Bayesian model averaging in meta-analysis: vitamin E supplementation and mortality

荟萃分析 数据提取 贝叶斯概率 医学 背景(考古学) 统计 马尔科夫蒙特卡洛 随机效应模型 梅德林 临床试验 内科学 数学 生物 古生物学 生物化学
作者
Donald A. Berry,J. Kyle Wathen,Margaret Newell
出处
期刊:Clinical Trials [SAGE Publishing]
卷期号:6 (1): 28-41 被引量:83
标识
DOI:10.1177/1740774508101279
摘要

Context The strength and relevance of a meta-analysis depends on the validity of the statistical methods used. Of special importance is appropriately assessing different sources of variability. Many studies including meta-analyses have evaluated the efficacy and safety of vitamin E and have yielded varying results. Illuminating and resolving these disparities requires addressing study variability and model uncertainty. Objective To describe Bayesian meta-analysis methods for combining data from clinical trials, using recent studies that analyzed the relationship between vitamin E dose and all-cause mortality. Data Sources Studies used in a previously published meta-analysis appended by studies identified by a search of MEDLINE from August 2004 to December 2005 using the MeSH terms vitamin e and alpha tocopherol. Study Selection Inclusion criteria: men and nonpregnant women; use of vitamin E alone or in combination with other vitamins or minerals; random allocation of participants to either vitamin E or a placebo or other control group; intervention and follow-up duration greater than 1 year; 10 or more deaths. Data Extraction Independent data extraction by one author was reviewed and confirmed by a second author. Corresponding authors of the original publications were contacted when questions arose. Data Synthesis Data collection included the number of patients and deaths, percent men, use of other vitamins or minerals, mean age, and length of follow-up. We combined study results using Bayesian hierarchical model averaging. Analyses used Markov chain Monte Carlo computational techniques. Conclusions Vitamin E intake is unlikely to affect mortality regardless of dose. The Bayesian meta-analyses presented here are ideal for incorporating disparate sources of variability, including trial effect and model uncertainty. Clinical Trials 2009; 6: 28—41. http://ctj.sagepub.com

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI6.2应助wugui采纳,获得10
2秒前
wway发布了新的文献求助10
2秒前
2秒前
evergarden发布了新的文献求助10
4秒前
7秒前
suye发布了新的文献求助10
8秒前
独特的松思完成签到,获得积分10
8秒前
张鱼大丸子完成签到 ,获得积分10
9秒前
song完成签到,获得积分10
9秒前
英俊的铭应助李庭福采纳,获得10
9秒前
徐青书发布了新的文献求助10
10秒前
机灵柚子发布了新的文献求助30
11秒前
英俊的铭应助Min采纳,获得10
11秒前
majuanwei完成签到,获得积分10
11秒前
wuwen应助XPH采纳,获得10
12秒前
Yuzi_YU发布了新的文献求助10
13秒前
梦珠发布了新的文献求助10
13秒前
干净的琦应助2226采纳,获得30
14秒前
14秒前
CipherSage应助科研小白采纳,获得10
15秒前
2226应助XPH采纳,获得10
15秒前
想发sci完成签到,获得积分10
16秒前
003发布了新的文献求助20
17秒前
万能图书馆应助王达采纳,获得10
18秒前
19秒前
张汶杰发布了新的文献求助10
21秒前
21秒前
zhangwenkang应助林小鱼采纳,获得10
22秒前
wst1988完成签到,获得积分0
22秒前
AN发布了新的文献求助150
23秒前
23秒前
26秒前
song发布了新的文献求助10
26秒前
乔文达发布了新的文献求助10
27秒前
18298859129完成签到,获得积分10
28秒前
学术文献互助完成签到,获得积分0
29秒前
zfl完成签到 ,获得积分10
29秒前
IWBAFL完成签到,获得积分10
29秒前
星辰大海应助chi采纳,获得10
30秒前
30秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Developing Genetic Editing Tools for Lysobacter 2000
卤化钙钛矿人工突触的研究 2000
Моделирование процессов самоорганизации в кристаллообразующих системах 1000
History of U.S. Space Surveillance and Satellite Cataloging 1000
Malcolm Fraser : a biography 700
Handbook of Optical Systems,Volume 6:Advanced Physical Optics 666
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6514760
求助须知:如何正确求助?哪些是违规求助? 8308155
关于积分的说明 17754713
捐赠科研通 5616566
什么是DOI,文献DOI怎么找? 2924722
邀请新用户注册赠送积分活动 1901757
关于科研通互助平台的介绍 1763118