia USA
35. Performance of control charts for autoregressive conditional heteroscedastic processes. Fang Y, Zhang J, J Appl Stat 1999 26 701 714 10.1080/02664769922142
36. Control charts for GARCH processes. Schipper S, Schmid W, Nonlinear Anal-Theor 2001 47 2049 2060 10.1016/S0362-546X(01)00332-7 (Pubitemid 32733088)
37. ARIMA models. Shumway RH, Stoffer DS, Time Series Analaysis and Its Applications With R Examples. third edition New York, NY: Springer Science+Business Media, LLC, Shumway RH, Stoffer DS, 2011 83 172
38. Estimating autocorrelations in the presence of deterministic trends. Wang S-H, Hafner C, J Time Ser Econom 2011 3 Article4; http://www.degruyter.com/ view/j/jtse.2011.3.2/jtse.2011.3.2.1022/jtse.2011.3.2.1022.xml
39. Trends and random walks in macroeconmic time series: some evidence and implications. Nelson CR, Plosser CR, J Monet Econ 1982 10 139 162 10.1016/0304-3932(82)90012-5
40. Trend and autocorrelation. Pierce DA, Commun Stat 1975 4 163 175
41. Additional time domain topics. Shumway RH, Stoffer DS, Time Series Analaysis and Its Applications With R Examples. third edition New York, NY: Springer Science+Business Media, LLC, Shumway RH, Stoffer DS, 2011 267 318
42. Asset volatility and Volatility models. Tsay RS, An Introduction to Analysis of Financial Data with R Hoboken, NJ: Wiley, Tsay RS, 2013 176 241
43. Karlsson S, ARMADIAG: Stata module to compute post-estimation residual diagnostics for time series 2009 [ http://econpapers.repec.org/scripts/search. asp?ft=armadiag ], Accessed May 2009
44. GARCH 101: the use of ARCH/GARCH models in applied econometrics. Engle R, J Econ Perspect 2001 15 157 168 10.1257/jep.15.4.157 (Pubitemid 33594781)
45. Time-series modeling for statistical process-control. Alwan LC, Roberts HV, J Bus Econ Stat 1988 6 87 95
46. EWMA control charts for autoregressive processes. Koehler AB, Marks NB, O'Connell RT, J Oper Res Soc 2001 52 699 707 10.1057/palgrave.jors.2601140 (Pubitemid 32609074)
47. Tsay RS, An Introduction to Analysis of Financial Data with R John Wiley & Sons, Inc: Hoboken, NJ 2013
48. AIC and BIC: comparisons of assumptions and performance. Kuha J, Sociol Method Res 2004 33 188 229 10.1177/0049124103262065 (Pubitemid 39519122)
49. tssmooth exponential -Single-exponential smoothing. StataCorp, Time Series Manual: Release 12 College Station, TX 2011 477 484
50. The problem of misplaced control limits. Alwan LC, Roberts HV, Appl Stat-J Roy St C 1995 44 269 278 10.2307/2986036
51. Quality quandaries: the effect of autocorrelation on statistical process procedures. Bisgaard S, Kulahci M, Qual Eng 2005 17 481 489 10.1081/QEN- 200068575 (Pubitemid 41009599)
52. Rethinking statistics for quality control. Box G, Narasimhan S, Qual Eng 2010 22 60 72 10.1080/08982110903510297
53. Robustness comparison of exponentially weighted moving-average charts on autocorrelated data and on residuals. Apley DW, Lee HC, J Qual Technol 2008 40 428 447
54. EWMA control charts for monitoring the mean of autocorrelated processes. Lu CW, Reynolds MR, J Qual Technol 1999 31 166 188
55. Control charts for monitoring processes with autocorrelated data. Reynolds MR, Lu CW, Nonlinear Anal-Theor 1997 30 4059 4067 10.1016/S0362- 546X(97)00011-4
56. Robustness of the EWMA control chart for individual observations. Human SW, Kritzinger P, Chakraborti S, J Appl Stat 2011 38 2071 2087 10.1080/02664763.2010.545114
57. Modification of control chart limits in the presence of data correlation. Vasilopoulis AV, Stamboulis AP, J Qual Technol 1978 10 20 30
58. Applications of statistical quality control to cardiac surgery. Shahian DM, Williamson WA, Svensson LG, Restuccia JD, DAgostino RS, Ann Thorac Surg 1996 62 1351 1358 10.1016/0003-4975(96)00796-5 8893568 (Pubitemid 26383530)
59. Monitoring patients using control charts: a systematic review. Tennant R, Mohammed MA, Coleman JJ, Martin U, Int J Qual Health Care 2007 19 187 194 10.1093/intqhc/mzm015 17545672 (Pubitemid 47248941)
60. Kirchgassner G, Wolters J, Introduction to Modern Times Series Analysis Berlin: Springer 2008
61. Parameter estimation in first-order autoregressive model for statistical process monitoring in the presence of data autocorrelation. Lwin T, J Stat Plan Infer 2011 141 2556 2575 10.1016/j.jspi.2011.02.005
62. Making control charts more effective by time series analysis: three illustrative applications. Roberts HV, Tsay RS, Commun Stat-Theory 1996 25 2767 2796 10.1080/03610929608831868 (Pubitemid 126710988)
63. Assignable causes and auto correlation: control charts for observations or residuals? Runger GC, J Qual Technol 2002 34 165 170
64. Time series control charts in the presence of model uncertainty. Apley DW, J Manuf Sci E-T Asme 2002 124 891 898 10.1115/1.1510520 (Pubitemid 35384308)
65. Improved design of robust exponentially weighted moving average control charts for autocorrelated processes. Lee HC, Apley DW, Qual Reliab Engng Int 2011 27 337 352 10.1002/qre.1126
66. A statistical control chart for stationary process data. Zhang NF, Technometrics 1998 40 24 38 10.1080/00401706.1998.10485479 (Pubitemid 128619911)
67. Patients as partners in clinical research-a proposal for applying quality improvement methods to patient care. Solodky C, Chen HG, Jones PK, Katcher W, Neuhauser D, Med Care 1998 36 S13 AS20 10.1097/00005650-199808001-00003 9708579
68. Review of the application of risk-adjusted charts to analyse mortality outcomes in critical care. Cook DA, Duke G, Hart GK, Pilcher D, Mullany D, Crit Care Resusc 2008 10 239 251 18798724
69. Risk-adjusted continuous outcome monitoring with an EWMA chart: could it have detected excess mortality among intensive care patients at Bundaberg Base Hospital? Pilcher DV, Hoffman T, Thomas C, Ernest D, Hart GK, Crit Care Resusc 2010 12 36 41 20196712
70. Automatic time series forecasting: the forecast package for R. Hyndman RJ, Khandakar Y, J Stat Softw 2008 27 1 22
71. Predicting the false alarm rate in multi-institution mortality monitoring. Bottle A, Aylin P, J Oper Res Soc 2011 62 1711 1718 10.1057/jors.2010.121
72. A randomized controlled trial of league tables and control charts as aids to health service decision-making. Marshall T, Mohammed MA, Rouse A, Int J Qual Health Care 2004 16 309 315 10.1093/intqhc/mzh054 15252005 (Pubitemid 39157542)
73. Multivariate GARCH models: a survey. Bauwens L, Laurent S, Rombouts JV, J Appl Econ 2006 21 79 109 10.1002/jae.842 (Pubitemid 43351230)
74. Why and how to use vector autoregressive models for quality control: the guideline and procedures. Pan X, Jarrett JE, Qual Quant 2012 46 935 948 10.1007/s11135-011-9437-x
75. The vector innovations structural time series framework: a simple approach to multivariate forecasting. de Silva A, Hyndman RJ, Snyder R, Stat Model 2010 10 353 374 10.1177/1471082X0901000401
76. Optimal combination forecasts for hierarchical time series. Hyndman RJ, Ahmed RA, Athanasopoulos G, Shang HL, Comput Stat Data An 2011 55 2579 2589 10.1016/j.csda.2011.03.006