scholarly journals Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets

2014 ◽  
Vol 34 (1) ◽  
pp. 106-117 ◽  
Author(s):  
Susan Gruber ◽  
Roger W. Logan ◽  
Inmaculada Jarrín ◽  
Susana Monge ◽  
Miguel A. Hernán
Keyword(s):  
Ensemble Learning ◽  
Structural Modeling ◽  
Inverse Probability ◽  
Inverse Probability Weights ◽  
Marginal Structural Modeling

scholarly journals Tackling missing radiographic progression data: multiple imputation technique compared with inverse probability weights and complete case analysis

Rheumatology ◽  
2012 ◽  
Vol 52 (2) ◽  
pp. 331-336 ◽  
Author(s):  
M. A. Descalzo ◽  
V. V. Garcia ◽  
I. Gonzalez-Alvaro ◽  
J. Carbonell ◽  
A. Balsa ◽  
...  
Keyword(s):  
Multiple Imputation ◽  
Radiographic Progression ◽  
Case Analysis ◽  
Complete Case Analysis ◽  
Complete Case ◽  
Inverse Probability ◽  
Inverse Probability Weights

scholarly journals Leveraging marginal structural modeling with Cox regression to assess the survival benefit of accepting versus declining kidney allograft offers

2019 ◽  
Author(s):  
Jordana B. Cohen ◽  
Vishnu Potluri ◽  
Paige M. Porrett ◽  
Ruohui Chen ◽  
Marielle Roselli ◽  
...  
Keyword(s):  
Survival Benefit ◽  
Cox Regression ◽  
Structural Modeling ◽  
Kidney Allograft ◽  
Marginal Structural Modeling

scholarly journals Impact of Cumulative Unhealthy Sleep Practices in Adolescence on Substance Use in Young Adulthood Estimated Using Marginal Structural Modeling

2020 ◽  
Vol 14 ◽  
Author(s):  
Chia-Yi Ho ◽  
Sheng-Hsuan Lin ◽  
Meng-Che Tsai ◽  
Tsung Yu ◽  
Carol Strong
Keyword(s):  
Substance Use ◽  
Young Adulthood ◽  
Structural Modeling ◽  
Marginal Structural Modeling

scholarly journals Hormonal contraception and HIV acquisition: reanalysis using marginal structural modeling

AIDS ◽  
2010 ◽  
Vol 24 (11) ◽  
pp. 1778-1781 ◽  
Author(s):  
Charles S Morrison ◽  
Pai-Lien Chen ◽  
Cynthia Kwok ◽  
Barbra A Richardson ◽  
Tsungai Chipato ◽  
...  
Keyword(s):  
Hormonal Contraception ◽  
Structural Modeling ◽  
Hiv Acquisition ◽  
Marginal Structural Modeling

scholarly journals Comparison of Parametric and Nonparametric Estimators for the Association Between Incident Prepregnancy Obesity and Stillbirth in a Population-Based Cohort Study

2019 ◽  
Vol 188 (7) ◽  
pp. 1328-1336 ◽  
Author(s):  
Ya-Hui Yu ◽  
Lisa M Bodnar ◽  
Maria M Brooks ◽  
Katherine P Himes ◽  
Ashley I Naimi
Keyword(s):  
Inverse Probability Weighting ◽  
Population Based ◽  
Parametric Models ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Nonparametric Estimators ◽  
Inverse Probability Weights ◽  
Highly Sensitive ◽  
Prepregnancy Obesity ◽  
Index Weight

AbstractWhile prepregnancy obesity increases risk of stillbirth, few studies have evaluated the role of newly developed obesity independent of long-standing obesity. Additionally, researchers have relied almost exclusively on parametric models, which require correct specification of an unknown function for consistent estimation. We estimated the association between incident obesity and stillbirth in a cohort constructed from linked birth and death records in Pennsylvania (2003–2013). Incident obesity was defined as body mass index (weight (kg)/height (m)2) greater than or equal to 30. We used parametric G-computation, semiparametric inverse-probability weighting, and parametric/nonparametric targeted minimum loss-based estimation (TMLE) to estimate the association between incident prepregnancy obesity and stillbirth. Compared with pregnancies from women who stayed nonobese, women who became obese prior to their next pregnancy were estimated to have 2.0 (95% confidence interval (CI): 0.5, 3.5) more stillbirths per 1,000 pregnancies using parametric G-computation. However, despite well-behaved stabilized inverse probability weights, risk differences estimated from inverse-probability weighting, nonparametric TMLE, and parametric TMLE represented 6.9 (95% CI: 3.7, 10.0), 0.4 (95% CI: 0.1, 0.7), and 2.9 (95% CI: 1.5, 4.2) excess stillbirths per 1,000 pregnancies, respectively. These results, particularly those derived from nonparametric TMLE, were highly sensitive to covariates included in the propensity score models. Our results suggest that caution is warranted when using nonparametric estimators to quantify exposure effects.

Structural equation modeling versus marginal structural modeling for assessing mediation in the presence of posttreatment confounding.

2015 ◽  
Vol 20 (2) ◽  
pp. 204-220 ◽  
Author(s):  
Beatrijs Moerkerke ◽  
Tom Loeys ◽  
Stijn Vansteelandt
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Structural Modeling ◽  
Equation Modeling ◽  
Marginal Structural Modeling

scholarly journals Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling

2013 ◽  
Vol 66 (8) ◽  
pp. S99-S109 ◽  
Author(s):  
Romain Neugebauer ◽  
Bruce Fireman ◽  
Jason A. Roy ◽  
Marsha A. Raebel ◽  
Gregory A. Nichols ◽  
...  
Keyword(s):  
Structural Modeling ◽  
Marginal Structural Modeling
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