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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...

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Autores principales: Stegherr, Regina, Beyersmann, Jan, Bramlage, Peter, Bluhmki, Tobias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
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.
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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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