Results 211 to 220 of about 155,620 (242)

Augmented inverse probability weighted fractional imputation in quantile regression

Pharmaceutical Statistics, 2020
SummaryBy employing all the observed information and the optimal augmentation term, we propose an augmented inverse probability weighted fractional imputation method (AFI) to handle covariates missing at random in quantile regression. Compared with the existing completely case analysis, inverse probability weighting, multiple imputation and fractional ...
openaire   +2 more sources

Constructing Inverse Probability Weights for Continuous Exposures

Epidemiology, 2014
Inverse probability-weighted marginal structural models with binary exposures are common in epidemiology. Constructing inverse probability weights for a continuous exposure can be complicated by the presence of outliers, and the need to identify a parametric form for the exposure and account for nonconstant exposure variance.
Ashley I, Naimi, Erica E M, Moodie, Nathalie, Auger, Jay S, Kaufman   +3 more
openaire   +2 more sources

Augmented Inverse Probability Weighted Estimator for Cox Missing Covariate Regression

Biometrics, 2001
Summary.This article investigates an augmented inverse selection probability weighted estimator for Cox regression parameter estimation when covariate variables are incomplete. This estimator extends the Horvitz and Thompson (1952,Journal of the American Statistical Association47, 663–685) weighted estimator.
Wang, C. Y., Chen, Hua Yun
openaire   +3 more sources

Methods for Inverse Probability of Attrition Weighting

2020
In this paper, we examine methods for Inverse Probability of Attrition Weighting (IPAW) in a cohort study. Such longitudinal studies often suffer from attrition bias when participants fail to attend follow up visits. IPAW is a common strategy to address attrition bias which allows for unbiased estimation of causal effects.
openaire   +1 more source

Review of inverse probability weighting for dealing with missing data

Statistical Methods in Medical Research, 2011
The simplest approach to dealing with missing data is to restrict the analysis to complete cases, i.e. individuals with no missing values. This can induce bias, however. Inverse probability weighting (IPW) is a commonly used method to correct this bias. It is also used to adjust for unequal sampling fractions in sample surveys.
Shaun R, Seaman, Ian R, White
openaire   +2 more sources

Investigation of selection bias using inverse probability weighting

European Journal of Epidemiology, 2007
In 1999-2001, university graduates in the city of Pamplo na, Spain were invited to participate in a study of cardio vascular diseases and motor vehicle injuries [1]. This selection prevented generalization of the results to the population of Pamplona.
openaire   +1 more source

An introduction to inverse probability of treatment weighting in observational research

CKJ: Clinical Kidney Journal, 2022
Nicholas C Chesnaye, Vianda S Stel, Friedo W Dekker   +2 more
exaly  

Early life exposure to greenness and executive function and behavior: An application of inverse probability weighting of marginal structural models

Environmental Pollution, 2021
Marcia P Jimenez, Izzuddin Aris, Jessica G Young   +2 more
exaly  

Inverse probability weighted methods

2008
openaire   +2 more sources

A note on convergence of calibration weights to inverse probability weights

Statistica Neerlandica
AbstractRecently, nonresponse rates in sample surveys have been increasing. Nonresponse bias is a serious concern in the analysis of sample surveys. The calibration and propensity score methods are used to adjust nonresponse bias. The propensity score method uses the weights of the inverse probability of response. The inverse probability of response is
openaire   +1 more source