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Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

BACKGROUND: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors fro...

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Autores principales: Neumair, Matthias, Kattan, Michael W., Freedland, Stephen J., Haese, Alexander, Guerrios-Rivera, Lourdes, De Hoedt, Amanda M., Liss, Michael A., Leach, Robin J., Boorjian, Stephen A., Cooperberg, Matthew R., Poyet, Cedric, Saba, Karim, Herkommer, Kathleen, Meissner, Valentin H., Vickers, Andrew J., Ankerst, Donna P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9306143/
https://www.ncbi.nlm.nih.gov/pubmed/35864460
http://dx.doi.org/10.1186/s12874-022-01674-x
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author Neumair, Matthias
Kattan, Michael W.
Freedland, Stephen J.
Haese, Alexander
Guerrios-Rivera, Lourdes
De Hoedt, Amanda M.
Liss, Michael A.
Leach, Robin J.
Boorjian, Stephen A.
Cooperberg, Matthew R.
Poyet, Cedric
Saba, Karim
Herkommer, Kathleen
Meissner, Valentin H.
Vickers, Andrew J.
Ankerst, Donna P.
author_facet Neumair, Matthias
Kattan, Michael W.
Freedland, Stephen J.
Haese, Alexander
Guerrios-Rivera, Lourdes
De Hoedt, Amanda M.
Liss, Michael A.
Leach, Robin J.
Boorjian, Stephen A.
Cooperberg, Matthew R.
Poyet, Cedric
Saba, Karim
Herkommer, Kathleen
Meissner, Valentin H.
Vickers, Andrew J.
Ankerst, Donna P.
author_sort Neumair, Matthias
collection PubMed
description BACKGROUND: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. METHODS: Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. RESULTS: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. CONCLUSION: Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01674-x.
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spelling pubmed-93061432022-07-23 Accommodating heterogeneous missing data patterns for prostate cancer risk prediction Neumair, Matthias Kattan, Michael W. Freedland, Stephen J. Haese, Alexander Guerrios-Rivera, Lourdes De Hoedt, Amanda M. Liss, Michael A. Leach, Robin J. Boorjian, Stephen A. Cooperberg, Matthew R. Poyet, Cedric Saba, Karim Herkommer, Kathleen Meissner, Valentin H. Vickers, Andrew J. Ankerst, Donna P. BMC Med Res Methodol Research BACKGROUND: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. METHODS: Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. RESULTS: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. CONCLUSION: Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01674-x. BioMed Central 2022-07-21 /pmc/articles/PMC9306143/ /pubmed/35864460 http://dx.doi.org/10.1186/s12874-022-01674-x Text en © The Author(s) 2022 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.
spellingShingle Research
Neumair, Matthias
Kattan, Michael W.
Freedland, Stephen J.
Haese, Alexander
Guerrios-Rivera, Lourdes
De Hoedt, Amanda M.
Liss, Michael A.
Leach, Robin J.
Boorjian, Stephen A.
Cooperberg, Matthew R.
Poyet, Cedric
Saba, Karim
Herkommer, Kathleen
Meissner, Valentin H.
Vickers, Andrew J.
Ankerst, Donna P.
Accommodating heterogeneous missing data patterns for prostate cancer risk prediction
title Accommodating heterogeneous missing data patterns for prostate cancer risk prediction
title_full Accommodating heterogeneous missing data patterns for prostate cancer risk prediction
title_fullStr Accommodating heterogeneous missing data patterns for prostate cancer risk prediction
title_full_unstemmed Accommodating heterogeneous missing data patterns for prostate cancer risk prediction
title_short Accommodating heterogeneous missing data patterns for prostate cancer risk prediction
title_sort accommodating heterogeneous missing data patterns for prostate cancer risk prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9306143/
https://www.ncbi.nlm.nih.gov/pubmed/35864460
http://dx.doi.org/10.1186/s12874-022-01674-x
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