TY - JOUR
T1 - Exponential tilt models for two-group comparison with censored data
AU - Wang, Chi
AU - Tan, Zhiqiang
AU - Louis, Thomas A.
N1 - Funding Information:
The authors wish to thank a reviewer for helpful comments that have greatly improved the article. This research was supported by the U.S. National Science Foundation for Zhiqiang Tan.
Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2011/3
Y1 - 2011/3
N2 - We study application of the Exponential Tilt Model (ETM) to compare survival distributions in two groups. The ETM assumes a parametric form for the density ratio of the two distributions. It accommodates a broad array of parametric models such as the log-normal and gamma models and can be sufficiently flexible to allow for crossing hazard and crossing survival functions. We develop a nonparametric likelihood approach to estimate ETM parameters in the presence of censoring and establish related asymptotic results. We compare the ETM to the Proportional Hazards Model (PHM) in simulation studies. When the proportional hazards assumption is not satisfied but the ETM assumption is, the ETM has better power for testing the hypothesis of no difference between the two groups. And, importantly, when the ETM relation is not satisfied but the PHM assumption is, the ETM can still have power reasonably close to that of the PHM. Application of the ETM is illustrated by a gastrointestinal tumor study.
AB - We study application of the Exponential Tilt Model (ETM) to compare survival distributions in two groups. The ETM assumes a parametric form for the density ratio of the two distributions. It accommodates a broad array of parametric models such as the log-normal and gamma models and can be sufficiently flexible to allow for crossing hazard and crossing survival functions. We develop a nonparametric likelihood approach to estimate ETM parameters in the presence of censoring and establish related asymptotic results. We compare the ETM to the Proportional Hazards Model (PHM) in simulation studies. When the proportional hazards assumption is not satisfied but the ETM assumption is, the ETM has better power for testing the hypothesis of no difference between the two groups. And, importantly, when the ETM relation is not satisfied but the PHM assumption is, the ETM can still have power reasonably close to that of the PHM. Application of the ETM is illustrated by a gastrointestinal tumor study.
KW - Censored data
KW - Exponential tilt model
KW - Non-parametric likelihood
KW - Proportional hazards model
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U2 - 10.1016/j.jspi.2010.09.009
DO - 10.1016/j.jspi.2010.09.009
M3 - Article
AN - SCOPUS:78549294799
SN - 0378-3758
VL - 141
SP - 1102
EP - 1117
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
IS - 3
ER -