Abstract
Children receiving a kidney transplant may be followed to identify predictors of mortality. Specifically, is mortality risk lower in recipients of kidneys obtained from a living donor? If so, is this effect explained by the time the transplanted kidney is in transport or how well the donor and recipient match on characteristics that affect immune response? Similarly, HIV-infected subjects may be followed to assess the effects of a new form of therapy on incidence of opportunistic infections. Or patients with liver cirrhosis may be followed to assess whether liver biopsy results predict mortality.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aalen, O. (1989). A linear regression model for the analysis of life times. Statistics in Medicine, 8(8), 907–925.
Aurora, P., Whitehead, B. and Wade, A. (1999). Lung transplantation and life extension in children with cystic fibrosis. Lancet, 354, 1591–1593.
Black, D., Cummings, S., Karpf, D., Cauley, J., Thompson, D., Nevitt, M., Bauer, D., Genant, H., Haskell, W., Marcus, R. et al. (1996b). Randomised trial of effect of alendronate on risk of fracture in women with existing vertebral fractures. The Lancet, 348(9041), 1535–1541.
Buchbinder, S. P., Douglas, J. M., McKirnan, D. J., Judson, F. N., Katz, M. H. and MacQueen, K. M. (1996). Feasibility of human immunodeficiency virus vaccine trials in homosexual men in the United States: risk behavior, seroincidence, and willingness to participate. Journal of Infectious Diseases, 174(5), 954–961.
DeGruttola, V. and Tu, X. M. (1994). Modelling progression of cd4-lymphocyte count and its relationship to survival time. Biometrics, 50, 1003–1014.
Dickson, E. R., Grambsch, P. M. and Fleming, T. R. (1989). Prognosis in primary biliary-cirrhosis - model for decision-making. Hepatology, 10, 1–7.
Fine, J. and Gray, R. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–497.
Gail, M. H., Wieand, S. and Piantodosi, S. (1984). Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika, 71, 431–444.
Glidden, D. V. and Vittinghoff, E. (2004). Modelling clustered survival data from multicentre clinical trials. Statistics in Medicine, 23, 369–388.
Henderson, R. and Oman, P. (1999). Effect of frailty on marginal regression estimates in survival analysis. Journal of the Royal Statistical Society, Series B, Methodological, 61, 367–379.
Hsieh, F. Y., Bloch, D. A. and Larsen, M. D. (1998). A simple method of sample size calculation for linear and logistic regression. Statistics in Medicine, 17, 541–557.
Hsieh, F. Y. and Lavori, P. W. (2000). Sample-size calculations for the Cox proportional hazards regression model with nonbinary covariates. Controlled Clinical Trials, 21, 552–560.
Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. John Wiley & Sons, New York.
Klein, J. P. and Moeschberger, M. L. (1997). Survival Analysis: Techniques for Censored and Truncated Data. Springer, New York.
Marubini, E. and Valsecchi, M. G. (1995). Analysing Survival Data from Clinical Trials and Observational Studies. John Wiley & Sons, New York, Chichester.
Miller, R. G., Gong, G. and Munoz, A. (1981). Survival Analysis. John Wiley & Sons, New York, Chichester.
Orwoll, E., Blank, J., Barrett-Connor, E., Cauley, J., Cummings, S., Ensrud, K., Lewis, C., Cawthon, P., Marcus, R., Marshall, L. et al. (2005). Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study–a large observational study of the determinants of fracture in older men. Contemporary Clinical Trials, 26(5), 569–585.
Rosenman, R. H., Friedman, M., Straus, R., Wurm, M., Kositchek, R., Hahn, W. and Werthessen, N. T. (1964). A predictive study of coronary heart disease: the western collaborative group study. Journal of the American Medical Association, 189, 113–120.
Schmoor, C., Sauerbrei, W. and Schumacher, M. (2000). Sample size considerations for the evaluation of prognostic factors in survival analysis. Statistics in Medicine, 19, 441–452.
Schmoor, C. and Schumacher, M. (1997). Effects of covariate omission and categorization when analysing randomized trials with the Cox model. Statistics in Medicine, 16, 225–237.
Schoenfeld, D. (1980). Chi-squared goodness-of-fit tests for the proportional hazards regression model. Biometrika, 67, 145–153.
Self, S. and Pawitan, Y. (1992). Modeling a marker of disease progression and onset of disease. In: AIDS Epidemiology: Methodlogical Issues (edited by N. Jewell, K. Dietz and V. Farewell). Birkhauser, Boston.
Suissa, S. (2008). Immortal time bias in pharmacoepidemiology. American Journal of Epidemiology, 167(4), 492–499.
Therneau, T. M. and Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer, New York.
Tsiatis, A. A. and Davidian, M. (2004). Joint modeling of longitudinal and time-to-event data. Statistica Sinica, 14(3), 809–834.
Vittinghoff, E. and McCulloch, C. E. (2007). Relaxing the rule of ten events per variable in logistic and Cox regression. American Journal of Epidemiology, 165, 710–718.
Volberding, P. A., Lagakos, S. W. and Koch, M. A. (1990). Zidovudine in asymptomatic human-immunodeficiency-virus infection – a controlled trial in persons with fewer than 500 cd4-positive cells per cubic millimeter. The New England Journal of Medicine, 322(14), 941–949.
Wei, L. (1992). The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis. Statistics in Medicine, 11(14-15), 1871–1879.
Wei, L. J. and Glidden, D. V. (1997). An overview of statistical methods for multiple failure time data in clinical trials (with discussion). Statistics in Medicine, 16(8), 833–839.
Wulfsohn, M. S. and Tsiatis, A. A. (1997). A joint model for survival and longitudinal data measured with error. Biometrics, 53, 330–339.
Bernardo, M. V. P., Lipsitz, S. R., Harrington, D. P. and Catalano, P. J. (2000). Sample size calculations for failure time random variables in non-randomized studies. Journal of the Royal Statistical Society (Series D): The Statistician, 49, 31–40.
Concato, J., Peduzzi, P. and Holfold, T. R. (1995). Importance of events per independent variable in proportional hazards analysis i. background, goals, and general strategy. Journal of Clinical Epidemiology, 48, 1495–1501.
Peduzzi, P., Concato, J. and Feinstein, A. R. (1995). Importance of events per independent variable in proportional hazards regression analysis ii. accuracy and precision of regression estimates. Journal of Clinical Epidemiology, 48, 1503–1510.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Vittinghoff, E., Glidden, D.V., Shiboski, S.C., McCulloch, C.E. (2012). Survival Analysis. In: Regression Methods in Biostatistics. Statistics for Biology and Health. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1353-0_6
Download citation
DOI: https://doi.org/10.1007/978-1-4614-1353-0_6
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4614-1352-3
Online ISBN: 978-1-4614-1353-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)