An Introduction to G Methods
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 epidem...
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| Published in | International journal of epidemiology Vol. 46; no. 2; pp. dyw323 - 762 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
01.04.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0300-5771 1464-3685 |
| DOI | 10.1093/ije/dyw323 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Cole, Stephen R Kennedy, Edward H Naimi, Ashley I |
| AuthorAffiliation | 1 Department of Epidemiology, University of Pittsburgh 3 Department of Statistics, Carnegie Mellon University 2 Department of Epidemiology, University of North Carolina at Chapel Hill and |
| AuthorAffiliation_xml | – name: 3 Department of Statistics, Carnegie Mellon University – name: 1 Department of Epidemiology, University of Pittsburgh – name: 2 Department of Epidemiology, University of North Carolina at Chapel Hill and |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28039382$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1201/9781420011579.ch23 10.1097/EDE.0b013e31818ef366 10.1093/ije/15.3.413 10.1093/ije/dyp334 10.1097/EDE.0b013e31821d0507 10.1097/EDE.0000000000000164 10.1093/aje/kws343 10.1515/jci-2012-0002 10.1002/sim.5316 10.1093/aje/kwp436 10.1097/EDE.0000000000000160 10.1002/sim.5686 10.1093/ije/dyp192 10.1214/14-STS493 10.2307/2532304 |
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| Copyright | The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association. The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association 2016 |
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| Keywords | G Methods Marginal Structural Model G Formula Inverse Probability Weighting Structural Nested Model G Estimation Monte Carlo Estimation |
| Language | English |
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| SubjectTerms | Causality Computer Simulation Confounding Factors (Epidemiology) Education Corner Epidemiologic Research Design Humans Models, Statistical |
| Title | An Introduction to G Methods |
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