Abstract
Reporting transplant center-specific survival rates after hematopoietic cell transplantation is required in the United States. We describe a method to report 1-year survival outcomes by center, as well as to quantify center performance relative to the transplant center network average, which can be reliably used with censored data and for small center sizes. Each center’s observed 1-year survival outcome is compared to a predicted survival outcome adjusted for patient characteristics using a pseudovalue regression technique. A 95% prediction interval for 1-year survival assuming no center effect is computed for each center by bootstrapping the scaled residuals from the regression model, and the observed 1-year survival is compared to this prediction interval to determine center performance. We illustrate the technique using a recent center specific analysis performed by the Center for International Blood and Marrow Transplant Research, and study the performance of this method using simulation.
Similar content being viewed by others
References
Andersen PK, Klein JP, Rosthoj S (2003) Generalized linear models or correlated pseudo-observations with applications to multi-state models. Biometrika 90: 15–7. doi:10.1093/biomet/90.1.15
Austin PC, Alter DA, Tu JV (2003) The use of fixed- and random-effects models for classifying hospitals as mortality outliers: a monte carlo assessment. Med Decis Making 23: 526–39. doi:10.1177/0272989X03258443
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57: 289–00
Christiansen CL, Morris CN (1997) Improving the statistical approach to health care provider profiling. Ann Intern Med 127: 764–68
DeLong ER, Peterson ED, DeLong DM, Muhlbaier LH, Hackett S, Mark DB (1997) Comparing risk-adjustment methods for provider profiling. Stat Med 16: 2645–664 doi:10.1002/(SICI)1097-0258(19971215)16:23<2645::AID-SIM696>3.0.CO;2-D
Dickinson DM, Shearon TH, O’Keefe J, Wong H-H, Berg CL, Rosendale JD et al (2006) SRTR Center-Specific Reporting Tools: Posttransplant Outcomes. Am J Transplant 6: 1198–211. doi:10.1111/j.1600-6143.2006.01275.x
Health Care Financing Administration (1989) Medicare hospital mortality information, 1988. Government Printing Office Washington, DC
Huang I-C, Dominici F, Frangakis C, Diette GB, Damberg CL, Wu AW (2005) Is risk-adjustor selection more important than statistical approach for provider profiling? Asthma as an example. Med Decis Making 25: 20–4. doi:10.1177/0272989X04273138
Iezzoni LI (1994) Risk adjustment for measuring health care outcomes. Health Administration Press, Ann Arbor, MI
Klein JP, Andersen PK (2005) Regression modeling of competing risks data based on pseudo-values of the cumulative incidence function. Biometrics 61: 223–29. doi:10.1111/j.0006-341X.2005.031209.x
Klein JP, Logan BR, Harhoff M, Andersen PK (2007) Analyzing survival curves at a fixed point in time. Stat Med 26: 4505–519. doi:10.1002/sim.2864
Klein JP, Gerster M, Andersen PK, Tarima S, Perme MP (2008) SAS and R functions to compute pseudo-values for censored data regression. Comput Methods Programs Biomed 89: 289–00. doi:10.1016/j.cmpb.2007.11.017
Landon B, Iezzoni L, Ash AS, Schwartz M, Daley J, Hughes JS et al (1996) Judging hospitals by severity adjusted mortality rates: the case of CABG surgery. Inquiry 33: 155–66
Liang K-Y, Zeger SL (1986) Longitudinal data analysis using generalized linear models. Biometrika 73: 13–2
Localio AR, Hamory BH, Sharp TJ, Weaver SL, TenHave TR, Landis JR (1995) Comparing hospital mortality in adult patients with pneumonia: a case study of statistical methods in a managed care program. Ann Intern Med 122: 125–32
Moulton LH, Zeger SL (1991) Bootstrapping generalized linear models. Comput Stat Data Anal 11: 53–3. doi:10.1016/0167-9473(91)90052-4
National Marrow Donor Program (2007) Choosing a transplant center: a patient’s guide. http://www.marrow.org/access. Accessed 19 Feb 2008
New York State Department of Health (1992) Coronary artery bypass graft surgery in New York State 1989–991. New York State Department of Health, Albany
Normand S-LT, Glickman ME, Gatsonis CA (1997) Statistical methods for profiling providers of medical care: issues and applications. JASA 92: 803–14
Pasquini MC, Wang Z, Schneider L (2007) CIBMTR summary slides 2007, Part 1. http://www.cibmtr.org/PUBLICATIONS/Newsletter/DOCS/2007Dec.pdf. Accessed 28 Jan 2008
Salem-Schatz S, Moore G, Rucker M, Pearson S (1994) The case for case-mix adjustment in practice profiling. JAMA 272: 871–74. doi:10.1001/jama.272.11.871
Thomas N, Longford NT, Rolph JE (1994) Empirical Bayes methods for estimating hospital-specific mortality rates. Stat Med 13: 889–03. doi:10.1002/sim.4780130902
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Logan, B.R., Nelson, G.O. & Klein, J.P. Analyzing center specific outcomes in hematopoietic cell transplantation. Lifetime Data Anal 14, 389–404 (2008). https://doi.org/10.1007/s10985-008-9100-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10985-008-9100-6