Systematic handling of missing data in complex study designs – experiences from the Health 2000 and 2011 Surveys

DOI10.1080/02664763.2016.1144725OpenAlexW2290141882WikidataQ109374017 ScholiaQ109374017MaRDI QIDQ5138215

Hanna Tolonen, Juha Karvanen, Tommi Härkänen, Teppo Juntunen, Risto Lehtonen, Seppo Koskinen, Kari Djerf

Publication date: 3 December 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: http://www.julkari.fi/handle/10024/129811

zbMATH Keywords

multiple imputationinverse probability weightingnon-responsedoubly robust methodscausal model with designnon-participation

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to social sciences (62P25) Graphical methods in statistics (62A09)

Uses Software

• rpart
• OpenBUGS
• MICE

Cites Work

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• Doubly Robust Estimation in Missing Data and Causal Inference Models
• What is meant by ``missing at random?
• Struggles with survey weighting and regression modeling
• Causal inference in statistics: an overview
• Estimating the dimension of a model
• Combining Multiple Imputation and Inverse‐Probability Weighting
• Subsample Ignorable Likelihood for Regression Analysis with Missing Data
• On the Bias of the Multiple-Imputation Variance Estimator in Survey Sampling
• The Calculation of Posterior Distributions by Data Augmentation
• Maximum likelihood identification of Gaussian autoregressive moving average models
• Inference and missing data
• Sample Selection Bias as a Specification Error
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Study Design in Causal Models

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