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An Introduction to Time-Trend Analysis

Published online by Cambridge University Press:  02 January 2015

David Birnbaum ,
John W. Ely ,
Jeffrey D. Dawson ,
Jon H. Lemke  and
Jon Rosenberg
John W. Ely*
Affiliation:
Department of Family Practice, University of Iowa Hospitals and Clinics, Iowa City, Iowa
Jeffrey D. Dawson
Affiliation:
Department of Preventive Medicine and Environmental Health, University of Iowa College of Medicine, Iowa City, Iowa
Jon H. Lemke
Affiliation:
Department of Preventive Medicine and Environmental Health, University of Iowa College of Medicine, Iowa City, Iowa
Jon Rosenberg
Affiliation:
Division of Communicable Disease Control, California Department of Health Services, Berkeley, California
*
Department of Family Practice, University of Iowa Hospitals and Clinics, 2109 Steindler Bldg, Iowa City, IA 52242
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Abstract

Healthcare professionals often are presented with data that appear to indicate an upward or downward trend over time. For example, admissions of acquired immunodeficiency syndrome (AIDS) patients appear to be increasing, cesarean section rates appear to be decreasing, or nosocomial pneumonia rates appear to be increasing. Critical decisions sometimes are based on such trends, which often are presented without a statistical analysis. Those responsible for decision making may be left wondering whether these apparent trends represent only chance variation. Graphs showing trends over time generally present one of three kinds of outcome data: counts (eg, three AIDS admissions), proportions (eg, 10 cesarean sections per 100 total deliveries), or person-time data (eg, 13 cases of nosocomial pneumonia per 10,000 patient days). Using familiar examples and a minimum of technical language, we illustrate the analysis of time trends.

Information

Type
Statistics for Hospital Epidemiology
Information
Infection Control & Hospital Epidemiology , Volume 18 , Issue 4 , April 1997 , pp. 267 - 274
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1997

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References

1. Kleinbaum, DG, Kupper, LL, Muller, KE. Applied Regression Analysis and Other Multivariable Methods. 2nd ed. Belmont, CA: Duxbury Press; 1988.Google Scholar
2. Freeman, J, McGowan, JEJ. Methodologic issues in hospital epidemiology, I: rates, case finding, and interpretation. Rev Infect Dis 1981;3:658–667.CrossRefGoogle ScholarPubMed
3. Sellick, JA Jr. The use of statistical process control charts in hospital epidemiology. Infect Control Hosp Epidemiol 1993;14:649–656.Google Scholar
4. Birnbaum, D. Analysis of hospital infection surveillance data. Infect Control 1984;5:332–338.Google Scholar
5. Childress, JA, Childress, JD. Statistical test for possible infection outbreaks. Infect Control 1981;2:247–249.CrossRefGoogle ScholarPubMed
6. Centers for Disease Control. Guidelines for investigating clusters of health events. MMWR 1990;39(RR-11):1–23.Google Scholar
7. Jacquez, GM, Waller, LA, Grimson, R, Wartenberg, D. The analysis of disease clusters, part I: state of the art. Infect Control Hosp Epidemiol 1996;17:319–327.CrossRefGoogle ScholarPubMed
8. Stroup, DF, Thacker, SB, Herndon, JL. Application of multiple time series analysis to the estimation of pneumonia and influenza mortality. Stat Med 1988;7:1045–1059.CrossRefGoogle Scholar
9. Kuhn, L, Davidson, LL, Durkin, MS. Use of Poisson regression and time series analysis for detecting changes over time in rates of child injury following a prevention program. Am J Epidemiol 1994;140:943–955.Google Scholar
10. Tiao, GC. An introduction to multiple time series analysis. Med Care 1993;31(suppl 5):YS71–YS74.Google Scholar
11. Stroup, DF, Williamson, DF, Dean, AD, Haddad, S, Basha, M, Rapose, W. Statistical Software for Public Health Surveillance. Atlanta, GA: Centers for Disease Control and Prevention; 1994.Google Scholar
12. Cleveland, WS. Visualizing Data. Summit, NJ: Hobart Press; 1993.Google Scholar
13. Rosner, B. Fundamentals of Biostatistics. 4th ed. Belmont, CA: Duxbury Press; 1995.Google Scholar
14. Daniels, HE. Rank correlation and population models. J R Stat Soc Series B 1950;12:171–181.Google Scholar
15. Mann, HB. Non-parametric tests against trend. Econometrica 1945;113:245–259.CrossRefGoogle Scholar
16. Conover, WJ. Practical Nonparametric Statistics. 2nd ed. New York, NY: John Wiley & Sons; 1980.Google Scholar
17. Kachigan, SK. Statistical Analysis: An Interdisciplinary Introduction to Univariate and Multivariate Methods. New York, NY: Radius Press; 1986.Google Scholar
18. Frome, EL, Kutner, MH, Beauchamp, JJ. Regression analysis of Poisson-distributed data. J Am Stat Assoc 1973;68:935–940.CrossRefGoogle Scholar
19. Frome, EL. The analysis of rates using Poisson regression models. Biometrics 1983;39:665–674.CrossRefGoogle ScholarPubMed
20. Frome, EL, Checkoway, H. Use of Poisson regression models in estimating incidence rates and ratios. Am J Epidemiol 1985;121:309–323.Google Scholar
21. McCullagh, P, Nelder, JA. Generalized Linear Models. 2nd ed. London, England: Chapman Hall; 1989.CrossRefGoogle Scholar
22. Draper, N, Smith, H. Applied Regression Analysis. 2nd ed. New York, NY: John Wiley & Sons; 1981.Google Scholar
23. Campbell, MJ, Machin, D, D'Arcangues, C. Coping with extra-Poisson variability in the analysis of factors influencing vaginal ring expulsions. Stat Med 1991;10:241–254.Google Scholar
24. Bartko, JJ. The negative binomial distribution: a review of properties and applications. Va J Science 1961;12:18–37.Google Scholar
25. Fisher, RA. The negative binomial distribution. Ann Eugenics 1941;11:182–187.Google Scholar
26. Gallus, G, Radaelli, G, Marchi, M. Poisson approximation to a negative binomial process in the surveillance of rare health events. Methods Inf Med 1991;30:206–209.Google Scholar
27. Kianifard, F, Gallo, PP. Poisson regression analysis in clinical research. J Biopharm Stat 1995;5:115–129.Google Scholar
28. Lawless, JF. Negative binomial and mixed Poisson regression. Can J Stat 1987;15:209.CrossRefGoogle Scholar
29. Rumke, CL. How long is the wait for an uncommon event? J Chronic Dis 1982;35:561–564.Google Scholar
30. Tripathi, RC. Negative binomial distribution. In: Kotz, S, Johnson, NL, eds. Encyclopedia of Statistical Sciences. Vol 6. New York, NY: John Wiley & Sons; 1985:169–177.Google Scholar
31. Hosmer, DW, Lemeshow, S. Applied Logistic Regression. New York, NY: John Wiley & Sons; 1989.Google Scholar
32. Hosmer, DW, Taber, S, Lemeshow, S. The importance of assessing the fit of logistic regression models: a case study. Am J Public Health 1991;81:1630–1635.CrossRefGoogle ScholarPubMed
33. Ely, JW, Dawson, JD, Mehr, DR, Burns, TL. Understanding logistic regression analysis through example. Fam Med 1996;28:134–140.Google Scholar
34. Berry, G. The analysis of mortality by the subject-years method. Biometrics 1983;39:173–184.CrossRefGoogle ScholarPubMed
35. Fuller, WA. Introduction to Statistical Time Series. New York, NY: John Wiley & Sons; 1989.Google Scholar
36. Kleinbaum, DG, Kupper, LL, Morgenstern, H. Epidemiologic Research: Principles and Quantitative Methods. New York, NY: Van Nostrand Reinhold; 1982:32–34.Google Scholar