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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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ipw: An R Package for Inverse Probability Weighting
Journal of Statistical Software, 2011 We describe the R package ipw for estimating inverse probability weights. We show how to use the package to fit marginal structural models through inverse probability weighting, to estimate causal effects.
Ronald B. Geskus, Willem M. van der Wal
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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.
Ross RK +5 more
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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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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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PLoS ONE, 2022 Objectives While corticosteroids have been hypothesized to exert protective benefits in patients infected with SARS-CoV-2, data remain mixed. This study sought to investigate the outcomes of methylprednisone administration in an Italian cohort of ...
Lorenzo Porta +6 more
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On inverse probability-weighted estimators in the presence of interference [PDF]
Biometrika, 2016 We consider inference about the causal effect of a treatment or exposure in the presence of interference, i.e., when one individual’s treatment affects the outcome of another individual. In the observational setting where the treatment assignment mechanism is not known, inverse probability-weighted estimators have been proposed when individuals can be ...
Liu, L. +2 more
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International Journal of Infectious Diseases, 2022 Objectives: To evaluate the effectiveness of remdesivir in the early stage of nonsevere COVID-19. Although several randomized controlled trials have compared the effectiveness of remdesivir with that of a placebo, there is limited evidence regarding its ...
Shinya Tsuzuki +20 more
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A note on overadjustment in inverse probability weighted estimation [PDF]
Biometrika, 2010 Standardized means, commonly used in observational studies in epidemiology to adjust for potential confounders, are equal to inverse probability weighted means with inverse weights equal to the empirical propensity scores. More refined standardization corresponds with empirical propensity scores computed under more flexible models.
Andrea Rotnitzky +2 more
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Inverse probability weighting with error-prone covariates [PDF]
Biometrika, 2013 Inverse probability-weighted estimators are widely used in applications where data are missing due to nonresponse or censoring and in the estimation of causal effects from observational studies. Current estimators rely on ignorability assumptions for response indicators or treatment assignment and outcomes being conditional on observed covariates which
Daniel F. McCaffrey +2 more
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