Results 11 to 20 of about 1,854,472 (300)
ipw: AnRPackage for Inverse Probability Weighting [PDF]
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. Our package can be used with data from a point treatment situation as well as with a time-varying exposure and time-varying confounders.
van der Wal, Willem, Geskus, Ronald
openaire +6 more sources
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.
europepmc +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
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
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
openaire +2 more sources
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
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
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
Journal of Biostatistics and Epidemiology, 2020 Introduction: Missing values are frequently seen in data sets of research studiesespecially in medical studies.Therefore, it is essential that the data, especially in medical research should evaluate in terms of the structure of missingness.This study ...
Freshteh Osmani, Ebrahim Hajizadeh
doaj +1 more source
An introduction to inverse probability of treatment weighting in observational research [PDF]
Clinical Kidney Journal, 2021 ABSTRACT In this article we introduce the concept of inverse probability of treatment weighting (IPTW) and describe how this method can be applied to adjust for measured confounding in observational research, illustrated by a clinical example from nephrology. IPTW involves two main steps.
Chesnaye, Nicholas C +6 more
openaire +4 more sources