Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study

被引：0

作者：

Emily Kawabata ^{[1
]}

Daniel Major-Smith ^{[2
]}

Gemma L. Clayton ^{[1
]}

Chin Yang Shapland ^{[2
]}

Tim P. Morris ^{[1
]}

Alice R. Carter ^{[2
]}

Alba Fernández-Sanlés ^{[1
]}

Maria Carolina Borges ^{[2
]}

Kate Tilling ^{[3
]}

Gareth J. Griffith ^{[1
]}

Louise A. C. Millard ^{[2
]}

George Davey Smith ^{[4
]}

Deborah A. Lawlor ^{[1
]}

Rachael A. Hughes ^{[2
]}

机构：

[1] University of Bristol,MRC Integrative Epidemiology Unit

[2] Bristol Medical School,Population Health Sciences

[3] University of Bristol,undefined

[4] MRC Clinical Trials Unit at UCL,undefined

[5] MRC Unit for Lifelong Health and Ageing at University College London,undefined

来源：

BMC Medical Research Methodology | / 24卷 / 1期

关键词：

Bayesian bias analysis; Inverse probability weighting; Missing not at random; Monte Carlo bias analysis; Multiple imputation; Probabilistic bias analysis; Sensitivity analysis; UK Biobank;

D O I：

10.1186/s12874-024-02382-4

中图分类号：

学科分类号：

摘要：

引用

共 42 条

[41] Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA
Burstyn, Igor
Goldstein, Neal D.
Gustafson, Paul
BMC MEDICAL RESEARCH METHODOLOGY, 2020, 20 (01)
[42] Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA
Igor Burstyn
Neal D. Goldstein
Paul Gustafson
BMC Medical Research Methodology, 20

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