TY - JOUR
T1 - Power and sample size calculation for the additive hazard model
AU - Su, Pei Fang
N1 - Publisher Copyright:
© 2017 Taylor & Francis.
PY - 2017/7/4
Y1 - 2017/7/4
N2 - Sample size determination is essential during the planning phases of a study. When the study endpoint is the time to an event, Cox proportional hazard model is the traditional technique to analyze the effects of covariates on survival time. In contrast to the proportional hazard model, the additive hazard (AH) model specifies that the effect of covariates additively increases or decreases the hazard function. The popularity of this model is that it gives a more intuitive interpretation without the proportional hazard assumption. Because there is no literature estimating the required sample size based on the AH model, we provide a flexible formula for calculating the required sample size. The proposed formula incorporates time-independent and time-dependent effects of covariates without complicated mathematical calculations. The performance of the method was evaluated by extensive simulations. Finally, some pilot studies are shown to illustrate the applications.
AB - Sample size determination is essential during the planning phases of a study. When the study endpoint is the time to an event, Cox proportional hazard model is the traditional technique to analyze the effects of covariates on survival time. In contrast to the proportional hazard model, the additive hazard (AH) model specifies that the effect of covariates additively increases or decreases the hazard function. The popularity of this model is that it gives a more intuitive interpretation without the proportional hazard assumption. Because there is no literature estimating the required sample size based on the AH model, we provide a flexible formula for calculating the required sample size. The proposed formula incorporates time-independent and time-dependent effects of covariates without complicated mathematical calculations. The performance of the method was evaluated by extensive simulations. Finally, some pilot studies are shown to illustrate the applications.
UR - http://www.scopus.com/inward/record.url?scp=84979009921&partnerID=8YFLogxK
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U2 - 10.1080/10543406.2016.1198368
DO - 10.1080/10543406.2016.1198368
M3 - Article
C2 - 27294342
AN - SCOPUS:84979009921
SN - 1054-3406
VL - 27
SP - 571
EP - 583
JO - Journal of Biopharmaceutical Statistics
JF - Journal of Biopharmaceutical Statistics
IS - 4
ER -