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Modeling unmeasured baseline information in observational time-to-event data subject to delayed study entry
Unmeasured baseline information in left-truncated data situations frequently occurs in observational time-to-event analyses. For instance, a typical timescale in trials of antidiabetic treatment is “time since treatment initiation”, but individuals may have initiated treatment before the start of lo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248294/ https://www.ncbi.nlm.nih.gov/pubmed/36924264 http://dx.doi.org/10.1177/09622802231163334 |
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author | Stegherr, Regina Beyersmann, Jan Bramlage, Peter Bluhmki, Tobias |
author_facet | Stegherr, Regina Beyersmann, Jan Bramlage, Peter Bluhmki, Tobias |
author_sort | Stegherr, Regina |
collection | PubMed |
description | Unmeasured baseline information in left-truncated data situations frequently occurs in observational time-to-event analyses. For instance, a typical timescale in trials of antidiabetic treatment is “time since treatment initiation”, but individuals may have initiated treatment before the start of longitudinal data collection. When the focus is on baseline effects, one widespread approach is to fit a Cox proportional hazards model incorporating the measurements at delayed study entry. This has been criticized because of the potential time dependency of covariates. We tackle this problem by using a Bayesian joint model that combines a mixed-effects model for the longitudinal trajectory with a proportional hazards model for the event of interest incorporating the baseline covariate, possibly unmeasured in the presence of left truncation. The novelty is that our procedure is not used to account for non-continuously monitored longitudinal covariates in right-censored time-to-event studies, but to utilize these trajectories to make inferences about missing baseline measurements in left-truncated data. Simulating times-to-event depending on baseline covariates we also compared our proposal to a simpler two-stage approach which performed favorably. Our approach is illustrated by investigating the impact of baseline blood glucose levels on antidiabetic treatment failure using data from a German diabetes register. |
format | Online Article Text |
id | pubmed-10248294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-102482942023-06-09 Modeling unmeasured baseline information in observational time-to-event data subject to delayed study entry Stegherr, Regina Beyersmann, Jan Bramlage, Peter Bluhmki, Tobias Stat Methods Med Res Original Research Articles Unmeasured baseline information in left-truncated data situations frequently occurs in observational time-to-event analyses. For instance, a typical timescale in trials of antidiabetic treatment is “time since treatment initiation”, but individuals may have initiated treatment before the start of longitudinal data collection. When the focus is on baseline effects, one widespread approach is to fit a Cox proportional hazards model incorporating the measurements at delayed study entry. This has been criticized because of the potential time dependency of covariates. We tackle this problem by using a Bayesian joint model that combines a mixed-effects model for the longitudinal trajectory with a proportional hazards model for the event of interest incorporating the baseline covariate, possibly unmeasured in the presence of left truncation. The novelty is that our procedure is not used to account for non-continuously monitored longitudinal covariates in right-censored time-to-event studies, but to utilize these trajectories to make inferences about missing baseline measurements in left-truncated data. Simulating times-to-event depending on baseline covariates we also compared our proposal to a simpler two-stage approach which performed favorably. Our approach is illustrated by investigating the impact of baseline blood glucose levels on antidiabetic treatment failure using data from a German diabetes register. SAGE Publications 2023-03-16 2023-05 /pmc/articles/PMC10248294/ /pubmed/36924264 http://dx.doi.org/10.1177/09622802231163334 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Articles Stegherr, Regina Beyersmann, Jan Bramlage, Peter Bluhmki, Tobias Modeling unmeasured baseline information in observational time-to-event data subject to delayed study entry |
title | Modeling unmeasured baseline information in observational
time-to-event data subject to delayed study entry |
title_full | Modeling unmeasured baseline information in observational
time-to-event data subject to delayed study entry |
title_fullStr | Modeling unmeasured baseline information in observational
time-to-event data subject to delayed study entry |
title_full_unstemmed | Modeling unmeasured baseline information in observational
time-to-event data subject to delayed study entry |
title_short | Modeling unmeasured baseline information in observational
time-to-event data subject to delayed study entry |
title_sort | modeling unmeasured baseline information in observational
time-to-event data subject to delayed study entry |
topic | Original Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248294/ https://www.ncbi.nlm.nih.gov/pubmed/36924264 http://dx.doi.org/10.1177/09622802231163334 |
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