TY - JOUR
T1 - A Bayesian semiparametric method for analyzing length-biased data
AU - Harun, Nusrat
AU - Cai, Bo
AU - Shen, Yu
N1 - Funding Information:
We would like to thank Dr. Christina Wolfson for creating and sharing the dataset on survival with dementia derived from the CSHA. The CSHA was supported by the Seniors Independence Research Program, through the National Health Research and Development Program (NHRDP) of Health Canada (project 6606-3954-MC[S]). The progression of dementia project within the CSHA was supported by Pfizer Canada through the Health Activity Program of the Medical Research Council of Canada and the Pharmaceutical Manufacturers Association of Canada; by the NHRDP (project 6603-1417-302[R]); by Bayer; and by the British Columbia Health Research Foundation (projects 38 [93-2] and 34 [96-1]).
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - Survival data obtained from prevalent cohort study designs are often subject to length-biased sampling. Frequentist methods including estimating equation approaches, as well as full likelihood methods, are available for assessing covariate effects on survival from such data. Bayesian methods allow a perspective of probability interpretation for the parameters of interest, and may easily provide the predictive distribution for future observations while incorporating weak prior knowledge on the baseline hazard function. There is lack of Bayesian methods for analyzing length-biased data. In this paper, we propose Bayesian methods for analyzing length-biased data under a proportional hazards model. The prior distribution for the cumulative hazard function is specified semiparametrically using I-Splines. Bayesian conditional and full likelihood approaches are developed for analyzing simulated and real data.
AB - Survival data obtained from prevalent cohort study designs are often subject to length-biased sampling. Frequentist methods including estimating equation approaches, as well as full likelihood methods, are available for assessing covariate effects on survival from such data. Bayesian methods allow a perspective of probability interpretation for the parameters of interest, and may easily provide the predictive distribution for future observations while incorporating weak prior knowledge on the baseline hazard function. There is lack of Bayesian methods for analyzing length-biased data. In this paper, we propose Bayesian methods for analyzing length-biased data under a proportional hazards model. The prior distribution for the cumulative hazard function is specified semiparametrically using I-Splines. Bayesian conditional and full likelihood approaches are developed for analyzing simulated and real data.
KW - Bayesian method
KW - I-splines
KW - length-biased data
KW - prevalent cohort study
KW - proportional hazards model
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U2 - 10.1080/02664763.2020.1753028
DO - 10.1080/02664763.2020.1753028
M3 - Article
C2 - 35707737
AN - SCOPUS:85083573521
SN - 0266-4763
VL - 48
SP - 977
EP - 992
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 6
ER -