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Comparison of an ensemble machine learning model to a Cox regression model to predict colorectal cancer risk among people with HIV using retrospective nationwide cohort data in Sweden : a study protocol

Nilsson, Josefin ; Deng, Yunyang ; Elvstam, Olof LU orcid ; Killander-Möller, Isabela ; Lei, Jiayao ; Mansson, Fredrik LU ; Naucler, Pontus LU ; Nygren, Jonas ; Ruhe-van der Werff, Suzanne and Wagner, Philippe LU , et al. (2026) In BMJ Open 16(9). p.1-8
Abstract

INTRODUCTION: There are currently no colorectal cancer (CRC) screening recommendations specifically outlined for people with HIV (PWH). Screening measures used for people without HIV (PWoH) have been previously discussed as sufficient for use among PWH, despite observations of higher CRC prevalence and CRC reportedly appearing at earlier ages among PWH in comparison to PWoH. Machine learning (ML) methods are regarded as robust approaches that may enhance predictive performance, particularly in the context of complex or high-dimensional data. This study aims to develop an ensemble ML model to predict CRC risk in PWH using comprehensive nationwide datasets. The model's predictive performance will be evaluated and compared with a baseline... (More)

INTRODUCTION: There are currently no colorectal cancer (CRC) screening recommendations specifically outlined for people with HIV (PWH). Screening measures used for people without HIV (PWoH) have been previously discussed as sufficient for use among PWH, despite observations of higher CRC prevalence and CRC reportedly appearing at earlier ages among PWH in comparison to PWoH. Machine learning (ML) methods are regarded as robust approaches that may enhance predictive performance, particularly in the context of complex or high-dimensional data. This study aims to develop an ensemble ML model to predict CRC risk in PWH using comprehensive nationwide datasets. The model's predictive performance will be evaluated and compared with a baseline Cox proportional regression model. The better-performing method will be implemented to develop a CRC risk prediction model with the aim of personalising screening recommendations for PWH.

METHODS AND ANALYSIS: The study population will include all PWH and PWoH born between 1940 and 2008, aged 18 or older and living in Sweden sometime between 1983 and 2024. The study population will be linked to six nationwide demographic and healthcare registers. Follow-up will continue until the first incident of CRC, emigration or death. The outcome of interest is CRC. PWH will be matched to negative controls 1:10. A Cox regression analysis will be completed first, and the results will be used as a baseline comparison to the ensemble ML results. A range of ML methods will be used to develop the ensemble model using stacking.

ETHICS AND DISSEMINATION: This study has ethical approval from the Regional Ethical Committee in Sweden (Dnr: 2024-04185-02, 2024-06783-02, 2023-00191-01, 2022-02897-02, 2022-05624-01, 2018/11-31/2). Given that the study is retrospective and register-based, using only pseudonymised data, there are minimal physical, psychological or privacy risks to included individuals. All results will be presented at the population level with no possibility of identification. The results of this study will be submitted for publication in a peer-reviewed journal.

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Contribution to journal
publication status
published
subject
keywords
Humans, Colorectal Neoplasms/epidemiology, Sweden/epidemiology, HIV Infections/complications, Proportional Hazards Models, Machine Learning, Retrospective Studies, Ensemble Learning, Risk Assessment/methods, Research Design, Adult, Predictive Learning Models, Female, Male, Risk Factors, Early Detection of Cancer/methods
in
BMJ Open
volume
16
issue
9
article number
e108693
pages
1 - 8
publisher
BMJ Publishing Group
external identifiers
  • pmid:42716689
ISSN
2044-6055
DOI
10.1136/bmjopen-2025-108693
language
English
LU publication?
yes
additional info
© Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY. Published by BMJ Group.
id
b877bbcb-eb8a-4660-84a2-69e50498faf2
date added to LUP
2026-09-10 16:53:48
date last changed
2026-09-11 07:26:25
@article{b877bbcb-eb8a-4660-84a2-69e50498faf2,
  abstract     = {{<p>INTRODUCTION: There are currently no colorectal cancer (CRC) screening recommendations specifically outlined for people with HIV (PWH). Screening measures used for people without HIV (PWoH) have been previously discussed as sufficient for use among PWH, despite observations of higher CRC prevalence and CRC reportedly appearing at earlier ages among PWH in comparison to PWoH. Machine learning (ML) methods are regarded as robust approaches that may enhance predictive performance, particularly in the context of complex or high-dimensional data. This study aims to develop an ensemble ML model to predict CRC risk in PWH using comprehensive nationwide datasets. The model's predictive performance will be evaluated and compared with a baseline Cox proportional regression model. The better-performing method will be implemented to develop a CRC risk prediction model with the aim of personalising screening recommendations for PWH.</p><p>METHODS AND ANALYSIS: The study population will include all PWH and PWoH born between 1940 and 2008, aged 18 or older and living in Sweden sometime between 1983 and 2024. The study population will be linked to six nationwide demographic and healthcare registers. Follow-up will continue until the first incident of CRC, emigration or death. The outcome of interest is CRC. PWH will be matched to negative controls 1:10. A Cox regression analysis will be completed first, and the results will be used as a baseline comparison to the ensemble ML results. A range of ML methods will be used to develop the ensemble model using stacking.</p><p>ETHICS AND DISSEMINATION: This study has ethical approval from the Regional Ethical Committee in Sweden (Dnr: 2024-04185-02, 2024-06783-02, 2023-00191-01, 2022-02897-02, 2022-05624-01, 2018/11-31/2). Given that the study is retrospective and register-based, using only pseudonymised data, there are minimal physical, psychological or privacy risks to included individuals. All results will be presented at the population level with no possibility of identification. The results of this study will be submitted for publication in a peer-reviewed journal.</p>}},
  author       = {{Nilsson, Josefin and Deng, Yunyang and Elvstam, Olof and Killander-Möller, Isabela and Lei, Jiayao and Mansson, Fredrik and Naucler, Pontus and Nygren, Jonas and Ruhe-van der Werff, Suzanne and Wagner, Philippe and Yilmaz, Aylin and Brännström, Johanna and Boman, Magnus and Carlander, Christina}},
  issn         = {{2044-6055}},
  keywords     = {{Humans; Colorectal Neoplasms/epidemiology; Sweden/epidemiology; HIV Infections/complications; Proportional Hazards Models; Machine Learning; Retrospective Studies; Ensemble Learning; Risk Assessment/methods; Research Design; Adult; Predictive Learning Models; Female; Male; Risk Factors; Early Detection of Cancer/methods}},
  language     = {{eng}},
  month        = {{09}},
  number       = {{9}},
  pages        = {{1--8}},
  publisher    = {{BMJ Publishing Group}},
  series       = {{BMJ Open}},
  title        = {{Comparison of an ensemble machine learning model to a Cox regression model to predict colorectal cancer risk among people with HIV using retrospective nationwide cohort data in Sweden : a study protocol}},
  url          = {{http://dx.doi.org/10.1136/bmjopen-2025-108693}},
  doi          = {{10.1136/bmjopen-2025-108693}},
  volume       = {{16}},
  year         = {{2026}},
}