Accounting for regression-to-the-mean in tests for recent changes in institutional performance: Analysis and power

Statistics in Medicine

Volumn 28, Issue 12, 2009, Pages 1645-1667

  Jones, Hayley E ^a   Spiegelhalter, David J ^a  

^a MRC BIOSTATISTICS UNIT   (United Kingdom)

Author keywords

Clostridium difficile; Individual changes; Mortality rates; Provider profiling; Regression to the mean; Teenage conceptions

Indexed keywords

ADOLESCENT PREGNANCY; ARTICLE; CLOSTRIDIUM DIFFICILE INFECTION; HEALTH CARE MANAGEMENT; HEALTH CARE PERSONNEL; HEART SURGERY; MORTALITY; NATIONAL HEALTH SERVICE; PERFORMANCE MEASUREMENT SYSTEM; PRACTICE GUIDELINE; REGRESSION ANALYSIS; REGRESSION TO THE MEAN; RISK ASSESSMENT; UNITED STATES;

ADOLESCENT; BIOMETRY; CLOSTRIDIUM DIFFICILE; CORONARY ARTERY BYPASS; CROSS INFECTION; ENTEROCOLITIS, PSEUDOMEMBRANOUS; FEMALE; GREAT BRITAIN; HEALTH PERSONNEL; HUMANS; MODELS, STATISTICAL; NEW YORK; PREGNANCY; PREGNANCY IN ADOLESCENCE; REGRESSION ANALYSIS;

EID: 69949085920     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3583     Document Type: Article

References (38)

1. Healthcare Commission. Assessing existing and new national targets, 2007. Available from: http://ratings2007.healthcarecommission.org.uk/ (accessed November 2008).
2. Galton F. Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute 1886; 15:246-263.
3. Morton V, Torgerson DJ. Effect of regression to the mean on decision making in health care. British Medical Journal 2003; 326:1083-1084.
4. Chuang-Stein C, Tong DM. The impact and implication of regression to the mean on the design and analysis of medical investigations. Statistical Methods in Medical Research 1997; 6:115-128.
5. Senn SJ, Collie GS. Accident blackspots and the bivariate negative binomial. Traffic Engineering and Control 1988; 29:168-169.
6. Trimble HC, Cronbach LJ. A practical procedure for the rigorous interpretation of test-retest scores in terms of pupil growth. Journal of Educational Research 1943; 36:481-488.
7. McSweeny AJ, Naugle RI, Chelune GJ, Lüders H. T scores for change: an illustration of a regression approach to depicting change in clinical neuropsychology. The Clinical Neuropsychologist 1993; 7(3):300-312.
8. Sawrie SM, Marson DC, Boothe AL, Harrell LE. A method for assessing clinically relevant individual cognitive change in older adult populations. Journal of Gerontology: Psychological Sciences 1999; 54:116-124.
9. Burgess JF, Christiansen CL, Michalak SE, Morris CN. Medical profiling: improving standards and risk adjustment using hierarchical models. Journal of Health Economics 2000; 19:291-309.
10. Morris CN, Christiansen CL. Hierarchical models for ranking and for identifying extremes, with applications. In Bayesian Statistics 5, Bernardo JM, Berger JO, Dawid AP, Smith AFM (eds). Oxford University Press: Oxford, 1996; 277-296.
11. Senn S. Regression: a new mode for an old meaning? The American Statistician 1990; 44(2):181-183.
12. Chesher A. Non-normal variation and regression to the mean. Statistical Methods in Medical Research 1997; 6:147-166.
13. Cronbach LJ, Furby L. How we should measure 'change'-or should we? Psychological Bulletin 1970; 74(1): 68-80.
14. Nunnally J, Bernstein I. Psychometric Theory (3rd edn). McGraw-Hill: New York, 1994.
15. Galton M (eds). Progress and Performance in the Primary Classroom Routledge and Kegan Paul: London, 1980.
16. Youngman MB. Analysing Social and Educational Research Data. McGraw-Hill: London, 1979.
17. The Fibrinogen Studies Collaboration. Regression dilution methods for meta-analysis: assessing long-term variability in plasma fibrinogen among 27,247 adults in 15 prospective studies. International Journal of Epidemiology 2006; 35:1570-1578.
18. Jacobson NS, Truax P. Clinical significance: a statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology 1991; 59(1):12-19.
19. Temkin NR, Heaton RK, Grant I, Dikmen SS. Detecting significant change in neuropsychological test performance: a comparison of four models. Journal of the International Neuropsychological Society 1999; 5:357-369.
20. DerSimonian R, Kacker R. Random-effects model for meta-analysis of clinical trials: an update. Contemporary Clinical Trials 2007; 28:105-114.
21. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 1995; 57:289-300.
22. Jones HE, Ohlssen DI, Spiegelhalter DJ. Use of the false discovery rate when comparing multiple healthcare providers. Journal of Clinical Epidemiology 2008; 61(3):232-240.
23. Storey JD. The positive false discovery rate: a Bayesian interpretation and the q-value. Annals of Statistics 2003; 6:2013-2035.
24. Unicef. A league table of teenage births in rich nations. Technical Report, Innocenti Research Centre, Florence, Italy, 2001. Available from: www.unicef-irc.org/publications/pdf/repcard3e.pdf (accessed November 2008).
25. Bradshaw J. Teenage births. Technical Report, Joseph Rowntree Foundation, York, U.K., 2006. Available from: www.jrf.org.uk/bookshop/ eBooks/9781859355046.pdf (accessed November 2008).
26. Office for National Statistics. Teenage pregnancy: statistics, 2007. Available from: www.everychildmatters.gov.uk/health/teenagepregnancy/ statistics/ (accessed November 2008).
27. Teenage Pregnancy Unit. Under 18 conception rates for top-tier local authorities 1998 and 2004. Available from: www.dfes.gov.uk/pns/pnattach/ 20060127/1.htm (accessed November 2008).
28. Wilkinson P, French R, Kane R, Lachowycz K, Stephenson J, Grundy C, Jacklin P, Kingori P, Stevens M, Wellings K. Teenage conceptions, abortions, and births in England, 1994-2003, and the national teenage pregnancy strategy. The Lancet 2006; 368:1879-1886.
29. Diamond I, Clements S, Stone N, Ingham R. Spatial variation in teenage conceptions in South and West England. Journal of the Royal Statistical Society, Series A 1999; 162:273-289.
30. Spiegelhalter DJ. Funnel plots for comparing institutional performance. Statistics in Medicine 2005; 24:1185-1202.
31. Health Protection Agency. Clostridium difficile: frequently asked questions, 2007. Available from: www.hpa.org.uk/infections/topicsaz/ clostridium difficile/C diff faqs.htm (accessed November 2008).
32. Health Protection Agency. Quarterly counts of C. difficile, 2007. Available from: www.hpa.org.uk/infections/topicsaz/hai/Mandatory Results.htm (accessed November 2008).
33. Shek FW, Stacey BSF, Rendell J, Hellier MD, Hanson PJV. The rise of Clostridium difficile: the effect of length of stay, patient age and antibiotic use. Journal of Hospital Infection 2000; 45:235-237.
34. New York State Department of Health. Cardiovascular disease data and statistics, 2007. Available from: www.health.state.ny.us/statistics/ diseases/cardiovascular/ (accessed November 2008).
35. Bronskill SE, Normand SLT, Landrum MB, Rosenheck RA. Longitudinal profiles of health care providers. Statistics in Medicine 2002; 21:1067-1088.
36. Aguilar O, West M. Analysis of hospital quality monitors using hierarchical time series models. In Case Studies in Bayesian Statistics, Gatsonis C, Kass RE, Carlin B, Carriquiry A, Gelman A, Verdinelli I, West M (ed.)., vol. 4. Springer: New York, 1998; 287-302.
37. Lin R, Louis TA, Paddock SM, Ridgeway G. Ranking USRDS provider-specific SMRs from 1998-2001. Technical Report, Department of Biostatistics, Johns Hopkins University, December 2004. Available from: www.bepress.com/jhubiostat/paper67 (accessed November 2008).
38. Böhning D, Sarol J, Rattanasiri S, Viwatwongkasem C, Biggeri A. A comparison of non-iterative and iterative estimators of heterogeneity variance for the standardized mortality ratio. Biostatistics 2004; 5(1):61-74.