Abstract
Mortality is the most frequently modeled outcome in injury research. It is easy to recognize, relatively free from measurement error, and fundamentally interesting. Injury researchers in public health or clinical medicine have become familiar with logistic regression as a standard way to model a binary outcome like mortality (or alternatively survival). Many other outcomes encountered in injury research can also be considered binary, such as the occurrence of a serious complication or an extended length of stay in hospital.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adewale, A. J., Hayduk, L., Estabrooks, C. A., et al. (2007). Understanding hierarchical linear models: Applications in nursing research. Nursing Research, 56, S40–6.
Antretter, E., Dunkel, D., Osvath, P., et al. (2006). Multilevel modeling was a convenient alternative to common regression designs in longitudinal suicide research. Journal of Clinical Epidemiology, 59, 576–86.
Austin, P. C., Tu, J. V., & Alter, D. A. (2003). Comparing hierarchical modeling with traditional logistic regression analysis among patients hospitalized with acute myocardial infarction: Should we be analyzing cardiovascular outcomes data differently? American Heart Journal, 145, 27–35.
Borrell, C., Rodriguez, M., Ferrando, J., et al. (2002). Role of individual and contextual effects in injury mortality: New evidence from small area analysis. Injury Prevention, 8, 297–302.
Browne, W. J., & Draper, D. (2006). A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis, 1, 473–514.
Centre for multilevel modelling. www.bristol.ac.uk/cmm. Accessed 1 Mar 2011.
Champion, H. R., Copes, W. S., Sacco, W. J., et al. (1990). The Major Trauma Outcome Study: Establishing national norms for trauma care. Journal of Trauma, 30, 1356–65.
Chan, C. K., Feinstein, A. R., Jekel, J. F., & Wells, C. K. (1988). The value and hazards of standardization in clinical epidemiologic research. Journal of Clinical Epidemiology, 41, 1125–34.
Clark, D. E., Hannan, E. L., & Wu, C. (2010a). Predicting risk-adjusted mortality for trauma patients: Logistic versus multilevel logistic models. Journal of the American College of Surgeons, 211, 224–31.
Clark, D. E., Hannan, E. L., & Raudenbush, S. W. (2010b). Using a hierarchical model to estimate risk-adjusted mortality for hospitals not included in the reference sample. Health Services Research, 45, 577–87.
Clark, D. E., Lucas, F. L., & Ryan, L. M. (2007). Predicting hospital mortality, length of stay, and transfer to long-term care for injured patients. Journal of Trauma, 62, 592–600.
Cohen, M. E., Dimick, J. B., Bilimoria, K. Y., et al. (2009). Risk adjustment in the American College of Surgeons National Surgical Quality Improvement Program: A comparison of logistic versus hierarchical modeling. Journal of the American College of Surgeons, 209, 687–93.
Cook, T. D., & DeMets, D. L. (2008). Introduction to statistical methods for clinical trials. Boca Raton, FL: Chapman and Hall.
DeLong, E. R., Peterson, E. D., DeLong, D. M., et al. (1997). Comparing risk-adjustment methods for provider profiling. Statistics in Medicine, 16, 2645–64.
Diez-Roux, A. V. (2000). Multilevel analysis in public health research. Annual Review of Public Health, 21, 171–92.
Dimick, J. B., & Welch, H. G. (2008). The zero mortality paradox in surgery. Journal of the American College of Surgeons, 206, 13–6.
Flora, J. D., Jr. (1978). A method for comparing survival of burn patients to a standard survival curve. Journal of Trauma, 18, 701–5.
Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press.
Glance, L. G., Osler, T. M., Dick, A. W., et al. (2010). The survival measurement and reporting trial for trauma (SMARTT): Background and study design. Journal of Trauma, 68, 1491–7.
Goldstein, H. (2003). Multilevel statistical models. London: Hodder Arnold.
Goldstein, H., Browne, W., & Rasbash, J. (2002). Multilevel modelling of medical data. Statistics in Medicine, 21, 3291–315.
Gorra, A. S., Clark, D. E., Mullins, R. J., & Delorenzo, M. A. (2008). Regional variation in hospital mortality and 30-day mortality for injured Medicare patients. World Journal of Surgery, 32, 954–9.
Hannan, E. L., Wu, C., DeLong, E. R., & Raudenbush, S. W. (2005). Predicting risk-adjusted mortality for CABG surgery: Logistic versus hierarchical logistic models. Med Care, 43, 726–35.
Hemmila, M. R., Nathens, A. B., Shafi, S., et al. (2010). The Trauma Quality Improvement Program: Pilot study and initial demonstration of feasibility. Journal of Trauma, 68, 253–62.
Jones, A. P., & Jorgensen, S. H. (2003). The use of multilevel models for the prediction of road accident outcomes. Accident Analysis and Prevention, 35, 59–69.
Jones, J. M., Redmond, A. D., & Templeton, J. (1995). Uses and abuses of statistical models for evaluating trauma care. Journal of Trauma, 38, 89–93.
Kim, D. G., Lee, Y., Washington, S., & Choi, K. (2007). Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models. Accident Analysis and Prevention, 39, 125–34.
Kirkham, J. J., & Bouamra, O. (2008). The use of statistical process control for monitoring institutional performance in trauma care. Journal of Trauma, 65, 1494–501.
Krumholz, H. M., Brindis, R. G., Brush, J. E., et al. (2006). Standards for statistical models used for public reporting of health outcomes. Circulation, 113, 456–62.
Lenguerrand, E., Martin, J. L., & Laumon, B. (2006). Modelling the hierarchical structure of road crash data – application to severity analysis. Accident Analysis and Prevention, 38, 43–53.
Lipsky, A. M., Gausche-Hill, M., Vienna, M., & Lewis, R. J. (2010). The importance of “shrinkage” in subgroup analyses. Annals of Emergency Medicine, 55(544–52), e3.
MacNab, Y. C. (2003). A Bayesian hierarchical model for accident and injury surveillance. Accident Analysis and Prevention, 35, 91–102.
Milton, J. C., Shankar, V. N., & Mannering, F. L. (2008). Highway accident severities and the mixed logit model: An exploratory empirical analysis. Accident Analysis and Prevention, 40, 260–6.
Moore, L., Hanley, J. A., Turgeon, A. F., et al. (2009a). A multiple imputation model for imputing missing physiologic data in the National Trauma Data Bank. Journal of the American College of Surgeons, 209, 572–9.
Moore, L., Lavoie, A., Turgeon, A. F., et al. (2009b). The trauma risk adjustment model: A new model for evaluating trauma care. Annals of Surgery, 249, 1040–6.
Moore, L., Hanley, J. A., Turgeon, A. F., & Lavoie, A. (2010a). Evaluating the performance of trauma centers: Hierarchical modeling should be used. Journal of Trauma, 69, 1132–7.
Moore, L., Hanley, J. A., Turgeon, A. F., et al. (2010b). A new method for evaluating trauma centre outcome performance: TRAM-adjusted mortality estimates. Annals of Surgery, 251, 952–8.
Mukamel, D. B., Glance, L. G., Dick, A. W., & Osler, T. M. (2010). Measuring quality for public reporting of health provider quality: Making it meaningful to patients. American Journal of Public Health, 100, 264–9.
Normand, S.-L. T., Glickman, M. E., & Gatsonis, C. A. (1997). Statistical methods for profiling providers of medical care: Issues and applications. Journal of the American Statistical Association, 92, 803–14.
Normand, S.-L. T., & Shahian, D. M. (2007). Statistical and clinical aspects of hospital outcomes profiling. Statistical Science, 22, 206–26.
O’Connell, A. A., & McCoach, D. B. (2004). Applications of hierarchical linear models for evaluations of health interventions: Demystifying the methods and interpretations of multilevel models. Evaluation and The Health Professions, 27, 119–51.
O’Hagan, A. (1994). Kendall’s advanced theory of statistics, volume 2B: Bayesian inference. New York: Halsted Press.
Pollock, D. A. (1999). Summary of the discussion: Trauma registry data and TRISS evidence. Journal of Trauma, 47, S56–S8.
Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and longitudinal modeling using Stata. College Station, TX: Stata Press.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models. London: Sage.
Rothman, K. J. (1986). Modern epidemiology. Boston: Little, Brown and Co.
Shahian, D. M., Normand, S.-L. T., Torchiana, D. F., et al. (2001). Cardiac surgery report cards: Comprehensive review and statistical critique. The Annals of Thoracic Surgery, 72, 2155–68.
Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis. London: Sage.
Spiegelhalter, D. J. (2005). Funnel plots for comparing institutional performance. Statistics in Medicine, 24, 1185–202.
The BUGS project. www.mrc-bsu.cam.ac.uk/bugs Accessed 1 Mar 2011.
Ulm, K. (1990). A simple method to calculate the confidence interval of a standardized mortality ratio (SMR). American Journal of Epidemiology, 131, 373–5.
Yannis, G., Papadimitriou, E., & Antoniou, C. (2007). Multilevel modelling for the regional effect of enforcement on road accidents. Accident Analysis and Prevention, 39, 818–25.
Acknowledgments
Supported in part by Grant R21HD061318 (PI Clark) from the National Institutes of Health, Grants R01HS015656 (PI Clark) and R01HS017718 (PI Shafi) from the Agency for Healthcare Research and Quality, a grant from the Maine Medical Center Research Strategic Plan (PI Clark), and a research award from the Canadian Institutes of Health Research (PI Moore).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Additional information
Disclaimers
Content reproduced from the National Trauma Data Bank remains the full and exclusive copyrighted property of the American College of Surgeons. The American College of Surgeons is not responsible for any claims arising from works based on the original data, text, tables, or figures.
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Clark, D.E., Moore, L. (2012). Multilevel Modeling. In: Li, G., Baker, S. (eds) Injury Research. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1599-2_23
Download citation
DOI: https://doi.org/10.1007/978-1-4614-1599-2_23
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4614-1598-5
Online ISBN: 978-1-4614-1599-2
eBook Packages: MedicineMedicine (R0)