| Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal L Wynants, B Van Calster, MMJ Bonten, GS Collins, TPA Debray, ... bmj 369, 2020 | 3333 | 2020 |
| The obesity paradox in cancer: a review H Lennon, M Sperrin, E Badrick, AG Renehan Current oncology reports 18, 1-8, 2016 | 574 | 2016 |
| Do patients have worse outcomes in heart failure than in cancer? A primary care‐based cohort study with 10‐year follow‐up in Scotland MA Mamas, M Sperrin, MC Watson, A Coutts, K Wilde, C Burton, ... European journal of heart failure 19 (9), 1095-1104, 2017 | 454 | 2017 |
| Causal inference and counterfactual prediction in machine learning for actionable healthcare M Prosperi, Y Guo, M Sperrin, JS Koopman, JS Min, X He, S Rich, ... Nature Machine Intelligence 2 (7), 369-375, 2020 | 365 | 2020 |
| Framework to construct and interpret latent class trajectory modelling H Lennon, S Kelly, M Sperrin, I Buchan, AJ Cross, M Leitzmann, MB Cook, ... BMJ open 8 (7), e020683, 2018 | 269 | 2018 |
| How much is too much? Threshold dose distributions for 5 food allergens BK Ballmer-Weber, M Fernandez-Rivas, K Beyer, M Defernez, M Sperrin, ... Journal of allergy and clinical immunology 135 (4), 964-971, 2015 | 207 | 2015 |
| Outcome-sensitive multiple imputation: a simulation study E Kontopantelis, IR White, M Sperrin, I Buchan BMC medical research methodology 17, 1-13, 2017 | 182 | 2017 |
| A review of statistical updating methods for clinical prediction models TL Su, T Jaki, GL Hickey, I Buchan, M Sperrin Statistical methods in medical research 27 (1), 185-197, 2018 | 147 | 2018 |
| Access and non–access site bleeding after percutaneous coronary intervention and risk of subsequent mortality and major adverse cardiovascular events: systematic review and … CS Kwok, MA Khan, SV Rao, T Kinnaird, M Sperrin, I Buchan, ... Circulation: Cardiovascular Interventions 8 (4), e001645, 2015 | 140 | 2015 |
| Probabilistic relabelling strategies for the label switching problem in Bayesian mixture models M Sperrin, T Jaki, E Wit Statistics and Computing 20 (3), 357-366, 2010 | 136 | 2010 |
| Collider bias is only a partial explanation for the obesity paradox M Sperrin, J Candlish, E Badrick, A Renehan, I Buchan Epidemiology 27 (4), 525-530, 2016 | 134 | 2016 |
| Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid: single biomarker re-derivation and external validation in three cohorts R Body, E Carlton, M Sperrin, PS Lewis, G Burrows, S Carley, ... Emergency Medicine Journal 34 (6), 349-356, 2017 | 124 | 2017 |
| Body mass index relates weight to height differently in women and older adults: serial cross-sectional surveys in England (1992–2011) M Sperrin, AD Marshall, V Higgins, AG Renehan, IE Buchan Journal of Public Health 38 (3), 607-613, 2016 | 117 | 2016 |
| Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks T Zebin, M Sperrin, N Peek, AJ Casson 2018 40th annual international conference of the IEEE engineering in …, 2018 | 113 | 2018 |
| Evaluation of clinical prediction models (part 1): from development to external validation GS Collins, P Dhiman, J Ma, MM Schlussel, L Archer, B Van Calster, ... bmj 384, 2024 | 110 | 2024 |
| Dynamic models to predict health outcomes: current status and methodological challenges DA Jenkins, M Sperrin, GP Martin, N Peek Diagnostic and prognostic research 2, 1-9, 2018 | 107 | 2018 |
| Impact of co-morbid burden on mortality in patients with coronary heart disease, heart failure, and cerebrovascular accident: a systematic review and meta-analysis M Rashid, CS Kwok, CP Gale, P Doherty, I Olier, M Sperrin, ... European Heart Journal–Quality of Care and Clinical Outcomes 3 (1), 20-36, 2017 | 107 | 2017 |
| Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as … Y Li, M Sperrin, DM Ashcroft, TP Van Staa bmj 371, 2020 | 103 | 2020 |
| Missing data should be handled differently for prediction than for description or causal explanation M Sperrin, GP Martin, R Sisk, N Peek Journal of clinical epidemiology 125, 183-187, 2020 | 99 | 2020 |
| Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small RD Riley, KIE Snell, GP Martin, R Whittle, L Archer, M Sperrin, GS Collins Journal of Clinical Epidemiology 132, 88-96, 2021 | 97 | 2021 |
Iain BuchanProfessor of Public Health and Clinical Informatics, Associate Pro Vice Chancellor for Innovationبريد إلكتروني تم التحقق منه على liverpool.ac.uk