Testing violations of the exponential assumption in cancer clinical trials with survival endpoints.
Personalized cancer therapy requires clinical trials with smaller sample sizes compared to trials involving unselected populations that have not been divided into biomarker subgroups. The use of exponential survival modeling for survival endpoints has the potential of gaining 35% efficiency or saving 28% required sample size (Miller, 1983), making personalized therapy trials more feasible. However, the use of exponential survival has not been fully accepted in cancer research practice due to uncertainty about whether or not the exponential assumption holds. We propose a test for identifying violations of the exponential assumption using a reduced piecewise exponential approach. Compared with an alternative goodness-of-fit test, which suffers from inflation of type I error rate under various censoring mechanisms, the proposed test maintains the correct type I error rate. We conduct power analysis using simulated data based on different types of cancer survival distribution in the SEER registry database, and demonstrate the implementation of this approach in existing cancer clinical trials.
Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M University, 212 Adriance Lab Road, College Station, Texas 77843, U.S.A.
Gang
G
Han
The Biostatistics and Bioinformatics Department, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, Tampa, Florida, 33612, U.S.A.|Oncologic Sciences, University of South Florida, 4202 E. Fowler Ave Tampa, Florida, 33620, U.S.A.
Michael J
MJ
Schell
Department of Biostatistics, Yale University School of Public Health, 60 College Street, New Haven, Connecticut, 06520, U.S.A.
Heping
H
Zhang
Department of Biostatistics, Yale University School of Public Health, 60 College Street, New Haven, Connecticut, 06520, U.S.A.
Daniel
D
Zelterman
Yale Comprehensive Cancer Center, Yale School of Medicine, 333 Cedar Street, New Haven, Connecticut, 06520, U.S.A.
Lajos
L
Pusztai
Yale Comprehensive Cancer Center, Yale School of Medicine, 333 Cedar Street, New Haven, Connecticut, 06520, U.S.A.
Kerin
K
Adelson
Yale Comprehensive Cancer Center, Yale School of Medicine, 333 Cedar Street, New Haven, Connecticut, 06520, U.S.A.
Christos
C
Hatzis
D015415:Biomarkers; D006801:Humans; D009369:Neoplasms; D012042:Registries; D018401:Sample Size; D016019:Survival Analysis; D035501:Uncertainty
D015415:Biomarkers
Censoring; Change-point modeling; Failure rate; Survival analysis; Uniformly most powerful unbiased test
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24982461
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18662965
Stat Biopharm Res. 2012 Oct 1;4(4):348-356
23814641
JAMA. 2011 May 11;305(18):1873-81
21558518
Stat Med. 2007 Oct 15;26(23):4352-74
17342754
J Clin Oncol. 2006 Sep 10;24(26):4236-44
16896004
N Engl J Med. 2010 Oct 28;363(18):1693-703
20979469
Stat Med. 2014 Jan 15;33(1):59-73
23900779
Biometrics. 1983 Dec;39(4):1077-81
6671119