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    Rights statement: Pankhurst, L, Mitra, R, Kimber, A, Collett, D. Multiply imputing missing values arising by design in transplant survival data. Biometrical Journal. 2020; 1192-1207. https://doi.org/10.1002/bimj.201800253 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for self-archiving.

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Multiply imputing missing values arising by design in transplant survival data

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Multiply imputing missing values arising by design in transplant survival data. / Mitra, Robin; Kimber, Alan; Pankhurst, Laura et al.
In: Biometrical Journal, Vol. 62, No. 5, 01.09.2020, p. 1192-1207.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Mitra, R, Kimber, A, Pankhurst, L & Collett, D 2020, 'Multiply imputing missing values arising by design in transplant survival data', Biometrical Journal, vol. 62, no. 5, pp. 1192-1207. https://doi.org/10.1002/bimj.201800253

APA

Mitra, R., Kimber, A., Pankhurst, L., & Collett, D. (2020). Multiply imputing missing values arising by design in transplant survival data. Biometrical Journal, 62(5), 1192-1207. https://doi.org/10.1002/bimj.201800253

Vancouver

Mitra R, Kimber A, Pankhurst L, Collett D. Multiply imputing missing values arising by design in transplant survival data. Biometrical Journal. 2020 Sept 1;62(5):1192-1207. Epub 2020 Feb 20. doi: 10.1002/bimj.201800253

Author

Mitra, Robin ; Kimber, Alan ; Pankhurst, Laura et al. / Multiply imputing missing values arising by design in transplant survival data. In: Biometrical Journal. 2020 ; Vol. 62, No. 5. pp. 1192-1207.

Bibtex

@article{7b3eafacbb7b4689a7b235de3081ad91,
title = "Multiply imputing missing values arising by design in transplant survival data",
abstract = "In this article, we address a missing data problem that occurs in transplant survival studies. Recipients of organ transplants are followed up from transplantation and their survival times recorded, together with various explanatory variables. Due to differences in data collection procedures in different centers or over time, a particular explanatory variable (or set of variables) may only be recorded for certain recipients, which results in this variable being missing for a substantial number of records in the data. The variable may also turn out to be an important predictor of survival and so it is important to handle this missing‐by‐design problem appropriately. Consensus in the literature is to handle this problem with complete case analysis, as the missing data are assumed to arise under an appropriate missing at random mechanism that gives consistent estimates here. Specifically, the missing values can reasonably be assumed not to be related to the survival time. In this article, we investigate the potential for multiple imputation to handle this problem in a relevant study on survival after kidney transplantation, and show that it comprehensively outperforms complete case analysis on a range of measures. This is a particularly important finding in the medical context as imputing large amounts of missing data is often viewed with scepticism.",
author = "Robin Mitra and Alan Kimber and Laura Pankhurst and Dave Collett",
note = "Pankhurst, L, Mitra, R, Kimber, A, Collett, D. Multiply imputing missing values arising by design in transplant survival data. Biometrical Journal. 2020; 1192-1207. https://doi.org/10.1002/bimj.201800253 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for self-archiving. ",
year = "2020",
month = sep,
day = "1",
doi = "10.1002/bimj.201800253",
language = "English",
volume = "62",
pages = "1192--1207",
journal = "Biometrical Journal",
issn = "0323-3847",
publisher = "Wiley-VCH Verlag",
number = "5",

}

RIS

TY - JOUR

T1 - Multiply imputing missing values arising by design in transplant survival data

AU - Mitra, Robin

AU - Kimber, Alan

AU - Pankhurst, Laura

AU - Collett, Dave

N1 - Pankhurst, L, Mitra, R, Kimber, A, Collett, D. Multiply imputing missing values arising by design in transplant survival data. Biometrical Journal. 2020; 1192-1207. https://doi.org/10.1002/bimj.201800253 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for self-archiving.

PY - 2020/9/1

Y1 - 2020/9/1

N2 - In this article, we address a missing data problem that occurs in transplant survival studies. Recipients of organ transplants are followed up from transplantation and their survival times recorded, together with various explanatory variables. Due to differences in data collection procedures in different centers or over time, a particular explanatory variable (or set of variables) may only be recorded for certain recipients, which results in this variable being missing for a substantial number of records in the data. The variable may also turn out to be an important predictor of survival and so it is important to handle this missing‐by‐design problem appropriately. Consensus in the literature is to handle this problem with complete case analysis, as the missing data are assumed to arise under an appropriate missing at random mechanism that gives consistent estimates here. Specifically, the missing values can reasonably be assumed not to be related to the survival time. In this article, we investigate the potential for multiple imputation to handle this problem in a relevant study on survival after kidney transplantation, and show that it comprehensively outperforms complete case analysis on a range of measures. This is a particularly important finding in the medical context as imputing large amounts of missing data is often viewed with scepticism.

AB - In this article, we address a missing data problem that occurs in transplant survival studies. Recipients of organ transplants are followed up from transplantation and their survival times recorded, together with various explanatory variables. Due to differences in data collection procedures in different centers or over time, a particular explanatory variable (or set of variables) may only be recorded for certain recipients, which results in this variable being missing for a substantial number of records in the data. The variable may also turn out to be an important predictor of survival and so it is important to handle this missing‐by‐design problem appropriately. Consensus in the literature is to handle this problem with complete case analysis, as the missing data are assumed to arise under an appropriate missing at random mechanism that gives consistent estimates here. Specifically, the missing values can reasonably be assumed not to be related to the survival time. In this article, we investigate the potential for multiple imputation to handle this problem in a relevant study on survival after kidney transplantation, and show that it comprehensively outperforms complete case analysis on a range of measures. This is a particularly important finding in the medical context as imputing large amounts of missing data is often viewed with scepticism.

U2 - 10.1002/bimj.201800253

DO - 10.1002/bimj.201800253

M3 - Journal article

VL - 62

SP - 1192

EP - 1207

JO - Biometrical Journal

JF - Biometrical Journal

SN - 0323-3847

IS - 5

ER -