TY - JOUR
T1 - Evaluating the impact of prior assumptions in Bayesian biostatistics
AU - Morita, Satoshi
AU - Thall, Peter F.
AU - Müller, Peter
N1 - Funding Information:
Acknowledgements Satoshi Morita’s work was supported in part by Grant H21-CLINRES-G-009 from the Ministry of Health, Labour, and Welfare in Japan. Peter Thall’s work was partially supported by Grant NIH/NCI 2R01 CA083932. Peter Müller’s work was partially supported by Grant NIH/NCI R01 CA75981.
PY - 2010
Y1 - 2010
N2 - A common concern in Bayesian data analysis is that an inappropriately informative prior may unduly influence posterior inferences. In the context of Bayesian clinical trial design, well chosen priors are important to ensure that posterior-based decision rules have good frequentist properties. However, it is difficult to quantify prior information in all but the most stylized models. This issue may be addressed by quantifying the prior information in terms of a number of hypothetical patients, i. e., a prior effective sample size (ESS). Prior ESS provides a useful tool for understanding the impact of prior assumptions. For example, the prior ESS may be used to guide calibration of prior variances and other hyperprior parameters. In this paper, we discuss such prior sensitivity analyses by using a recently proposed method to compute a prior ESS. We apply this in several typical settings of Bayesian biomedical data analysis and clinical trial design. The data analyses include cross-tabulated counts, multiple correlated diagnostic tests, and ordinal outcomes using a proportional-odds model. The study designs include a phase I trial with late-onset toxicities, a phase II trial that monitors event times, and a phase I/II trial with dose-finding based on efficacy and toxicity.
AB - A common concern in Bayesian data analysis is that an inappropriately informative prior may unduly influence posterior inferences. In the context of Bayesian clinical trial design, well chosen priors are important to ensure that posterior-based decision rules have good frequentist properties. However, it is difficult to quantify prior information in all but the most stylized models. This issue may be addressed by quantifying the prior information in terms of a number of hypothetical patients, i. e., a prior effective sample size (ESS). Prior ESS provides a useful tool for understanding the impact of prior assumptions. For example, the prior ESS may be used to guide calibration of prior variances and other hyperprior parameters. In this paper, we discuss such prior sensitivity analyses by using a recently proposed method to compute a prior ESS. We apply this in several typical settings of Bayesian biomedical data analysis and clinical trial design. The data analyses include cross-tabulated counts, multiple correlated diagnostic tests, and ordinal outcomes using a proportional-odds model. The study designs include a phase I trial with late-onset toxicities, a phase II trial that monitors event times, and a phase I/II trial with dose-finding based on efficacy and toxicity.
KW - Bayesian analysis
KW - Bayesian biostatistics
KW - Bayesian clinical trial design
KW - Effective sample size
KW - Parametric prior distribution
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U2 - 10.1007/s12561-010-9018-x
DO - 10.1007/s12561-010-9018-x
M3 - Article
C2 - 20668651
AN - SCOPUS:77949559356
SN - 1867-1764
VL - 2
SP - 1
EP - 17
JO - Statistics in Biosciences
JF - Statistics in Biosciences
IS - 1
ER -