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1. Digital Health Management During and Beyond the COVID-19 Pandemic: Opportunities, Barriers, and Recommendations

Author

Inkster, Becky, O’Brien, Ross, Selby, Emma, Joshi, Smriti, Subramanian, Vinod, Kadaba, Madhura, Schroeder, Knut, Godson, Suzi, Comley, Kerstyn, Vollmer, Sebastian J, and Mateen, Bilal A

Subjects
Psychology, BF1-990
Abstract

During the coronavirus disease (COVID-19) crisis, digital technologies have become a major route for accessing remote care. Therefore, the need to ensure that these tools are safe and effective has never been greater. We raise five calls to action to ensure the safety, availability, and long-term sustainability of these technologies: (1) due diligence: remove harmful health apps from app stores; (2) data insights: use relevant health data insights from high-quality digital tools to inform the greater response to COVID-19; (3) freely available resources: make high-quality digital health tools available without charge, where possible, and for as long as possible, especially to those who are most vulnerable; (4) digital transitioning: transform conventional offline mental health services to make them digitally available; and (5) population self-management: encourage governments and insurers to work with developers to look at how digital health management could be subsidized or funded. We believe this should be carried out at the population level, rather than at a prescription level.

Published
2020
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3. The value of standards for health datasets in artificial intelligence-based applications

Author

Arora, Anmol, Alderman, Joseph E., Palmer, Joanne, Ganapathi, Shaswath, Laws, Elinor, McCradden, Melissa D., Oakden-Rayner, Lauren, Pfohl, Stephen R., Ghassemi, Marzyeh, McKay, Francis, Treanor, Darren, Rostamzadeh, Negar, Mateen, Bilal, Gath, Jacqui, Adebajo, Adewole O., Kuku, Stephanie, Matin, Rubeta, Heller, Katherine, Sapey, Elizabeth, Sebire, Neil J., Cole-Lewis, Heather, Calvert, Melanie, Denniston, Alastair, and Liu, Xiaoxuan

Published
2023
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4. Faking feature importance: A cautionary tale on the use of differentially-private synthetic data

Author

Giles, Oscar, Hosseini, Kasra, Mingas, Grigorios, Strickson, Oliver, Bowler, Louise, Smith, Camila Rangel, Wilde, Harrison, Lim, Jen Ning, Mateen, Bilal, Amarasinghe, Kasun, Ghani, Rayid, Heppenstall, Alison, Lomax, Nik, Malleson, Nick, O'Reilly, Martin, and Vollmerteke, Sebastian

Subjects
Computer Science - Machine Learning, Statistics - Applications
Abstract

Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing. However, for many applications, synthetic data has been shown to have limited utility when used to train predictive models. One promising potential application of these data is in the exploratory phase of the machine learning workflow, which involves understanding, engineering and selecting features. This phase often involves considerable time, and depends on the availability of data. There would be substantial value in synthetic data that permitted these steps to be carried out while, for example, data access was being negotiated, or with fewer information governance restrictions. This paper presents an empirical analysis of the agreement between the feature importance obtained from raw and from synthetic data, on a range of artificially generated and real-world datasets (where feature importance represents how useful each feature is when predicting a the outcome). We employ two differentially-private methods to produce synthetic data, and apply various utility measures to quantify the agreement in feature importance as this varies with the level of privacy. Our results indicate that synthetic data can sometimes preserve several representations of the ranking of feature importance in simple settings but their performance is not consistent and depends upon a number of factors. Particular caution should be exercised in more nuanced real-world settings, where synthetic data can lead to differences in ranked feature importance that could alter key modelling decisions. This work has important implications for developing synthetic versions of highly sensitive data sets in fields such as finance and healthcare., Comment: 27 pages, 8 figures

Published
2022

5. Model updating after interventions paradoxically introduces bias

Author

Liley, James, Emerson, Samuel R, Mateen, Bilal A, Vallejos, Catalina A, Aslett, Louis J M, and Vollmer, Sebastian J

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Machine learning is increasingly being used to generate prediction models for use in a number of real-world settings, from credit risk assessment to clinical decision support. Recent discussions have highlighted potential problems in the updating of a predictive score for a binary outcome when an existing predictive score forms part of the standard workflow, driving interventions. In this setting, the existing score induces an additional causative pathway which leads to miscalibration when the original score is replaced. We propose a general causal framework to describe and address this problem, and demonstrate an equivalent formulation as a partially observed Markov decision process. We use this model to demonstrate the impact of such `naive updating' when performed repeatedly. Namely, we show that successive predictive scores may converge to a point where they predict their own effect, or may eventually tend toward a stable oscillation between two values, and we argue that neither outcome is desirable. Furthermore, we demonstrate that even if model-fitting procedures improve, actual performance may worsen. We complement these findings with a discussion of several potential routes to overcome these issues., Comment: Sections of this preprint on 'Successive adjuvancy' (section 4, theorem 2, figures 4,5, and associated discussions) were not included in the originally submitted version of this paper due to length. This material does not appear in the published version of this manuscript, and the reader should be aware that these sections did not undergo peer review

Published
2020

6. A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services

Author

Wilde, Harrison, Chen, Lucia Lushi, Nguyen, Austin, Kimpel, Zoe, Sidgwick, Joshua, De Unanue, Adolfo, Veronese, Davide, Mateen, Bilal, Ghani, Rayid, and Vollmer, Sebastian

Subjects
Statistics - Applications, Statistics - Machine Learning
Abstract

Rough sleeping is a chronic problem faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link, a UK-based charity, in developing a data-driven approach to assess the quality of incoming alerts from members of the public aimed at connecting people sleeping rough on the streets with outreach service providers. Alerts are prioritised based on the predicted likelihood of successfully connecting with the rough sleeper, helping to address capacity limitations and to quickly, effectively, and equitably process all of the alerts that they receive. Initial evaluation concludes that our approach increases the rate at which rough sleepers are found following a referral by at least 15\% based on labelled data, implying a greater overall increase when the alerts with unknown outcomes are considered, and suggesting the benefit in a trial taking place over a longer period to assess the models in practice. The discussion and modelling process is done with careful considerations of ethics, transparency and explainability due to the sensitive nature of the data in this context and the vulnerability of the people that are affected., Comment: 10 pages, 5 figures, 5 tables

Published
2020

7. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Author

Abbasizanjani, Hoda, Ahmed, Nida, Ahmed, Badar, Akbari, Ashley, Akinoso-Imran, Abdul Qadr, Allara, Elias, Allery, Freya, Angelantonio, Emanuele Di, Ashworth, Mark, Ayyar-Gupta, Vandana, Babu-Narayan, Sonya, Bacon, Seb, Ball, Steve, Banerjee, Ami, Barber, Mark, Barrett, Jessica, Bennie, Marion, Berry, Colin, Beveridge, Jennifer, Birney, Ewan, Bojanić, Lana, Bolton, Thomas, Bone, Anna, Boyle, Jon, Braithwaite, Tasanee, Bray, Ben, Briffa, Norman, Brind, David, Brown, Katherine, Buch, Maya, Canoy, Dexter, Caputo, Massimo, Carragher, Raymond, Carson, Alan, Cezard, Genevieve, Chang, Jen-Yu Amy, Cheema, Kate, Chin, Richard, Chudasama, Yogini, Cooper, Jennifer, Copland, Emma, Crallan, Rebecca, Cripps, Rachel, Cromwell, David, Curcin, Vasa, Curry, Gwenetta, Dale, Caroline, Danesh, John, Das-Munshi, Jayati, Dashtban, Ashkan, Davies, Alun, Davies, Joanna, Davies, Gareth, Davies, Neil, Day, Joshua, Delmestri, Antonella, Denaxas, Spiros, Denholm, Rachel, Dennis, John, Denniston, Alastair, Deo, Salil, Dhillon, Baljean, Docherty, Annemarie, Dong, Tim, Douiri, Abdel, Downs, Johnny, Dregan, Alexandru, Ellins, Elizabeth A, Elwenspoek, Martha, Falck, Fabian, Falter, Florian, Fan, Yat Yi, Firth, Joseph, Fraser, Lorna, Friebel, Rocco, Gavrieli, Amir, Gerstung, Moritz, Gilbert, Ruth, Gillies, Clare, Glickman, Myer, Goldacre, Ben, Goldacre, Raph, Greaves, Felix, Green, Mark, Grieco, Luca, Griffiths, Rowena, Gurdasani, Deepti, Halcox, Julian, Hall, Nick, Hama, Tuankasfee, Handy, Alex, Hansell, Anna, Hardelid, Pia, Hardy, Flavien, Harris, Daniel, Harrison, Camille, Harron, Katie, Hassaine, Abdelaali, Hassan, Lamiece, Healey, Russell, Hemingway, Harry, Henderson, Angela, Herz, Naomi, Heyl, Johannes, Hidajat, Mira, Higginson, Irene, Hinchliffe, Rosie, Hippisley-Cox, Julia, Ho, Frederick, Hocaoglu, Mevhibe, Hollings, Sam, Horne, Elsie, Hughes, David, Humberstone, Ben, Inouye, Mike, Ip, Samantha, Islam, Nazrul, Jackson, Caroline, Jenkins, David, Jiang, Xiyun, Johnson, Shane, Kadam, Umesh, Kallis, Costas, Karim, Zainab, Kasan, Jake, Katsoulis, Michalis, Kavanagh, Kim, Kee, Frank, Keene, Spencer, Kent, Seamus, Khalid, Sara, Khawaja, Anthony, Khunti, Kamlesh, Killick, Richard, Kinnear, Deborah, Knight, Rochelle, Kolamunnage-Dona, Ruwanthi, Kontopantelis, Evan, Kurdi, Amanj, Lacey, Ben, Lai, Alvina, Lambarth, Andrew, Larzjan, Milad Nazarzadeh, Lawler, Deborah, Lawrence, Thomas, Lawson, Claire, Li, Qiuju, Li, Ken, Llinares, Miguel Bernabeu, Lorgelly, Paula, Lowe, Deborah, Lyons, Jane, Lyons, Ronan, Machado, Pedro, Macleod, Mary Joan, Macleod, John, Malgapo, Evaleen, Mamas, Mamas, Mamouei, Mohammad, Manohar, Sinduja, Mapeta, Rutendo, Martelli, Javiera Leniz, Martos, David Moreno, Mateen, Bilal, McCarthy, Aoife, Melville, Craig, Milton, Rebecca, Mizani, Mehrdad, Moncusi, Marta Pineda, Morales, Daniel, Mordi, Ify, Morrice, Lynn, Morris, Carole, Morris, Eva, Mu, Yi, Mueller, Tanja, Murdock, Lars, Nafilyan, Vahé, Nicholson, George, Nikiphorou, Elena, Nolan, John, Norris, Tom, Norris, Ruth, North, Laura, North, Teri-Louise, O'Connell, Dan, Oliver, Dominic, Oluyase, Adejoke, Olvera-Barrios, Abraham, Omigie, Efosa, Onida, Sarah, Padmanabhan, Sandosh, Palmer, Tom, Pasea, Laura, Patel, Riyaz, Payne, Rupert, Pell, Jill, Petitjean, Carmen, Pherwani, Arun, Pickrell, Owen, Pierotti, Livia, Pirmohamed, Munir, Priedon, Rouven, Prieto-Alhambra, Dani, Proudfoot, Alastair, Quinn, Terry, Quint, Jennifer, Raffetti, Elena, Rahimi, Kazem, Rao, Shishir, Razieh, Cameron, Roberts, Brian, Rogers, Caroline, Rossdale, Jennifer, Salim, Safa, Samani, Nilesh, Sattar, Naveed, Schnier, Christian, Schwartz, Roy, Selby, David, Seminog, Olena, Shabnam, Sharmin, Shah, Ajay, Shelton, Jon, Sheppard, James, Sinha, Shubhra, Skrypak, Mirek, Slapkova, Martina, Sleeman, Katherine, Smith, Craig, Sofat, Reecha, Sosenko, Filip, Sperrin, Matthew, Steeg, Sarah, Sterne, Jonathan, Stoica, Serban, Sudell, Maria, Sudlow, Cathie, Sun, Luanluan, Suseeladevi, Arun Karthikeyan, Sweeting, Michael, Sydes, Matt, Takhar, Rohan, Tang, Howard, Thygesen, Johan, Tilston, George, Tochel, Claire, Toit, Clea du, Tomlinson, Christopher, Toms, Renin, Torabi, Fatemeh, Torralbo, Ana, Townson, Julia, Tufail, Adnan, Tungamirai, Tapiwa, Varma, Susheel, Vollmer, Sebastian, Walker, Venexia, Wang, Tianxiao, Wang, Huan, Warwick, Alasdair, Watkinson, Ruth, Watson, Harry, Whiteley, William, Whittaker, Hannah, Wilde, Harry, Wilkinson, Tim, Williams, Gareth, Williams, Michelle, Williams, Richard, Withnell, Eloise, Wolfe, Charles, Wood, Angela, Wright, Lucy, Wu, Honghan, Wu, Jinge, Wu, Jianhua, Yates, Tom, Zaccardi, Francesco, Zhang, Haoting, Zhang, Huayu, Zuccolo, Luisa, Thygesen, Johan H, Mizani, Mehrdad A, Banerjee, Amitava, Lai, Alvina G, Li, Kezhi, Mateen, Bilal A, Sterne, Jonathan A C, Pagel, Christina, and Whiteley, William N

Published
2022
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9. Machine learning and AI research for Patient Benefit: 20 Critical Questions on Transparency, Replicability, Ethics and Effectiveness

Author

Vollmer, Sebastian, Mateen, Bilal A., Bohner, Gergo, Király, Franz J, Ghani, Rayid, Jonsson, Pall, Cumbers, Sarah, Jonas, Adrian, McAllister, Katherine S. L., Myles, Puja, Granger, David, Birse, Mark, Branson, Richard, Moons, Karel GM, Collins, Gary S, Ioannidis, John P. A., Holmes, Chris, and Hemingway, Harry

Subjects
Computer Science - Computers and Society, Computer Science - Machine Learning, Statistics - Applications, Statistics - Machine Learning, 68T01
Abstract

Machine learning (ML), artificial intelligence (AI) and other modern statistical methods are providing new opportunities to operationalize previously untapped and rapidly growing sources of data for patient benefit. Whilst there is a lot of promising research currently being undertaken, the literature as a whole lacks: transparency; clear reporting to facilitate replicability; exploration for potential ethical concerns; and, clear demonstrations of effectiveness. There are many reasons for why these issues exist, but one of the most important that we provide a preliminary solution for here is the current lack of ML/AI- specific best practice guidance. Although there is no consensus on what best practice looks in this field, we believe that interdisciplinary groups pursuing research and impact projects in the ML/AI for health domain would benefit from answering a series of questions based on the important issues that exist when undertaking work of this nature. Here we present 20 questions that span the entire project life cycle, from inception, data analysis, and model evaluation, to implementation, as a means to facilitate project planning and post-hoc (structured) independent evaluation. By beginning to answer these questions in different settings, we can start to understand what constitutes a good answer, and we expect that the resulting discussion will be central to developing an international consensus framework for transparent, replicable, ethical and effective research in artificial intelligence (AI-TREE) for health., Comment: 25 pages, 2 boxes, 1 figure

Published
2018

10. NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature

Author

Király, Franz J, Mateen, Bilal, and Sonabend, Raphael

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Objective: To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature. Data Sources: Papers published in the Neural Information Processing Systems (NeurIPS, n\'ee NIPS) journal where the official record showed a 2017 year of publication. Eligibility Criteria: Studies reporting a (semi-)supervised model, or pre-processing fused with (semi-)supervised models for tabular data. Study Appraisal: Three reviewers applied the assessment criteria to determine argumentative completeness. The criteria were split into three groups, including: experiments (e.g real and/or synthetic data), baselines (e.g uninformed and/or state-of-art) and quantitative comparison (e.g. performance quantifiers with confidence intervals and formal comparison of the algorithm against baselines). Results: Of the 121 eligible manuscripts (from the sample of 679 abstracts), 99\% used real-world data and 29\% used synthetic data. 91\% of manuscripts did not report an uninformed baseline and 55\% reported a state-of-art baseline. 32\% reported confidence intervals for performance but none provided references or exposition for how these were calculated. 3\% reported formal comparisons. Limitations: The use of one journal as the primary information source may not be representative of all ML/AI literature. However, the NeurIPS conference is recognised to be amongst the top tier concerning ML/AI studies, so it is reasonable to consider its corpus to be representative of high-quality research. Conclusion: Using the 2017 sample of the NeurIPS supervised learning corpus as an indicator for the quality and trustworthiness of current ML/AI research, it appears that complete argumentative chains in demonstrations of algorithmic effectiveness are rare.

Published
2018

12. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Author

Vasey, Baptiste, Nagendran, Myura, Campbell, Bruce, Clifton, David A., Collins, Gary S., Denaxas, Spiros, Denniston, Alastair K., Faes, Livia, Geerts, Bart, Ibrahim, Mudathir, Liu, Xiaoxuan, Mateen, Bilal A., Mathur, Piyush, McCradden, Melissa D., Morgan, Lauren, Ordish, Johan, Rogers, Campbell, Saria, Suchi, Ting, Daniel S. W., Watkinson, Peter, Weber, Wim, Wheatstone, Peter, and McCulloch, Peter

Published
2022
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13. Probabilistic supervised learning

Author

Gressmann, Frithjof, Király, Franz J., Mateen, Bilal, and Oberhauser, Harald

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Statistics Theory, Statistics - Methodology
Abstract

Predictive modelling and supervised learning are central to modern data science. With predictions from an ever-expanding number of supervised black-box strategies - e.g., kernel methods, random forests, deep learning aka neural networks - being employed as a basis for decision making processes, it is crucial to understand the statistical uncertainty associated with these predictions. As a general means to approach the issue, we present an overarching framework for black-box prediction strategies that not only predict the target but also their own predictions' uncertainty. Moreover, the framework allows for fair assessment and comparison of disparate prediction strategies. For this, we formally consider strategies capable of predicting full distributions from feature variables, so-called probabilistic supervised learning strategies. Our work draws from prior work including Bayesian statistics, information theory, and modern supervised machine learning, and in a novel synthesis leads to (a) new theoretical insights such as a probabilistic bias-variance decomposition and an entropic formulation of prediction, as well as to (b) new algorithms and meta-algorithms, such as composite prediction strategies, probabilistic boosting and bagging, and a probabilistic predictive independence test. Our black-box formulation also leads (c) to a new modular interface view on probabilistic supervised learning and a modelling workflow API design, which we have implemented in the newly released skpro machine learning toolbox, extending the familiar modelling interface and meta-modelling functionality of sklearn. The skpro package provides interfaces for construction, composition, and tuning of probabilistic supervised learning strategies, together with orchestration features for validation and comparison of any such strategy - be it frequentist, Bayesian, or other.

Published
2018

15. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Author

Collins, Gary S, primary, Moons, Karel G M, additional, Dhiman, Paula, additional, Riley, Richard D, additional, Beam, Andrew L, additional, Van Calster, Ben, additional, Ghassemi, Marzyeh, additional, Liu, Xiaoxuan, additional, Reitsma, Johannes B, additional, van Smeden, Maarten, additional, Boulesteix, Anne-Laure, additional, Camaradou, Jennifer Catherine, additional, Celi, Leo Anthony, additional, Denaxas, Spiros, additional, Denniston, Alastair K, additional, Glocker, Ben, additional, Golub, Robert M, additional, Harvey, Hugh, additional, Heinze, Georg, additional, Hoffman, Michael M, additional, Kengne, André Pascal, additional, Lam, Emily, additional, Lee, Naomi, additional, Loder, Elizabeth W, additional, Maier-Hein, Lena, additional, Mateen, Bilal A, additional, McCradden, Melissa D, additional, Oakden-Rayner, Lauren, additional, Ordish, Johan, additional, Parnell, Richard, additional, Rose, Sherri, additional, Singh, Karandeep, additional, Wynants, Laure, additional, and Logullo, Patricia, additional

Published
2024
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16. The “Is mpMRI Enough” or IMRIE Study: A Multicentre Evaluation of Prebiopsy Multiparametric Magnetic Resonance Imaging Compared with Biopsy

Author

Stonier, Thomas, Simson, Nick, Shah, Taimur, Lobo, Niyati, Amer, Tarik, Lee, Su-Min, Bass, Edward, Chau, Edwin, Grey, Alistair, McCartan, Neil, Acher, Peter, Ahmad, Imran, Arumainayagam, Nimalan, Brown, Dominic, Chapman, Alex, Elf, Deborah, Hartington, Thomas, Ibrahim, Ibrahim, Leung, Hing, Liyanage, Sidath, Lovegrove, Catherine, Malthouse, Theo, Mateen, Bilal, Mistry, Kiki, Morrison, Iain, Nalagatla, Sarika, Persad, Raj, Pope, Alvan, Sokhi, Heminder, Syed, Hira, Tadtayev, Sergey, Tharmaratnam, Meera, Qteishat, Ahmed, Miah, Saiful, Emberton, Mark, Moore, Caroline, Walton, Tom, Eddy, Ben, and Ahmed, Hashim U.

Published
2021
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18. Machine Learning in Falls Prediction; A cognition-based predictor of falls for the acute neurological in-patient population

Author

Mateen, Bilal A., Bussas, Matthias, Doogan, Catherine, Waller, Denise, Saverino, Alessia, Király, Franz J, and Playford, E Diane

Subjects
Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Background Information: Falls are associated with high direct and indirect costs, and significant morbidity and mortality for patients. Pathological falls are usually a result of a compromised motor system, and/or cognition. Very little research has been conducted on predicting falls based on this premise. Aims: To demonstrate that cognitive and motor tests can be used to create a robust predictive tool for falls. Methods: Three tests of attention and executive function (Stroop, Trail Making, and Semantic Fluency), a measure of physical function (Walk-12), a series of questions (concerning recent falls, surgery and physical function) and demographic information were collected from a cohort of 323 patients at a tertiary neurological center. The principal outcome was a fall during the in-patient stay (n = 54). Data-driven, predictive modelling was employed to identify the statistical modelling strategies which are most accurate in predicting falls, and which yield the most parsimonious models of clinical relevance. Results: The Trail test was identified as the best predictor of falls. Moreover, addition of any others variables, to the results of the Trail test did not improve the prediction (Wilcoxon signed-rank p < .001). The best statistical strategy for predicting falls was the random forest (Wilcoxon signed-rank p < .001), based solely on results of the Trail test. Tuning of the model results in the following optimized values: 68% (+- 7.7) sensitivity, 90% (+- 2.3) specificity, with a positive predictive value of 60%, when the relevant data is available. Conclusion: Predictive modelling has identified a simple yet powerful machine learning prediction strategy based on a single clinical test, the Trail test. Predictive evaluation shows this strategy to be robust, suggesting predictive modelling and machine learning as the standard for future predictive tools.

Published
2016
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19. Tackling bias in AI health datasets through the STANDING Together initiative

Author

Ganapathi, Shaswath, Palmer, Jo, Alderman, Joseph E., Calvert, Melanie, Espinoza, Cyrus, Gath, Jacqui, Ghassemi, Marzyeh, Heller, Katherine, Mckay, Francis, Karthikesalingam, Alan, Kuku, Stephanie, Mackintosh, Maxine, Manohar, Sinduja, Mateen, Bilal A., Matin, Rubeta, McCradden, Melissa, Oakden-Rayner, Lauren, Ordish, Johan, Pearson, Russell, Pfohl, Stephen R., Rostamzadeh, Negar, Sapey, Elizabeth, Sebire, Neil, Sounderajah, Viknesh, Summers, Charlotte, Treanor, Darren, Denniston, Alastair K., and Liu, Xiaoxuan

Published
2022
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20. A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI

Author

Sounderajah, Viknesh, Ashrafian, Hutan, Rose, Sherri, Shah, Nigam H., Ghassemi, Marzyeh, Golub, Robert, Kahn, Jr, Charles E., Esteva, Andre, Karthikesalingam, Alan, Mateen, Bilal, Webster, Dale, Milea, Dan, Ting, Daniel, Treanor, Darren, Cushnan, Dominic, King, Dominic, McPherson, Duncan, Glocker, Ben, Greaves, Felix, Harling, Leanne, Ordish, Johan, Cohen, Jérémie F., Deeks, Jon, Leeflang, Mariska, Diamond, Matthew, McInnes, Matthew D. F., McCradden, Melissa, Abràmoff, Michael D., Normahani, Pasha, Markar, Sheraz R., Chang, Stephanie, Liu, Xiaoxuan, Mallett, Susan, Shetty, Shravya, Denniston, Alastair, Collins, Gary S., Moher, David, Whiting, Penny, Bossuyt, Patrick M., and Darzi, Ara

Published
2021
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21. Risk factor associations for severe COVID-19, influenza and pneumonia in people with diabetes to inform future pandemic preparations: UK population-based cohort study

Author

Hopkins, Rhian, primary, Young, Katherine G, additional, Thomas, Nicholas J, additional, Godwin, James, additional, Raja, Daniyal, additional, Mateen, Bilal A, additional, Challen, Robert J, additional, Vollmer, Sebastian J, additional, Shields, Beverley M, additional, McGovern, Andrew P, additional, and Dennis, John M, additional

Published
2024
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24. Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Author

Vasey, Baptiste, Nagendran, Myura, Campbell, Bruce, Clifton, David A., Collins, Gary S., Denaxas, Spiros, Denniston, Alastair K., Faes, Livia, Geerts, Bart, Ibrahim, Mudathir, Liu, Xiaoxuan, Mateen, Bilal A., Mathur, Piyush, McCradden, Melissa D., Morgan, Lauren, Ordish, Johan, Rogers, Campbell, Saria, Suchi, Ting, Daniel S. W., Watkinson, Peter, Weber, Wim, Wheatstone, Peter, and McCulloch, Peter

Published
2022
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27. Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes

Author

Cardoso, Pedro, primary, Young, Katie G., additional, Nair, Anand T.N., additional, Hopkins, Rhian, additional, McGovern, Andrew P, additional, Haider, Eram, additional, Karunaratne, Piyumanga, additional, Donnelly, Louise, additional, Mateen, Bilal A., additional, Sattar, Naveed, additional, Holman, Rury R., additional, Bowden, Jack, additional, Hattersley, Andrew T., additional, Pearson, Ewan R., additional, Jones, Angus G., additional, Shields, Beverley M., additional, McKinley, Trevelyan J., additional, and Dennis, John M., additional

Published
2023
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31. Hospital admissions linked to SARS-CoV-2 infection in children and adolescents: cohort study of 3.2 million first ascertained infections in England

Author

Wilde, Harrison, primary, Tomlinson, Christopher, additional, Mateen, Bilal A, additional, Selby, David, additional, Kanthimathinathan, Hari Krishnan, additional, Ramnarayan, Padmanabhan, additional, Du Pre, Pascale, additional, Johnson, Mae, additional, Pathan, Nazima, additional, Gonzalez-Izquierdo, Arturo, additional, Lai, Alvina G, additional, Gurdasani, Deepti, additional, Pagel, Christina, additional, Denaxas, Spiros, additional, Vollmer, Sebastian, additional, and Brown, Katherine, additional

Published
2023
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32. Qualitative interviews to understand methods and systems used to collect ethnicity information in health administrative data sources in England

Author

Quayle, Gemma, primary, Jones, Bethan, additional, Atkins, Jessica, additional, Shannon, Caitriona, additional, Smith, Roxanne, additional, Tabor, David, additional, Bałabuch, Zuzanna, additional, Cox, Courtney, additional, Horsell, Sarah, additional, John, Marie, additional, McGrail White, Tomas, additional, Vickers, Sophie, additional, Whittinger, Sophia, additional, Bannister, Neil, additional, Raleigh, Veena, additional, Mateen, Bilal, additional, and Drummond, Rosemary, additional

Published
2023
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37. Systematic review: The effectiveness of 6-thioguanine nucleotide-based dose optimisation of thiopurines in the treatment of inflammatory bowel disease

Author

Mateen, Bilal, Patel, Mehul, Akobeng, Anthony, Gordon, Morris, Hayee, Bu'Hussain, Mateen, Bilal, Patel, Mehul, Akobeng, Anthony, Gordon, Morris, and Hayee, Bu'Hussain

Abstract

Introduction: Clinical guidelines highlight the potential utility of metabolite-based thiopurine dose optimisation strategies in inflammatory bowel disease (IBD). The aim of this review was to summarise the evidence of effectiveness and safety of a 6-thioguanine nucleotide (6-TGN) metabolite-based dosing strategy for maintenance of remission using azathioprine or mercaptopurine in Crohn's disease and ulcerative colitis. Methods: We searched the Cochrane central register of controlled trials, EMBASE, clinicaltrials.gov, the WHO international clinical trials registry platform, and relevant grey literature, up to 1 December 2021. Inclusion criteria were: all randomised (active comparator) controlled trials of azathioprine or mercaptopurine used for the purposes of maintenance of remission in Crohn's disease or ulcerative colitis where the dose in the intervention arm was optimised based on 6-TGN metabolite assay results. Studies of any duration were eligible for inclusion, and no age restrictions were applied. Results: No studies met the eligibility criteria for this review. Four randomised controlled studies (two of which are currently underway) were identified that assessed the effectiveness of metabolite-based dose optimisation for thiopurine therapy, but were not eligible either because they did not differentiate between induction and maintenance therapy or because of a lack of an appropriate active comparator. Conclusions: There is no RCT-based evidence for dose optimisation using a 6-TGN metabolite-based dosing strategy for maintenance of remission in Crohn's disease or ulcerative colitis. Where evidence is available from combination induction and maintenance trials, it suggests that such a strategy is no better than weight-based dosing.

Published
2023
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38. COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

Author

Thygesen, Johan H, primary, Tomlinson, Christopher, additional, Hollings, Sam, additional, Mizani, Mehrdad A, additional, Handy, Alex, additional, Akbari, Ashley, additional, Banerjee, Amitava, additional, Cooper, Jennifer, additional, Lai, Alvina G, additional, Li, Kezhi, additional, Mateen, Bilal A, additional, Sattar, Naveed, additional, Sofat, Reecha, additional, Torralbo, Ana, additional, Wu, Honghan, additional, Wood, Angela, additional, Sterne, Jonathan A C, additional, Pagel, Christina, additional, Whiteley, William N, additional, Sudlow, Cathie, additional, Hemingway, Harry, additional, Denaxas, Spiros, additional, Abbasizanjani, Hoda, additional, Ahmed, Nida, additional, Ahmed, Badar, additional, Akinoso-Imran, Abdul Qadr, additional, Allara, Elias, additional, Allery, Freya, additional, Angelantonio, Emanuele Di, additional, Ashworth, Mark, additional, Ayyar-Gupta, Vandana, additional, Babu-Narayan, Sonya, additional, Bacon, Seb, additional, Ball, Steve, additional, Banerjee, Ami, additional, Barber, Mark, additional, Barrett, Jessica, additional, Bennie, Marion, additional, Berry, Colin, additional, Beveridge, Jennifer, additional, Birney, Ewan, additional, Bojanić, Lana, additional, Bolton, Thomas, additional, Bone, Anna, additional, Boyle, Jon, additional, Braithwaite, Tasanee, additional, Bray, Ben, additional, Briffa, Norman, additional, Brind, David, additional, Brown, Katherine, additional, Buch, Maya, additional, Canoy, Dexter, additional, Caputo, Massimo, additional, Carragher, Raymond, additional, Carson, Alan, additional, Cezard, Genevieve, additional, Chang, Jen-Yu Amy, additional, Cheema, Kate, additional, Chin, Richard, additional, Chudasama, Yogini, additional, Copland, Emma, additional, Crallan, Rebecca, additional, Cripps, Rachel, additional, Cromwell, David, additional, Curcin, Vasa, additional, Curry, Gwenetta, additional, Dale, Caroline, additional, Danesh, John, additional, Das-Munshi, Jayati, additional, Dashtban, Ashkan, additional, Davies, Alun, additional, Davies, Joanna, additional, Davies, Gareth, additional, Davies, Neil, additional, Day, Joshua, additional, Delmestri, Antonella, additional, Denholm, Rachel, additional, Dennis, John, additional, Denniston, Alastair, additional, Deo, Salil, additional, Dhillon, Baljean, additional, Docherty, Annemarie, additional, Dong, Tim, additional, Douiri, Abdel, additional, Downs, Johnny, additional, Dregan, Alexandru, additional, Ellins, Elizabeth A, additional, Elwenspoek, Martha, additional, Falck, Fabian, additional, Falter, Florian, additional, Fan, Yat Yi, additional, Firth, Joseph, additional, Fraser, Lorna, additional, Friebel, Rocco, additional, Gavrieli, Amir, additional, Gerstung, Moritz, additional, Gilbert, Ruth, additional, Gillies, Clare, additional, Glickman, Myer, additional, Goldacre, Ben, additional, Goldacre, Raph, additional, Greaves, Felix, additional, Green, Mark, additional, Grieco, Luca, additional, Griffiths, Rowena, additional, Gurdasani, Deepti, additional, Halcox, Julian, additional, Hall, Nick, additional, Hama, Tuankasfee, additional, Hansell, Anna, additional, Hardelid, Pia, additional, Hardy, Flavien, additional, Harris, Daniel, additional, Harrison, Camille, additional, Harron, Katie, additional, Hassaine, Abdelaali, additional, Hassan, Lamiece, additional, Healey, Russell, additional, Henderson, Angela, additional, Herz, Naomi, additional, Heyl, Johannes, additional, Hidajat, Mira, additional, Higginson, Irene, additional, Hinchliffe, Rosie, additional, Hippisley-Cox, Julia, additional, Ho, Frederick, additional, Hocaoglu, Mevhibe, additional, Horne, Elsie, additional, Hughes, David, additional, Humberstone, Ben, additional, Inouye, Mike, additional, Ip, Samantha, additional, Islam, Nazrul, additional, Jackson, Caroline, additional, Jenkins, David, additional, Jiang, Xiyun, additional, Johnson, Shane, additional, Kadam, Umesh, additional, Kallis, Costas, additional, Karim, Zainab, additional, Kasan, Jake, additional, Katsoulis, Michalis, additional, Kavanagh, Kim, additional, Kee, Frank, additional, Keene, Spencer, additional, Kent, Seamus, additional, Khalid, Sara, additional, Khawaja, Anthony, additional, Khunti, Kamlesh, additional, Killick, Richard, additional, Kinnear, Deborah, additional, Knight, Rochelle, additional, Kolamunnage-Dona, Ruwanthi, additional, Kontopantelis, Evan, additional, Kurdi, Amanj, additional, Lacey, Ben, additional, Lai, Alvina, additional, Lambarth, Andrew, additional, Larzjan, Milad Nazarzadeh, additional, Lawler, Deborah, additional, Lawrence, Thomas, additional, Lawson, Claire, additional, Li, Qiuju, additional, Li, Ken, additional, Llinares, Miguel Bernabeu, additional, Lorgelly, Paula, additional, Lowe, Deborah, additional, Lyons, Jane, additional, Lyons, Ronan, additional, Machado, Pedro, additional, Macleod, Mary Joan, additional, Macleod, John, additional, Malgapo, Evaleen, additional, Mamas, Mamas, additional, Mamouei, Mohammad, additional, Manohar, Sinduja, additional, Mapeta, Rutendo, additional, Martelli, Javiera Leniz, additional, Martos, David Moreno, additional, Mateen, Bilal, additional, McCarthy, Aoife, additional, Melville, Craig, additional, Milton, Rebecca, additional, Mizani, Mehrdad, additional, Moncusi, Marta Pineda, additional, Morales, Daniel, additional, Mordi, Ify, additional, Morrice, Lynn, additional, Morris, Carole, additional, Morris, Eva, additional, Mu, Yi, additional, Mueller, Tanja, additional, Murdock, Lars, additional, Nafilyan, Vahé, additional, Nicholson, George, additional, Nikiphorou, Elena, additional, Nolan, John, additional, Norris, Tom, additional, Norris, Ruth, additional, North, Laura, additional, North, Teri-Louise, additional, O'Connell, Dan, additional, Oliver, Dominic, additional, Oluyase, Adejoke, additional, Olvera-Barrios, Abraham, additional, Omigie, Efosa, additional, Onida, Sarah, additional, Padmanabhan, Sandosh, additional, Palmer, Tom, additional, Pasea, Laura, additional, Patel, Riyaz, additional, Payne, Rupert, additional, Pell, Jill, additional, Petitjean, Carmen, additional, Pherwani, Arun, additional, Pickrell, Owen, additional, Pierotti, Livia, additional, Pirmohamed, Munir, additional, Priedon, Rouven, additional, Prieto-Alhambra, Dani, additional, Proudfoot, Alastair, additional, Quinn, Terry, additional, Quint, Jennifer, additional, Raffetti, Elena, additional, Rahimi, Kazem, additional, Rao, Shishir, additional, Razieh, Cameron, additional, Roberts, Brian, additional, Rogers, Caroline, additional, Rossdale, Jennifer, additional, Salim, Safa, additional, Samani, Nilesh, additional, Schnier, Christian, additional, Schwartz, Roy, additional, Selby, David, additional, Seminog, Olena, additional, Shabnam, Sharmin, additional, Shah, Ajay, additional, Shelton, Jon, additional, Sheppard, James, additional, Sinha, Shubhra, additional, Skrypak, Mirek, additional, Slapkova, Martina, additional, Sleeman, Katherine, additional, Smith, Craig, additional, Sosenko, Filip, additional, Sperrin, Matthew, additional, Steeg, Sarah, additional, Sterne, Jonathan, additional, Stoica, Serban, additional, Sudell, Maria, additional, Sun, Luanluan, additional, Suseeladevi, Arun Karthikeyan, additional, Sweeting, Michael, additional, Sydes, Matt, additional, Takhar, Rohan, additional, Tang, Howard, additional, Thygesen, Johan, additional, Tilston, George, additional, Tochel, Claire, additional, Toit, Clea du, additional, Toms, Renin, additional, Torabi, Fatemeh, additional, Townson, Julia, additional, Tufail, Adnan, additional, Tungamirai, Tapiwa, additional, Varma, Susheel, additional, Vollmer, Sebastian, additional, Walker, Venexia, additional, Wang, Tianxiao, additional, Wang, Huan, additional, Warwick, Alasdair, additional, Watkinson, Ruth, additional, Watson, Harry, additional, Whiteley, William, additional, Whittaker, Hannah, additional, Wilde, Harry, additional, Wilkinson, Tim, additional, Williams, Gareth, additional, Williams, Michelle, additional, Williams, Richard, additional, Withnell, Eloise, additional, Wolfe, Charles, additional, Wright, Lucy, additional, Wu, Jinge, additional, Wu, Jianhua, additional, Yates, Tom, additional, Zaccardi, Francesco, additional, Zhang, Haoting, additional, Zhang, Huayu, additional, and Zuccolo, Luisa, additional

Published
2022
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40. Extended Data for Systematic Review: The effectiveness of 6-thioguanine nucleotide based dose optimisation of thiopurines in the treatment of inflammatory bowel disease

Author

Mateen, Bilal

Subjects
Medicine and Health Sciences, Life Sciences
Abstract

Extended data, including PRISMA checklist, flow diagram and search strategy for 'Systematic Review: The effectiveness of 6-thioguanine nucleotide based dose optimisation of thiopurines in the treatment of inflammatory bowel disease'.

Published
2023
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41. Additional file 1 of Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine

Author

Venkatasubramaniam, Ashwini, Mateen, Bilal A., Shields, Beverley M., Hattersley, Andrew T., Jones, Angus G., Vollmer, Sebastian J., and Dennis, John M.

Abstract

Additional file 1: sFlowchart. A) CANTATA D and D2 trials. B) CPRD patient flow and inclusion criteria. sTable 1. Additional baseline clinical characteristics by initiated drug class in CPRD. sFigure 1. Distribution of the predicted individualized treatment effect of SGLT2-inhibitor treatment compared to DPP4-inhibitor treatment in the RCT derivation data. a) Penalized regression. sTable 2. Comparison of model performance for the penalised ridge regression model with lasso regression. sTable 3. Sensitivity analysis testing variation in predicted treatment effects varying the number of trees in the causal forest algorithm. sFigure 2. Treatment selection model performance for A) Penalized regression and B) Causal forest in CPRD validation data using double robust estimation incorporating inverse probability of treatment weighting and adjustment for all predictors and confounders in the outcome model. sFigure 3. Treatment selection model performance for A) Penalized regression and B) Causal forest in CPRD validation data using double robust estimation incorporating inverse probability of treatment weighting, inverse probability of censoring weighting, and adjustment for all predictors and confounders in the outcome model. sFigure 4. Treatment selection model performance for A) Penalized regression and B) Causal forest in CPRD validation data, in patients initiating second-line therapy only. TRIPOD Checklist.

Published
2023
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43. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Author

Vasey, Baptiste, primary, Nagendran, Myura, additional, Campbell, Bruce, additional, Clifton, David A, additional, Collins, Gary S, additional, Denaxas, Spiros, additional, Denniston, Alastair K, additional, Faes, Livia, additional, Geerts, Bart, additional, Ibrahim, Mudathir, additional, Liu, Xiaoxuan, additional, Mateen, Bilal A, additional, Mathur, Piyush, additional, McCradden, Melissa D, additional, Morgan, Lauren, additional, Ordish, Johan, additional, Rogers, Campbell, additional, Saria, Suchi, additional, Ting, Daniel S W, additional, Watkinson, Peter, additional, Weber, Wim, additional, Wheatstone, Peter, additional, and McCulloch, Peter, additional

Published
2022
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44. COVID-19 trajectories among 57 million adults in England:a cohort study using electronic health records

Author

Thygesen, Johan H., Tomlinson, Christopher, Hollings, Sam, Mizani, Mehrdod A., Handy, Alex, Akbari, Ashley, Banerjee, Amitava, Cooper, Jennifer, Lai, Alvina G., Li, Kehzi, Mateen, Bilal A., Sattar, Naveed, Sofat, Reecha, Torralbo, Ana, Wu, Honghan, Wood, Angela, Sterne, Jonathan A C, Pagel, Christina, Whitely, William N., Sudlow, Cathie, Hemingway, Harry, Denaxas, Spiros, Thygesen, Johan H., Tomlinson, Christopher, Hollings, Sam, Mizani, Mehrdod A., Handy, Alex, Akbari, Ashley, Banerjee, Amitava, Cooper, Jennifer, Lai, Alvina G., Li, Kehzi, Mateen, Bilal A., Sattar, Naveed, Sofat, Reecha, Torralbo, Ana, Wu, Honghan, Wood, Angela, Sterne, Jonathan A C, Pagel, Christina, Whitely, William N., Sudlow, Cathie, Hemingway, Harry, and Denaxas, Spiros

Abstract

Background: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. Methods: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. Findings: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the fi

Published
2022

45. Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (Nature Medicine, (2022), 28, 5, (924-933), 10.1038/s41591-022-01772-9)

Author

Vasey, Baptiste, Nagendran, Myura, Campbell, Bruce, Clifton, David A., Collins, Gary S., Denaxas, Spiros, Denniston, Alastair K., Faes, Livia, Geerts, Bart, Ibrahim, Mudathir, Liu, Xiaoxuan, Mateen, Bilal A., Mathur, Piyush, McCradden, Melissa D., Morgan, Lauren, Ordish, Johan, Rogers, Campbell, Saria, Suchi, Ting, Daniel S.W., Watkinson, Peter, Weber, Wim, Wheatstone, Peter, McCulloch, Peter, Lee, Aaron Y., Fraser, Alan G., Connell, Ali, Vira, Alykhan, Esteva, Andre, Althouse, Andrew D., and Beam, Andrew L.

Abstract

In the version of this article initially published, a list of the DECIDE-AI expert group members and their affiliations was omitted and has now been included in the HTML and PDF versions of the article. *A list of authors and their affiliations appears online.

Published
2022

46. Where next for partial randomisation of research funding? The feasibility of RCTs and alternatives (RoRI Working Paper No.9)

Author

Stafford, Tom, Rombach, Ines, Hind, Daniel, Mateen, Bilal, Dimairo, Munyaradzi, Woods, Helen Buckley, and Wilsdon, James

Abstract

In recent years, applications of partial randomisation to research funding processes have received growing attention from meta-researchers and research funders (Woods & Wilsdon, 2021a). Partial randomisation, also known as focal randomisation or random selection, is a method for allocating research funding. It is used in addition to peer review, where peer review has reached its limits and the qualities of applications are largely indistinguishable or ‘equally good’ (Bedessem, 2020). In this article we outline essential considerations for any study of partial randomisation of research funding, and consider scenarios in which RCTs would be relevant. We highlight the interdependence of target outcomes, sample availability and statistical power for determining the cost and feasibility of a trial. For many choices of target outcome RCTs may be less practical and more expensive than they first appear (in large part due to issues pertaining to sample size and statistical power). As such, we also introduce and briefly discuss alternatives to RCTs. It is worth noting that many of the considerations relevant to experiments on partial randomisation may also apply to other potential experiments on funding processes (see Bendiscioli et al, 2021).

Published
2022
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47. Derivation and validation of a type 2 diabetes treatment selection algorithm for SGLT2-inhibitor and DPP4-inhibitor therapies based on glucose-lowering efficacy: cohort study using trial and routine clinical data

Author

Dennis, John M, primary, Young, Katherine G, additional, McGovern, Andrew P, additional, Mateen, Bilal A, additional, Vollmer, Sebastian J, additional, Simpson, Michael D, additional, Henley, William E, additional, Holman, Rury R, additional, Sattar, Naveed, additional, Pearson, Ewan R, additional, Hattersley, Andrew, additional, Jones, Angus G, additional, and Shields, Beverley, additional

Published
2021
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48. Understanding COVID-19 trajectories from a nationwide linked electronic health record cohort of 56 million people: phenotypes, severity, waves & vaccination

Author

Thygesen, Johan H, primary, Tomlinson, Christopher, additional, Hollings, Sam, additional, Mizani, Mehrdad, additional, Handy, Alex, additional, Akbari, Ashley, additional, Banerjee, Amitava, additional, Cooper, Jennifer, additional, Lai, Alvina, additional, Li, Ken, additional, Mateen, Bilal, additional, Sattar, Naveed, additional, Sofat, Reecha, additional, Torralbo, Ana, additional, Wu, Honghan, additional, Wood, Angela, additional, Sterne, Jonathan A C, additional, Pagel, Christina, additional, Whiteley, William, additional, Sudlow, Cathie, additional, Hemingway, Harry, additional, and Denaxas, Spiros, additional

Published
2021
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