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

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Lewin, Antoine
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Benmarhnia, Tarik
Thomas, Frederique
Chaix, Basile
EPIDEMIOLOGY, 2018, 29 (01) : 87 - 95
[2] ACCOUNTING FOR BIAS IN SURVIVAL ANALYSIS DUE TO MISSING COVARIATE VALUES
GLYNN, RJ
FIELD, TS
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[3] Evaluation of bias and precision in methods of analysis for pragmatic trials with missing outcome data: a simulation study
Royes Joseph
Julius Sim
Reuben Ogollah
Martyn Lewis
Trials, 14 (Suppl 1)
[4] Estimating the Bias in Meta Analysis Estimates for Continuous Data With Non-Random Missing Study Variance
Idris, Nik Ruzni Nik
MATEMATIKA, 2011, 27 (02) : 121 - 128
[5] plasmode simulation to address confounding bias due to missing data in a large electronic health records dataset
Puzhko, Svetlana
Bartlett, Gillian
Schuster, Tibor
PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2020, 29 : 388 - 388
[6] TWO APPROACHES TO EVALUATE MISSING CLINICAL OUTCOME ASSESSMENT RESPONSES: A SIMULATION STUDY
Qin, S.
Ma, J.
Nelson, L.
VALUE IN HEALTH, 2019, 22 : S319 - S319
[7] Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study
van Kuijk, Sander M. J.
Viechtbauer, Wolfgang
Peeters, Louis L.
Smits, Luc
EPIDEMIOLOGY BIOSTATISTICS AND PUBLIC HEALTH, 2016, 13 (01)
[8] Bias of complete-case analysis of williams square crossover designs when data are missing not at random
Sofia Bazakou
Robin Henderson
Linda Sharples
John Matthews
Trials, 16
[9] Bias of complete-case analysis of williams square crossover designs when data are missing not at random
Bazakou, Sofia
Henderson, Robin
Sharples, Linda
Matthews, John
TRIALS, 2015, 16
[10] The M-Value: A Simple Sensitivity Analysis for Bias Due to Missing Data in Treatment Effect Estimates
Mathur, Maya B.
AMERICAN JOURNAL OF EPIDEMIOLOGY, 2023, 192 (04) : 612 - 620

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