Results 11 to 20 of about 155,620 (242)
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
openaire +4 more sources
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
core +4 more sources
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
openaire +5 more sources
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
openaire +3 more sources
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
openaire +2 more sources
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
openaire +3 more sources
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
openaire +3 more sources
Inverse probability weighting for clustered nonresponse [PDF]
Biometrika, 2011 Correlated nonresponse within clusters arises in certain survey settings. It is often represented by a random effects model and assumed to be cluster-specific nonignorable, in the sense that survey and nonresponse outcomes are conditionally independent given cluster-level random effects.
Chris J. Skinner, Julia D'Arrigo
openaire +3 more sources
Augmented Inverse Probability Weighting and the Double Robustness Property [PDF]
Medical Decision Making, 2021 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
openaire +4 more sources
Variance estimation in inverse probability weighted Cox models
Biometrics, 2020 AbstractInverse probability weighted Cox models can be used to estimate marginal hazard ratios under different point treatments in observational studies. To obtain variance estimates, the robust sandwich variance estimator is often recommended to account for the induced correlation among weighted observations.
Di Shu +3 more
openaire +5 more sources