A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies

BACKGROUND: Case-crossover studies have been widely used in various fields including pharmacoepidemiology. Vines and Farrington indicated in 2001 that when within-subject exposure dependency exists, conditional logistic regression can be biased. However, this bias has not been well studied. METHODS:...

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Published in:BMC Medical Research Methodology
Main Authors: Kubota, Kiyoshi, Kelly, Thu-Lan, Sato, Tsugumichi, Pratt, Nicole, Roughead, Elizabeth, Yamaguchi, Takuhiro
Format: Text
Language:English
Published: BioMed Central 2021
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Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520620/
https://doi.org/10.1186/s12874-021-01408-5
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spelling ftpubmed:oai:pubmedcentral.nih.gov:8520620 2023-05-15T16:30:18+02:00 A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies Kubota, Kiyoshi Kelly, Thu-Lan Sato, Tsugumichi Pratt, Nicole Roughead, Elizabeth Yamaguchi, Takuhiro 2021-10-17 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520620/ https://doi.org/10.1186/s12874-021-01408-5 en eng BioMed Central http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520620/ http://dx.doi.org/10.1186/s12874-021-01408-5 © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. CC0 PDM CC-BY BMC Med Res Methodol Research Text 2021 ftpubmed https://doi.org/10.1186/s12874-021-01408-5 2021-10-24T00:37:51Z BACKGROUND: Case-crossover studies have been widely used in various fields including pharmacoepidemiology. Vines and Farrington indicated in 2001 that when within-subject exposure dependency exists, conditional logistic regression can be biased. However, this bias has not been well studied. METHODS: We have extended findings by Vines and Farrington to develop a weighting method for the case-crossover study which removes bias from within-subject exposure dependency. Our method calculates the exposure probability at the case period in the case-crossover study which is used to weight the likelihood formulae presented by Greenland in 1999. We simulated data for the population with a disease where most patients receive a cyclic treatment pattern with within-subject exposure dependency but no time trends while some patients stop and start treatment. Finally, the method was applied to real-world data from Japan to study the association between celecoxib and peripheral edema and to study the association between selective serotonin reuptake inhibitor (SSRI) and hip fracture in Australia. RESULTS: When the simulated rate ratio of the outcome was 4.0 in a case-crossover study with no time-varying confounder, the proposed weighting method and the Mantel-Haenszel odds ratio reproduced the true rate ratio. When a time-varying confounder existed, the Mantel-Haenszel method was biased but the weighting method was not. When more than one control period was used, standard conditional logistic regression was biased either with or without time-varying confounding and the bias increased (up to 8.7) when the study period was extended. In real-world analysis with a binary exposure variable in Japan and Australia, the point estimate of the odds ratio (around 2.5 for the association between celecoxib and peripheral edema and around 1.6 between SSRI and hip fracture) by our weighting method was equal to the Mantel-Haenszel odds ratio and stable compared with standard conditional logistic regression. CONCLUSION: Case-crossover studies may ... Text Greenland PubMed Central (PMC) Greenland BMC Medical Research Methodology 21 1
institution Open Polar
collection PubMed Central (PMC)
op_collection_id ftpubmed
language English
topic Research
spellingShingle Research
Kubota, Kiyoshi
Kelly, Thu-Lan
Sato, Tsugumichi
Pratt, Nicole
Roughead, Elizabeth
Yamaguchi, Takuhiro
A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies
topic_facet Research
description BACKGROUND: Case-crossover studies have been widely used in various fields including pharmacoepidemiology. Vines and Farrington indicated in 2001 that when within-subject exposure dependency exists, conditional logistic regression can be biased. However, this bias has not been well studied. METHODS: We have extended findings by Vines and Farrington to develop a weighting method for the case-crossover study which removes bias from within-subject exposure dependency. Our method calculates the exposure probability at the case period in the case-crossover study which is used to weight the likelihood formulae presented by Greenland in 1999. We simulated data for the population with a disease where most patients receive a cyclic treatment pattern with within-subject exposure dependency but no time trends while some patients stop and start treatment. Finally, the method was applied to real-world data from Japan to study the association between celecoxib and peripheral edema and to study the association between selective serotonin reuptake inhibitor (SSRI) and hip fracture in Australia. RESULTS: When the simulated rate ratio of the outcome was 4.0 in a case-crossover study with no time-varying confounder, the proposed weighting method and the Mantel-Haenszel odds ratio reproduced the true rate ratio. When a time-varying confounder existed, the Mantel-Haenszel method was biased but the weighting method was not. When more than one control period was used, standard conditional logistic regression was biased either with or without time-varying confounding and the bias increased (up to 8.7) when the study period was extended. In real-world analysis with a binary exposure variable in Japan and Australia, the point estimate of the odds ratio (around 2.5 for the association between celecoxib and peripheral edema and around 1.6 between SSRI and hip fracture) by our weighting method was equal to the Mantel-Haenszel odds ratio and stable compared with standard conditional logistic regression. CONCLUSION: Case-crossover studies may ...
format Text
author Kubota, Kiyoshi
Kelly, Thu-Lan
Sato, Tsugumichi
Pratt, Nicole
Roughead, Elizabeth
Yamaguchi, Takuhiro
author_facet Kubota, Kiyoshi
Kelly, Thu-Lan
Sato, Tsugumichi
Pratt, Nicole
Roughead, Elizabeth
Yamaguchi, Takuhiro
author_sort Kubota, Kiyoshi
title A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies
title_short A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies
title_full A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies
title_fullStr A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies
title_full_unstemmed A novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies
title_sort novel weighting method to remove bias from within-subject exposure dependency in case-crossover studies
publisher BioMed Central
publishDate 2021
url http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520620/
https://doi.org/10.1186/s12874-021-01408-5
geographic Greenland
geographic_facet Greenland
genre Greenland
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op_source BMC Med Res Methodol
op_relation http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520620/
http://dx.doi.org/10.1186/s12874-021-01408-5
op_rights © The Author(s) 2021
https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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