Generalized Additive Selection Models for the Analysis of Studies with Potentially Nonignorable Missing Outcome Data

DOI10.1111/1541-0420.00070zbMath1210.62100OpenAlexW2149568488WikidataQ30883597 ScholiaQ30883597MaRDI QIDQ3079158

Daniel O. Scharfstein, Rafael A. Irizarry

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1541-0420.00070

zbMATH Keywords

smoothingsensitivity analysisdouble robustnessbackfittinginverse weighting

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Generalized linear models (logistic models) (62J12)

Related Items (4)

Efficacy studies of malaria treatments in Africa: efficient estimation with missing indicators of failure ⋮ The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder ⋮ Semi-parametric Bayesian analysis of binary responses with a continuous covariate subject to non-random missingness ⋮ Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates

Cites Work

• Unnamed Item
• The dimensionality reduction principle for generalized additive models
• Model Selection in Estimating Equations
• Semiparametric efficiency bounds
• Estimation of additive regression models with known links
• Inference and missing data
• Inference in Generalized Additive Mixed Models by Using Smoothing Splines
• Estimation of the failure time distribution in the presence of informative censoring
• Bayesian Smoothing and Regression Splines for Measurement Error Problems
• Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse
• Variable Selection and Function Estimation in Additive Nonparametric Regression Using a Data-Based Prior
• Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
• Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
• Local Likelihood Estimation in Generalized Additive Models
• Informative Drop-Out in Longitudinal Data Analysis
• Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes

This page was built for publication: Generalized Additive Selection Models for the Analysis of Studies with Potentially Nonignorable Missing Outcome Data