Predicting Hearing Threshold in Nonresponsive Subjects Using a Log-Normal Bayesian Linear Model in the Presence of Left-Censored Covariates

被引:7
|
作者
Gajewski, Byron J. [1 ,2 ]
Nicholson, Nannette [3 ]
Widen, Judith E. [4 ]
机构
[1] Univ Kansas, Med Ctr, Sch Med, Dept Biostat, Kansas City, KS 66160 USA
[2] Univ Kansas, Med Ctr, Sch Nursing, Dept Biostat, Kansas City, KS 66160 USA
[3] Univ Arkansas Med Sci, Dept Audiol & Speech Pathol, Little Rock, AR 72204 USA
[4] Univ Kansas, Med Ctr, Dept Speech & Hearing, Kansas City, KS 66160 USA
来源
关键词
Data augmentation; Left-censored data; Gibbs sampler; Prediction; Simple regression; WinBUGS;
D O I
10.1198/sbr.2009.0015
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
We provide a nontrivial example illustrating analysis of a Bayesian clinical trial. Many of the issues discussed in the article are emphasized in a recent Food and Drug Administration (FDA) guidance on use of Bayesian statistics in medical device clinical trials. Here we present a fully Bayesian data analysis for predicting hearing thresholds in subjects who cannot respond to usual hearing tests. The article begins with simple concepts such as simple linear regression and proceeds into more complex issues such as censoring in the dependent and independent variables. Throughout, we emphasize the substantive interpretation of the analysis. Of particular interest is interval estimation, predictive probability for outcomes in future patients, missing data, model checking, and the assessment of frequentist properties of the Bayesian method.
引用
收藏
页码:137 / 148
页数:12
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