Constructing Inverse Probability Weights for Marginal Structural Models [PDF]
American Journal of Epidemiology, 2008 The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the four assumptions of consistency, exchangeability, positivity, and no misspecification of the model used to estimate weights.
Stephen R, Cole, Miguel A, Hernán
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Inverse probability weighting for covariate adjustment in randomized studies [PDF]
Statistics in Medicine, 2013 Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a 'favorable' model that yields strong treatment benefit ...
Li, Lingling +2 more
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Stable inverse probability weighting estimation for longitudinal studies [PDF]
Scandinavian Journal of Statistics, 2021 AbstractWe consider estimation of the average effect of time‐varying dichotomous exposure on outcome using inverse probability weighting (IPW) under the assumption that there is no unmeasured confounding of the exposure–outcome association at each time point.
Vahe Avagyan, Stijn Vansteelandt
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Augmented Inverse Probability Weighting and the Double Robustness Property. [PDF]
Med Decis Making, 2022 This article discusses the augmented inverse propensity weighted (AIPW) estimator as an estimator for average treatment effects. The AIPW combines both the properties of the regression-based estimator and the inverse probability weighted (IPW) estimator and is therefore a “doubly robust” method in that it requires only either the propensity or outcome
Kurz CF.
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On Variance of the Treatment Effect in the Treated When Estimated by Inverse Probability Weighting. [PDF]
Am J Epidemiol, 2022 Reifeis SA, Hudgens MG.
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Inverse probability weighting [PDF]
BMJ, 2016 Statistical analysis usually treats all observations as equally important. In some circumstances, however, it is appropriate to vary the weight given to different observations. Well known examples are in meta-analysis, where the inverse variance (precision) weight given to each contributing study varies, and in the analysis of clustered data.1 ...
Mansournia, M, Altman, D
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Early life exposure to greenness and executive function and behavior: An application of inverse probability weighting of marginal structural models. [PDF]
Environ Pollut, 2021 Jimenez MP +7 more
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Accounting for nonmonotone missing data using inverse probability weighting. [PDF]
Stat Med, 2023 Inverse probability weighting can be used to correct for missing data. New estimators for the weights in the nonmonotone setting were introduced in 2018. These estimators are the unconstrained maximum likelihood estimator (UMLE) and the constrained Bayesian estimator (CBE), an alternative if UMLE fails to converge.
Ross RK +5 more
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Robust Inference Using Inverse Probability Weighting [PDF]
Journal of the American Statistical Association, 2019 Inverse probability weighting (IPW) is widely used in empirical work in economics and other disciplines. As Gaussian approximations perform poorly in the presence of “small denominators,” trimming is routinely employed as a regularization strategy. However, ad hoc trimming of the observations renders usual inference procedures invalid for the target ...
Ma, Xinwei, Wang, Jingshen
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Combining Multiple Imputation and Inverse‐Probability Weighting [PDF]
Biometrics, 2011 Summary Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse‐probability weighting (IPW). IPW is also used to adjust for unequal sampling fractions. MI is generally more efficient than IPW but more complex. Whereas IPW requires only a model for the probability that an individual has complete data (a univariate
Seaman, Shaun R. +3 more
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