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:...
Published in: | BMC Medical Research Methodology |
---|---|
Main Authors: | , , , , , |
Format: | Text |
Language: | English |
Published: |
BioMed Central
2021
|
Subjects: | |
Online Access: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520620/ https://doi.org/10.1186/s12874-021-01408-5 |
id |
ftpubmed:oai:pubmedcentral.nih.gov:8520620 |
---|---|
record_format |
openpolar |
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 |
genre_facet |
Greenland |
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. |
op_rightsnorm |
CC0 PDM CC-BY |
op_doi |
https://doi.org/10.1186/s12874-021-01408-5 |
container_title |
BMC Medical Research Methodology |
container_volume |
21 |
container_issue |
1 |
_version_ |
1766020021843132416 |