Vallejos Group
Understanding heterogeneity in complex biomedical data
About
While biomedical data sometimes classifies as “big data” (where the number of samples and/or variables is large), complexity is its most prominent feature. This arises from a combination of different sources of heterogeneity: heterogeneity across individuals in a population (e.g. response to treatment), heterogeneity in terms of the type of data we collect (e.g. health records & genomics) and heterogeneity that is introduced by the data collection process (e.g. measurement error).
We focus on the development of novel statistical methodology to address and study these sources of heterogeneity. This is a highly multidisciplinary task: from the understanding of complex biomedical problems and technologies, to the development of new methodology and the implementation of open-source analysis tools. Our current research focuses on two areas of application. Firstly, single-cell RNA-sequencing, a cutting-edge experimental technique that allows genome-wide quantification of gene expression on a cell-by-cell basis. Secondly, electronic health records research, to develop predictive models based on observational data that is routinely collected by health providers (e.g. NHS). Developing computational tools that can make full advantage of the rich information provided by these data sources is ought to improve our understanding of health and disease, playing an important role in precision medicine initiatives.
News
| Jan 31, 2024 | Nathan has passed his viva! |
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| May 26, 2022 | Cata got tenure after a successful ESAT review. |
| May 6, 2022 | Alan has passed his viva! |
| Oct 1, 2021 | Linda and Elena have joined the group! |
Selected Publications
- NPJ DMDevelopment and assessment of a machine learning tool for predicting emergency admission in Scotlandnpj Digital Medicine Oct 2024
Emergency admissions (EA), where a patient requires urgent in-hospital care, are a major challenge for healthcare systems. The development of risk prediction models can partly alleviate this problem by supporting primary care interventions and public health planning. Here, we introduce SPARRAv4, a predictive score for EA risk that will be deployed nationwide in Scotland. SPARRAv4 was derived using supervised and unsupervised machine-learning methods applied to routinely collected electronic health records from approximately 4.8M Scottish residents (2013-18). We demonstrate improvements in discrimination and calibration with respect to previous scores deployed in Scotland, as well as stability over a 3-year timeframe. Our analysis also provides insights about the epidemiology of EA risk in Scotland, by studying predictive performance across different population sub-groups and reasons for admission, as well as by quantifying the effect of individual input features. Finally, we discuss broader challenges including reproducibility and how to safely update risk prediction models that are already deployed at population level.
@article{Liley_2024, abbr = {NPJ DM}, bibtex_show = {true}, selected = {true}, title = {Development and assessment of a machine learning tool for predicting emergency admission in Scotland}, volume = {7}, issn = {2398-6352}, url = {http://dx.doi.org/10.1038/s41746-024-01250-1}, doi = {10.1038/s41746-024-01250-1}, number = {1}, journal = {npj Digital Medicine}, publisher = {Springer Science and Business Media LLC}, author = {Liley, James and Bohner, Gergo and Emerson, Samuel R. and Mateen, Bilal A. and Borland, Katie and Carr, David and Heald, Scott and Oduro, Samuel D. and Ireland, Jill and Moffat, Keith and Porteous, Rachel and Riddell, Stephen and Rogers, Simon and Thoma, Ioanna and Cunningham, Nathan and Holmes, Chris and Payne, Katrina and Vollmer, Sebastian J. and Vallejos, Catalina A. and Aslett, Louis J. M.}, year = {2024}, month = oct } - AI & EthicsEthical considerations of use of hold-out sets in clinical prediction model managementAI and Ethics Sep 2024
Clinical prediction models are statistical or machine learning models used to quantify the risk of a certain health outcome using patient data. These can then inform potential interventions on patients, causing an effect called performative prediction: predictions inform interventions which influence the outcome they were trying to predict, leading to a potential underestimation of risk in some patients if a model is updated on this data. One suggested resolution to this is the use of hold-out sets, in which a set of patients do not receive model derived risk scores, such that a model can be safely retrained. We present an overview of clinical and research ethics regarding potential implementation of hold-out sets for clinical prediction models in health settings. We focus on the ethical principles of beneficence, non-maleficence, autonomy and justice. We also discuss informed consent, clinical equipoise, and truth-telling. We present illustrative cases of potential hold-out set implementations and discuss statistical issues arising from different hold-out set sampling methods. We also discuss differences between hold-out sets and randomised control trials, in terms of ethics and statistical issues. Finally, we give practical recommendations for researchers interested in the use hold-out sets for clinical prediction models.
@article{Chislett_2024, abbr = {AI & Ethics}, bibtex_show = {true}, selected = {true}, title = {Ethical considerations of use of hold-out sets in clinical prediction model management}, issn = {2730-5961}, url = {http://dx.doi.org/10.1007/s43681-024-00561-z}, doi = {10.1007/s43681-024-00561-z}, journal = {AI and Ethics}, publisher = {Springer Science and Business Media LLC}, author = {Chislett, Louis and Aslett, Louis J. M. and Davies, Alisha R. and Vallejos, Catalina A. and Liley, James}, year = {2024}, month = sep } - medRxivBlood-based DNA methylation study of alcohol consumptionFeb 2024
Alcohol consumption is an important risk factor for multiple diseases. It is typically assessed via self-report, which is open to measurement error and bias. Instead, molecular data such as blood-based DNA methylation (DNAm) could be used to derive a more objective measure of alcohol consumption by incorporating information from cytosine-phosphate-guanine (CpG) sites known to be linked to the trait. Here, we explore the epigenetic architecture of self-reported weekly units of alcohol consumption in the Generation Scotland study. We first create a blood-based epigenetic score (EpiScore) of alcohol consumption using elastic net penalised linear regression. We explore the effect of pre-filtering for CpG features ahead of elastic net, as well as differential patterns by sex and by units consumed in the last week relative to an average week. The final EpiScore was trained on 16,717 individuals and tested in four external cohorts: the Lothian Birth Cohorts (LBC) of 1921 and 1936, the Sister Study, and the Avon Longitudinal Study of Parents and Children (total N across studies > 10,000). The maximum Pearson correlation between the EpiScore and self-reported alcohol consumption within cohort ranged from 0.41 to 0.53. In LBC1936, higher EpiScore levels had significant associations with poorer global brain imaging metrics, whereas self-reported alcohol consumption did not. Finally, we identified two novel CpG loci via a Bayesian penalized regression epigenome-wide association study (EWAS) of alcohol consumption. Together, these findings show how DNAm can objectively characterize patterns of alcohol consumption that associate with brain health, unlike self-reported estimates.
@article{Bernabeu_2024, abbr = {medRxiv}, bibtex_show = {true}, selected = {true}, title = {Blood-based DNA methylation study of alcohol consumption}, url = {http://dx.doi.org/10.1101/2024.02.26.24303397}, doi = {10.1101/2024.02.26.24303397}, publisher = {Cold Spring Harbor Laboratory}, author = {Bernabeu, Elena and Chybowska, Aleksandra D and Kresovich, Jacob K. and Suderman, Matthew and McCartney, Daniel L and Hillary, Robert F and Corley, Janie and Valdés-Hernández, Maria Del C. and Muñoz Maniega, Susana and Bastin, Mark E. and Wardlaw, Joanna M. and Xu, Zongli and Sandler, Dale P. and Campbell, Archie and Harris, Sarah E and McIntosh, Andrew M and Taylor, Jack A. and Yousefi, Paul and Cox, Simon R and Evans, Kathryn L and Robinson, Matthew R and Vallejos, Catalina A and Marioni, Riccardo E}, year = {2024}, month = feb } - PLOS DHDifferential behaviour of a risk score for emergency hospital admission by demographics in Scotland—A retrospective studyPLOS Digital Health Dec 2024
@article{Thoma_2024, abbr = {PLOS DH}, bibtex_show = {true}, selected = {true}, title = {Differential behaviour of a risk score for emergency hospital admission by demographics in Scotland—A retrospective study}, volume = {3}, issn = {2767-3170}, url = {http://dx.doi.org/10.1371/journal.pdig.0000675}, doi = {10.1371/journal.pdig.0000675}, number = {12}, journal = {PLOS Digital Health}, publisher = {Public Library of Science (PLoS)}, author = {Thoma, Ioanna and Rogers, Simon and Ireland, Jillian and Porteous, Rachel and Borland, Katie and Vallejos, Catalina A. and Aslett, Louis J. M. and Liley, James}, editor = {Kuo, Po-Chih}, year = {2024}, month = dec, pages = {e0000675}, abtract = {The Scottish Patients at Risk of Re-Admission and Admission (SPARRA) score predicts individual risk of emergency hospital admission for approximately 80% of the Scottish population. It was developed using routinely collected electronic health records, and is used by primary care practitioners to inform anticipatory care, particularly for individuals with high healthcare needs. We comprehensively assess the SPARRA score across population subgroups defined by age, sex, ethnicity, socioeconomic deprivation, and geographic location. For these subgroups, we consider differences in overall performance, score distribution, and false positive and negative rates, using causal methods to identify effects mediated through age, sex, and deprivation. We show that the score is well-calibrated across subgroups, but that rates of false positives and negatives vary widely, mediated by various causes including variability in demographic characteristics, admission reasons, and potentially differential data availability. Our work assists practitioners in the application and interpretation of the SPARRA score in population subgroups.} } - BiometA review on statistical and machine learning competing risks methodsBiometrical Journal Feb 2024
Abstract When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high-dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed in applied settings. This article aims to aid the uptake of such methods by providing a condensed compendium of CR survival methods with a unified notation and interpretation across approaches. We highlight available software and, when possible, demonstrate their usage via reproducible R vignettes. Moreover, we discuss two major concerns that can affect benchmark studies in this context: the choice of performance metrics and reproducibility.
@article{https://doi.org/10.1002/bimj.202300060, abbr = {Biomet}, bibtex_show = {true}, author = {Monterrubio-Gómez, Karla and Constantine-Cooke, Nathan and Vallejos, Catalina A.}, title = {A review on statistical and machine learning competing risks methods}, journal = {Biometrical Journal}, volume = {66}, number = {2}, pages = {2300060}, keywords = {competing risks, risk prediction, survival analysis, time-to-event data}, doi = {https://doi.org/10.1002/bimj.202300060}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202300060}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202300060}, year = {2024}, month = feb, selected = {true} }