Statistical analysis of recurrent adverse events
Liqun Diao Ph.D.
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1
Search for more papers by this authorRichard J. Cook Ph.D.
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1
Search for more papers by this authorKer-Ai Lee M.Sc.
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1
Search for more papers by this authorLiqun Diao Ph.D.
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1
Search for more papers by this authorRichard J. Cook Ph.D.
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1
Search for more papers by this authorKer-Ai Lee M.Sc.
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1
Search for more papers by this authorA. Lawrence Gould
Merck Research Laboratories, 770 Sumneytown Pike, West Point, PA 19486, USA
Search for more papers by this authorSummary
In many clinical trials, adverse events may occur repeatedly over the course of treatment and follow-up. This chapter focuses primarily on the setting of transient adverse events for which it may be sensible to count the number of occurrences and make comparisons between groups on the basis of these counts. This is often reasonable when individuals are followed for the same length of time and there is little interest in when the events occur. The chapter defines notation and discusses general models for recurrent events. Simple regression models are appealing when comparing treatment groups and the chapter describes one such regression approach here. It is increasingly common for the Food and Drug Administration to recommend conduct of large Phase 4 trials to facilitate the collection of more extensive adverse event data in a sample of individuals treated under the standard of care.
References
- Basaria, S., Coviello, A.D., Travison, T.G. et al. (2010) Adverse events associated with testosterone administration. New England Journal of Medicine, 363, 109–122.
- Cobleigh, M.A., Vogel, C.L., Tripaty, D. et al. (1999) Multinational study of efficacy and safety of humanized anti-HER2 monoclonal antibody in women who have HER2-overexpressing metastatic breast cancer that has progressed after chemotherapy for metastatic disease. Journal of Clinical Oncology, 17, 2639–2648.
- Cole, E.H., Cattran, D.C., Farewell, V.T. et al. (1994) A comparison of rabbit antithymocyte serum and OKT3 as prophylaxis against renal allograft rejection. Transplantation, 57, 60–67.
-
Crowder, M.J. (2001) Classical Competing Risks, Chapman & Hall/CRC Press, Boca Raton, FL.
10.1201/9781420035902 Google Scholar
-
Kalbfleisch, J.D. and Prentice, R.L. (2002) The Statistical Analysis of Failure Time Data, John Wiley & Sons, Inc., New York.
10.1002/9781118032985 Google Scholar
- Lawless, J.F. (2003) Statistical Models and Methods for Lifetime Data, John Wiley & Sons, Inc., Hoboken, NJ.
-
Andersen, P.K., Borgan, Ø., Gill, R.D. and Keiding, N. (1993) Statistical Models Based on Counting Processes, Springer-Verlag, New York.
10.1007/978-1-4612-4348-9 Google Scholar
- Lawless, J.F. and Nadeau, J.C. (1995) Nonparametric estimation of cumulative mean functions for recurrent events. Technometrics, 37, 158–168.
- Aalen, O.O. (1978) Nonparametric inference for a family of counting processes. Annals of Statistics, 6, 701–726.
- Cook, R.J. and Lawless, J.F. (2007) Statistical Analysis of Recurrent Events, Springer, New York.
-
Nelson, W. (1969) Hazard plotting for incomplete failure data. Journal of Quality Technology, 1, 27–52.
10.1080/00224065.1969.11980344 Google Scholar
- Ghosh, D. and Lin, D.Y. (2000) Nonparametric analysis of recurrent events and death. Biometrics, 56, 554–562.
- Freedman, L., Sylvester, R. and Byar, D.P. (1989) Using permutation tests and bootstrap confidence intervals to analyse repeated events data from clinical trials. Controlled Clinical Trials, 10, 129–141.
- Siddiqui, O. (2009) Statistical methods to analyze adverse events data of randomized clinical trials. Journal of Biopharmaceutical Statistics, 19, 889–899.
- Cook, R.J. and Major, P. (2001) Methodology for treatment evaluation in patients with cancer metastatic to the bone. Journal of the National Cancer Institute, 93, 534–538.
- Glynn, R.J. and Buring, J.E. (2001) Coutning recurrent events in cancer research. Journal of the National Cancer Institute, 93, 486–489.
- McCullagh, P. and Nelder, J.A. (1989) Generalized Linear Models, 2nd edn, Chapman & Hall, London.
- Lawless, J.F. (1987) Negative binomial and mixed Poisson regression. Canadian Journal of Statistics/Revue Canadienne de Statistique, 15, 209–225.
- Goldberg-Alberts, R. and Page, S. (2006) Multivariate analysis of adverse events. Drug Information Journal, 40, 99–110.
- Dubin, J.A. and O'Malley, S.S. (2010) Event charts for the analysis of adverse events in longitudinal studies: an example from a smoking cessation pharmacotherapy trial. Open Epidemiology Journal, 3, 34–41.
- Hsu, C., Zhou, Z., and Hardin, J.M. (2002) A useful chart to display adverse event occurrences in clinical trials. Proceedings of the 27th Annual SAS Users Group International Conference, pp. 119–127.
- Lee, J.J., Hess, K.R. and Dubin, J.A. (2000) Extensions and applications of event charts. American Statistician, 54, 63–70.
- Guttner, A., Kubler, J. and Pigeot, I. (2007) Multivariate time-to-event analysis of multiple adverse events of drugs in integrated analyses. Statistics in Medicine, 26, 1518–1531.
- Andersen, P.K. and Gill, R.D. (1982) Cox's regression model for counting processes: a large sample study. Annals of Statistics, 10, 1100–1120.
- Lin, D.Y., Wei, L.J., Yang, I. and Ying, Z. (2000) Semiparametric regression for the mean and rate functions of recurrent events. Journal of the Royal Statistical Society, Series B, 62, 711–730.
-
Therneau, T.A. and Hamilton, S.A. (1997) rhDNase as an example of recurrent event analysis. Statistics in Medicine, 16, 2029–2047.
10.1002/(SICI)1097-0258(19970930)16:18<2029::AID-SIM637>3.0.CO;2-H CAS PubMed Web of Science® Google Scholar
-
Therneau, T.A. and Grambsch, P.M. (2000) Modeling Survival Data: Extending the Cox Model, Springer, New York.
10.1007/978-1-4757-3294-8 Google Scholar
- Rondeau, V., Mazroui, Y. and Gonzalez, J.R. (2012) frailtypack: an R package for the analysis of correlated survival data with frailty models using penalized likelihood estimation or parametrical estimation. Journal of Statistical Software, 47, 1–28.
- Cleves, M. (1999) Analysis of Multiple Failure-Time Survival Data, Stata Technical Bulletin STB-49, pp. 30–39, http://www.stata.com/support/faqs/statistics/multiple-failure-time-data/ (accessed 25 June 2014).
- Webert, K.E., Cook, R.J., Sigouin, C. et al. (2006) The risk of bleeding in thrombocytopenic patients with acute myeloid leukemia. Heematologica, 91, 1530–1537.
- Rebulla, P., Finazzi, R., Marangoni, F. et al. (1997) The threshold for prophylactic platelet transfusions in adults with acute myeloid leukemia. New England Journal of Medicine, 337, 1870–1875.
- Henderson, T.O., Whitton, J., Stovall, M. et al. (2007) Secondary sarcomas in childhood cancer survivors: a report from the childhood cancer survivor study. Journal of the National Cancer Institute, 99, 300–308.
- Cook, R.J. and Farewell, V.T. (1994) Guidelines for monitoring efficacy and toxicity responses in clinical-trials. Biometrics, 50, 1146–1152.
- Jennison, C. and Turnbull, B.W. (1993) Group sequential tests for bivariate response: interim analyses of clinical trials with both efficacy and safety endpoints. Biometrics, 49, 741–752.