Adjusted Analyses in Studies Addressing Therapy and Harm

观察研究 医学 工具变量 结果(博弈论) 倾向得分匹配 随机化 干预(咨询) 选择偏差 危害 随机对照试验 计量经济学 统计 内科学 心理学 精神科 病理 数理经济学 社会心理学 经济 数学
作者
Thomas Agoritsas,Arnaud Merglen,Nilay D. Shah,Martin O’Donnell,Gordon Guyatt
出处
期刊:JAMA [American Medical Association]
卷期号:317 (7): 748-748 被引量:121
标识
DOI:10.1001/jama.2016.20029
摘要

Observational studies almost always have bias because prognostic factors are unequally distributed between patients exposed or not exposed to an intervention. The standard approach to dealing with this problem is adjusted or stratified analysis. Its principle is to use measurement of risk factors to create prognostically homogeneous groups and to combine effect estimates across groups. The purpose of this Users’ Guide is to introduce readers to fundamental concepts underlying adjustment as a way of dealing with prognostic imbalance and to the basic principles and relative trustworthiness of various adjustment strategies. One alternative to the standard approach is propensity analysis, in which groups are matched according to the likelihood of membership in exposed or unexposed groups. Propensity methods can deal with multiple prognostic factors, even if there are relatively few patients having outcome events. However, propensity methods do not address other limitations of traditional adjustment: investigators may not have measured all relevant prognostic factors (or not accurately), and unknown factors may bias the results. A second approach, instrumental variable analysis, relies on identifying a variable associated with the likelihood of receiving the intervention but not associated with any prognostic factor or with the outcome (other than through the intervention); this could mimic randomization. However, as with assumptions of other adjustment approaches, it is never certain if an instrumental variable analysis eliminates bias. Although all these approaches can reduce the risk of bias in observational studies, none replace the balance of both known and unknown prognostic factors offered by randomization.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
1秒前
xsx发布了新的文献求助10
2秒前
陈大侠完成签到 ,获得积分10
3秒前
6秒前
超帅向雁发布了新的文献求助10
6秒前
求索完成签到 ,获得积分10
6秒前
cyh发布了新的文献求助30
7秒前
英俊的铭应助醉熏的幼珊采纳,获得10
9秒前
我是老大应助超帅向雁采纳,获得10
11秒前
nini完成签到,获得积分10
11秒前
12秒前
007完成签到,获得积分20
13秒前
cwy完成签到,获得积分10
14秒前
kevin完成签到,获得积分10
14秒前
艳艳子完成签到,获得积分10
15秒前
王DD完成签到,获得积分10
17秒前
看见了紫荆花完成签到 ,获得积分10
18秒前
a1441949575完成签到 ,获得积分10
19秒前
温暖寻琴发布了新的文献求助10
21秒前
michaelvin完成签到,获得积分10
21秒前
心理学狗都不学完成签到,获得积分10
22秒前
24秒前
24秒前
浮三白完成签到,获得积分10
25秒前
25秒前
25秒前
26秒前
LIU发布了新的文献求助10
28秒前
独孤完成签到 ,获得积分10
30秒前
新奇发布了新的文献求助10
31秒前
echo完成签到 ,获得积分10
32秒前
风夏完成签到,获得积分10
32秒前
韭菜发布了新的文献求助10
32秒前
8R60d8应助温暖寻琴采纳,获得10
33秒前
科研通AI2S应助独特背包采纳,获得10
33秒前
33秒前
诚c发布了新的文献求助10
34秒前
于芋菊完成签到,获得积分0
35秒前
coo完成签到,获得积分10
35秒前
35秒前
高分求助中
The Oxford Handbook of Social Cognition (Second Edition, 2024) 1050
Kinetics of the Esterification Between 2-[(4-hydroxybutoxy)carbonyl] Benzoic Acid with 1,4-Butanediol: Tetrabutyl Orthotitanate as Catalyst 1000
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Handbook of Qualitative Cross-Cultural Research Methods 600
Chen Hansheng: China’s Last Romantic Revolutionary 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3139867
求助须知:如何正确求助?哪些是违规求助? 2790746
关于积分的说明 7796497
捐赠科研通 2447159
什么是DOI,文献DOI怎么找? 1301623
科研通“疑难数据库(出版商)”最低求助积分说明 626313
版权声明 601185