Connections between Survey Calibration Estimators and Semiparametric Models for Incomplete Data

DOI10.1111/j.1751-5823.2011.00138.xzbMath1422.62048OpenAlexW2142082267WikidataQ30653939 ScholiaQ30653939MaRDI QIDQ5299793

Thomas Lumley, James Y. Dai, Pamela A. Shaw

Publication date: 21 June 2013

Published in: International Statistical Review (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3699889

zbMATH Keywords

regressioncausal inferencedesigned-based inference

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Sampling theory, sample surveys (62D05) Estimation in survival analysis and censored data (62N02)

Related Items (20)

Meta-analysis of independent datasets using constrained generalised method of moments ⋮ Z-estimation and stratified samples: application to survival models ⋮ Variance Estimation under Two‐Phase Sampling ⋮ ON DERIVATION OF THE SEMI-PARAMETRIC WEIGHTED LIKELIHOOD ESTIMATOR, SPW, AND THE WEIGHTED CONDITIONAL PSEUDO LIKELIHOOD ESTIMATOR, WCPE ⋮ Complex sampling designs: uniform limit theorems and applications ⋮ Weight calibration to improve the efficiency of pure risk estimates from case‐control samples nested in a cohort ⋮ Efficient odds ratio estimation under two‐phase sampling using error‐prone data from a multi‐national HIV research cohort ⋮ Integrating information from existing risk prediction models with no model details ⋮ Calibration Weighting Methods for Complex Surveys ⋮ Weight calibration to improve efficiency for estimating pure risks from the additive hazards model with the nested case‐control design ⋮ Second-order asymptotic theory for calibration estimators in sampling and missing-data problems ⋮ Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference ⋮ Improved generalized raking estimators to address dependent covariate and failure‐time outcome error ⋮ Accounting for dependent errors in predictors and time-to-event outcomes using electronic health records, validation samples and multiple imputation ⋮ Combining Multiple Observational Data Sources to Estimate Causal Effects ⋮ Weighted likelihood estimation under two-phase sampling ⋮ Response‐dependent two‐phase sampling designs for biomarker studies ⋮ Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation ⋮ Estimation of finite population duration distributions from longitudinal survey panels with intermittent followup ⋮ Oracle, multiple robust and multipurpose calibration in a missing response problem

Uses Software

• Survey

Cites Work

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