Testing the treatment effect on competing causes of death in oncology clinical trials

BMC Medical Research Methodology

Stefan Michiels²  Federico Rotolo¹ 

[1] Gustave Roussy, Service de Biostatistique et d’Épidémiologie, F-94805 Villejuif, France;Univérsité Paris-Sud, F-94805 Villejuif, France

关键词: Gray’s test;    Cause-specific hazard;    Cumulative incidence function;    Cancer death;    Cause of death;    Peto’s test;    Competing risks;   

Others  :  865400
DOI  :  10.1186/1471-2288-14-72

 received in 2013-12-04, accepted in 2014-05-21,  发布年份 2014

【 摘 要 】

Background

Chemotherapy is expected to reduce cancer deaths (CD), while possibly being harmful in terms of non-cancer deaths (NCD) because of toxicity. Peto’s log-rank test is popular in the medical literature, but its operating characteristics are barely known. We compared this test to the most common ones in the statistical literature: the cause-specific hazard test and Gray’s test on the hazard of the subdistribution. We investigated for the first time the impact of reclassifications of causes of death (CoD) after recurrences, and of misclassification of CoD.

Methods

We present a simulation study in which we varied the censoring rate and the correlation between CD and NCD times, we generated recurrence times to study the role of the reclassification of CoD, and we added 20% misclassified CoD. We considered four scenarios for the treatment effect: none; none for CD and negative for NCD; positive for CD and none for NCD; positive for CD and negative for NCD. We applied the three tests to a randomized clinical trial evaluating adjuvant chemotherapy in 1,867 patients with non-small-cell lung cancer.

Results

Most often the three tests well preserved their nominal size, Gray’s test did not when the treatment had an effect on the competing CoD. With a high rate of misclassified CoD, Gray’s and the cause-specific tests lost much of their power, whereas the Peto’s test had the highest power. The cause-specific test had inflated size for NCD when the treatment was beneficial for CD with many misclassified CoD, but had the highest power for NCD when the treatment had no effect on CD, and had similar power to Peto’s test for CD when the treatment had no effect on NCD. Gray’s test performed best when the effect on the two CoD was opposite. The higher the censoring, the lower the rejection probabilities of all the tests and the smaller their differences.

Conclusions

In this first head-to-head comparison of the three tests, the cause-specific test often proved to be the most reliable. Comparing results with and without misclassification of the CoD, Peto’s test was the least influenced by the presence of such misclassification.

【 授权许可】

   
2014 Rotolo and Michiels; licensee BioMed Central Ltd.

【 图 表 】

【 参考文献 】

• [1]Prentice RL, Kalbfleisch JD, Peterson AV, Flournoy N, Farewell VT, Breslow NE: The analysis of failure times in the presence of competing risks. Biometrics 1978, 34(4):541-554.
• [2]Gooley T, Leisenring W, Crowley J, Storer B: Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. Stat Med 1999, 30(6):695-705. doi:10.1002/(SICI)1097-02581999033018:6<695::AID-SIM60>3.0.CO;2-O
• [3]Putter H, Fiocco M, Geskus RB: Tutorial in biostatistics: competing risks and multi-state models. Stat Med 2007, 26(11):2389-430. doi:10.1002/sim.2712
• [4]Allignol A, Schumacher M, Wanner C, Drechsler C, Beyersmann J: Understanding competing risks: a simulation point of view. BMC Med Res Methodol 2011, 11(1):86. doi:10.1186/1471-2288-11-86 BioMed Central Full Text
• [5]Koller M, Raatz H, Steyerberg E, Wolbers M: Competing risks and the clinical community: irrelevance or ignorance? Stat Med 2012, 31(11–12):1089-1097. doi:10.1002/sim.4384
• [6]Klein JP, Shu YY: Multi-state models for bone marrow transplantation studies. Stat Methods Med Res 2002, 11:117-139. doi:10.1191/0962280202sm277ra
• [7]Lim H, Zhang X, Dyck R, Osgood N: Methods of competing risks analysis of end-stage renal disease and mortality among people with diabetes. BMC Med Res Methodol 2010, 10(1):97. doi:10.1186/1471-2288-10-97 BioMed Central Full Text
• [8]Deslandes E, Chevret S: Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to icu data. BMC Med Res Methodol 2010, 10(1):69. doi:10.1186/1471-2288-10-69 BioMed Central Full Text
• [9]Chappell R: Competing risk analyses: how are they different and why should you care? Clin Cancer Res 2012, 18(8):2127-2129. doi:10.1158/1078-0432.CCR-12-0455
• [10]Dignam JJ, Zhang Q, Kocherginsky M: The use and interpretation of competing risks regression models. Clin Cancer Res 2012, 18(8):2301-2308. doi:10.1158/1078-0432.CCR-11-2097
• [11]Rauch G, Kieser M, Ulrich S, Doherty P, Rauch B, Schneider S, Riemer T, Senges J: Competing time-to-event endpoints in cardiology trials: a simulation study to illustrate the importance of an adequate statistical analysis. Eur J Prev Cardiol 2014, 21(1):74-80. doi:10.1177/2047487312460518
• [12]Pintilie M: Competing Risks: A Practical Perspective. New York: Wiley; 2006. doi:10.1002/9780470870709
• [13]Gaynor JJ, Feuer EJ, Tan CC, Wu DH, Little CR, Straus DJ, Clarkson BD, Brennan MF: On the use of cause-specific failure and conditional failure probabilities: examples from clinical oncology data. J Am Stat Assoc 1993, 88(422):400-409. doi:10.1080/01621459.1993.10476289
• [14]Gray RJ: A class of k-sample tests for comparing the cumulative incidence of a competing risk. Ann Stat 1988, 16(3):1141-1154.
• [15]Fine JP, Gray RJ: A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999, 94(446):496-509.
• [16]Early Breast Cancer Trialists’ Collaborative Group: Treatment of Early Breast Cancer: Worldwide Evidence 1985–1990, vol. 1. Oxford: Oxford University Press; 1990.
• [17]Early Breast Cancer Trialists’ Collaborative Group: Effects of radiotherapy and surgery in early breast cancer – an overview of the randomized trials. New Engl J Med 1995, 333(22):1444-1456. doi:10.1056/NEJM199511303332202
• [18]Early Breast Cancer Trialists’ Collaborative Group: Tamoxifen for early breast cancer: an overview of the randomised trials. Lancet 1998, 351(9114):1451-1467. doi:10.1016/S0140-67369711423-4
• [19]Early Breast Cancer Trialists’ Collaborative Group: Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 2005, 365:1687-1717. doi:10.1016/S0140-67360566544-0
• [20]Bourhis J, Overgaard J, Audry H, Ang KK, Saunders M, Bernier J, Horiot J-C, Maître AL, Pajak TF, Poulsen MG, O’Sullivan B, Dobrowsky W, Hliniak A, Skladowski K, Hay JH, Pinto LH, Fallai C, Fu KK, Sylvester R, Pignon J-P: Hyperfractionated or accelerated radiotherapy in head and neck cancer: a meta-analysis. Lancet 2006, 368(9538):843-854. doi:10.1016/S0140-67360669121-6
• [21]Pignon J, Bourhis J, Domenge C, Designé L: Chemotherapy added to locoregional treatment for head and neck squamous-cell carcinoma: three meta-analyses of updated individual data. Lancet 2000, 355(9208):949-955. doi:10.1016/S0140-67360090011-4
• [22]Pignon J-P, le Maître A, Maillard E, Bourhis J: Meta-analysis of chemotherapy in head and neck cancer (MACH-NC): an update on 93 randomised trials and 17,346 patients. Radiother Oncol 2009, 92(1):4-14. doi:10.1016/j.radonc.2009.04.014
• [23]Early Breast Cancer Trialists’ Collaborative Group: Effects of radiotherapy and of differences in the extent of surgery for early breast cancer on local recurrence and 15-year survival: an overview of the randomised trials. Lancet 2005, 366(9503):2087l. doi:10.1016/S0140-67360567887-7
• [24]Early Breast Cancer Trialists’ Collaborative Group: Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet 2011, 378(9793):771-784. doi:10.1016/S0140-67361160993-8
• [25]Dignam JJ, Kocherginsky MN: Choice and interpretation of statistical tests used when competing risks are present. J Clin Oncol 2008, 26(24):4027-4034. doi:10.1200/JCO.2007.12.9866
• [26]Pintilie M: Dealing with competing risks: testing covariates and calculating sample size. Stat Med 2002, 21(22):3317-3324. doi:10.1002/sim.1271
• [27]Freidlin B, Korn EL: Testing treatment effects in the presence of competing risks. Stat Med 2005, 24(11):1703-1712. doi:10.1002/sim.2054
• [28]Williamson PR, Kolamunnage-Dona R, Tudur Smith C: The influence of competing-risks setting on the choice of hypothesis test for treatment effect. Biostatistics 2007, 8(4):689-694. doi:10.1093/biostatistics/kxl040
• [29]Ruan PK, Gray RJ: A method for analyzing disease-specific mortality with missing cause of death information. Lifetime Data Anal 2006, 12(1):35-51. doi:10.1007/s10985-005-7219-2
• [30]International Adjuvant Lung Cancer Trial Collaborative Group: Cisplatin-based adjuvant chemotherapy in patients with completely resected non-small-cell lung cancer. New Engl J Med 2004, 350(4):351. doi:10.1056/NEJMoa031644
• [31]Arriagada R, Dunant A, Pignon J-P, Bergman B, Chabowski M, Grunenwald D, Kozlowski M, Le Péchoux C, Pirker R, Pinel M-IS, Tarayre M, Le Chevalier T: Long-Term results of the international adjuvant lung cancer trial evaluating adjuvant Cisplatin-Based chemotherapy in resected lung cancer. J Clin Oncol 2010, 28(1):35-42. doi:10.1200/JCO.2009.23.2272
• [32]Nelson W: Theory and applications of hazard plotting for censored failure data. Technometrics 1972, 14(4):945-966. doi:10.1080/00401706.1972.10488991
• [33]Aalen O: Nonparametric inference for a family of counting processes. Ann Stat 1978, 6(3):701-726. doi:10.1214/aos/1176344198
• [34]Aalen OO, Johansen S: An empirical transition matrix for non-homogeneous markov chains based on censored observations. Scand J Stat 1978, 5(3):141-150.
• [35]Moreno-Betancur M, Latouche A: Regression modeling of the cumulative incidence function with missing causes of failure using pseudo-values. Stat Med 2013, 32(18):3206-3223. doi:10.1002/sim.5755