An introduction to g methods

被引：224
作者：
Naimi, Ashley I. [1 ]
Cole, Stephen R. [2 ]
Kennedy, Edward H. [3 ]
机构：
[1] Univ Pittsburgh, Dept Epidemiol, 130 DeSoto St 503 Parran Hall, Pittsburgh, PA 15261 USA
[2] Univ North Carolina Chapel Hill, Dept Epidemiol, Chapel Hill, NC USA
[3] Carnegie Mellon Univ, Dept Stat, Pittsburgh, PA 15213 USA
来源：
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY | 2017年 / 46卷 / 02期
关键词：
G Methods; Marginal Structural Model; Structural Nested Model; G Formula; Inverse Probability Weighting; G Estimation; Monte Carlo Estimation; PARAMETRIC-G-FORMULA; EXPOSURE; MODELS;
D O I：
10.1093/ije/dyw323
中图分类号：
R1 [预防医学、卫生学];
学科分类号：
1004 ; 120402 ;
摘要：
Robins' generalized methods (g methods) provide consistent estimates of contrasts (e.g. differences, ratios) of potential outcomes under a less restrictive set of identification conditions than do standard regression methods (e.g. linear, logistic, Cox regression). Uptake of g methods by epidemiologists has been hampered by limitations in understanding both conceptual and technical details. We present a simple worked example that illustrates basic concepts, while minimizing technical complications.
引用
收藏
页码：756 / 762
页数：7
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