Using observational study data as an external control group for a clinical trial: an empirical comparison of methods to account for longitudinal missing data

被引：0
作者：
Vibeke Norvang
Espen A. Haavardsholm
Sara K. Tedeschi
Houchen Lyu
Joseph Sexton
Maria D. Mjaavatten
Tore K. Kvien
Daniel H. Solomon
Kazuki Yoshida
机构：
[1] Diakonhjemmet Hospital,Division of Rheumatology and Research
[2] Department of Medicine,Division of Rheumatology, Inflammation, and Immunity
[3] Brigham and Women’s Hospital/Harvard Medical School,Faculty of Medicine
[4] University of Oslo,Department of Orthopedics
[5] Harvard Medical School,undefined
[6] Chinese PLA General Hospital,undefined
来源：
BMC Medical Research Methodology | / 22卷
关键词：
External control group; Missing data; Multiple imputation; Inverse probability weighting;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
[41]   Assessment of score- and Rasch-based methods for group comparison of longitudinal patient-reported outcomes with intermittent missing data (informative and non-informative) [J].
de Bock, Elodie ;
Hardouin, Jean-Benoit ;
Blanchin, Myriam ;
Le Neel, Tanguy ;
Kubis, Gildas ;
Sebille, Veronique .
QUALITY OF LIFE RESEARCH, 2015, 24 (01) :19-29
[42]   Assessment of score- and Rasch-based methods for group comparison of longitudinal patient-reported outcomes with intermittent missing data (informative and non-informative) [J].
Élodie de Bock ;
Jean-Benoit Hardouin ;
Myriam Blanchin ;
Tanguy Le Neel ;
Gildas Kubis ;
Véronique Sébille .
Quality of Life Research, 2015, 24 :19-29
[43]   How to deal with missing longitudinal data in cost of illness analysis in Alzheimer’s disease—suggestions from the GERAS observational study [J].
Mark Belger ;
Josep Maria Haro ;
Catherine Reed ;
Michael Happich ;
Kristin Kahle-Wrobleski ;
Josep Maria Argimon ;
Giuseppe Bruno ;
Richard Dodel ;
Roy W Jones ;
Bruno Vellas ;
Anders Wimo .
BMC Medical Research Methodology, 16
[44]   Application of Real-World Data to External Control Groups in Oncology Clinical Trial Drug Development [J].
Yap, Timothy A. ;
Jacobs, Ira ;
Baumfeld Andre, Elodie ;
Lee, Lauren J. ;
Beaupre, Darrin ;
Azoulay, Laurent .
FRONTIERS IN ONCOLOGY, 2022, 11
[45]   A simulation study on missing data imputation for dichotomous variables using statistical and machine learning methods [J].
Ge, Yingfeng ;
Li, Zhiwei ;
Zhang, Jinxin .
SCIENTIFIC REPORTS, 2023, 13 (01)
[46]   How to deal with missing longitudinal data in cost of illness analysis in Alzheimer's disease-suggestions from the GERAS observational study [J].
Belger, Mark ;
Haro, Josep Maria ;
Reed, Catherine ;
Happich, Michael ;
Kahle-Wrobleski, Kristin ;
Argimon, Josep Maria ;
Bruno, Giuseppe ;
Dodel, Richard ;
Jones, Roy W. ;
Vellas, Bruno ;
Wimo, Anders .
BMC MEDICAL RESEARCH METHODOLOGY, 2016, 16
[47]   Simulation-Based Study Comparing Multiple Imputation Methods for Non-Monotone Missing Ordinal Data in Longitudinal Settings [J].
Donneau, A. F. ;
Mauer, M. ;
Lambert, P. ;
Molenberghs, G. ;
Albert, A. .
JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 2015, 25 (03) :570-601
[48]   Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study [J].
Marshall, Andrea ;
Altman, Douglas G. ;
Holder, Roger L. .
BMC MEDICAL RESEARCH METHODOLOGY, 2010, 10
[49]   Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study [J].
Andrea Marshall ;
Douglas G Altman ;
Roger L Holder .
BMC Medical Research Methodology, 10
[50]   An Empirical Comparison of Statistical Methods for Missing Data in Randomized, Double-Blind, Placebo-Controlled, Phase 3 Clinical Trials for Chronic Pain and Lipid-Lowering Products [J].
Gnang, Jeanine ;
Kim, Yoonhee ;
Ren, Yi ;
Travis, James ;
Kim, Yongman .
THERAPEUTIC INNOVATION & REGULATORY SCIENCE, 2020, 54 (06) :1416-1427
12345