Analysis of incomplete data under non-random mechanisms: bayesian inference
作者:
Patricia A. Pepple,
Sung C. Choi,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1994)
卷期:
Volume 23,
issue 3
页码: 743-767
ISSN:0361-0918
年代: 1994
DOI:10.1080/03610919408813197
出版商: Marcel Dekker, Inc.
关键词: Bayes factor;clinical trials;measure of evidence;noninformative prior;non-random incomplete data;posterior probability;proportion
数据来源: Taylor
摘要:
Inferences are made concerning population proportions when data are not missing at random.Both one sample and two sample situations are considered with examples in clinical trials.The one samplesituation involves the existence of response related incomplete data in a study conducted to make inferences involving the proportion. The two sample problem involves comparing two treatments in clinical trials when there exists dropouts due to both the treatment and the response to the treatment.Bayes procedures are used in estimating parameters of interest and testing hypotheses of interest in these two situations. An ad-hoc approach to the classical inference is presented for each ofthe two situations and compared with the Bayesian approach discussed. To illustrate the theory developed, data from clinical trials of severe head trauma patients at the Medical College of Virginia Head Injury Center from 1984 to 1987 is considered
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