Staff profile

• Applied Statistics
• Biostatistics
• Machine Learning and Causal Inference
• Missing Data Methodology
• Models for Rare Events

Journal Article

• Bawamia, B., Spray, L., Wangsaputra, V. K., Bennaceur, K., Vahabi, S., Stellos, K., …Spyridopoulos, I. (2023). Activation of telomerase by TA-65 enhances immunity and reduces inflammation post myocardial infarction. GeroScience, 45(4), 2689-2705. https://doi.org/10.1007/s11357-023-00794-6
• Akowuah, E., Mathias, A., Bardgett, M., Harrison, S., Kasim, A. S., Loughran, K., …Maier, R. H. (2023). Prehabilitation in elective patients undergoing cardiac surgery: a randomised control trial (THE PrEPS TRIAL) – a study protocol. BMJ Open, 13(1), Article e065992. https://doi.org/10.1136/+bmjopen-2022-065992
• Dudley, R., Dodgson, G., Common, S., O'Grady, L., Watson, F., Gibbs, C., …Aynsworth, C. (2022). Managing Unusual Sensory Experiences in People with First-Episode Psychosis (MUSE FEP): a study protocol for a single-blind parallel-group randomised controlled feasibility trial. BMJ Open, 12(5), Article e061827. https://doi.org/10.1136/bmjopen-2022-061827
• Ogundimu, E. O. (2022). Regularization and variable selection in Heckman selection model. Statistical Papers, 63(2), 421-439. https://doi.org/10.1007/s00362-021-01246-z
• Ogundimu, E. O. (2022). On Lasso and adaptive Lasso for non-random sample in credit scoring. Statistical Modelling, https://doi.org/10.1177/1471082x221092181
• Ogundimu, E. O. (2019). Prediction of default probability by using statistical models for rare events. Journal of the Royal Statistical Society: Series A, 182(4), 1143-1162. https://doi.org/10.1111/rssa.12467
• Ogundimu, E. O., & Collins, G. S. (2019). A robust imputation method for missing responses and covariates in sample selection models. Statistical Methods in Medical Research, 28(1), 102-116. https://doi.org/10.1177/0962280217715663
• Ogundimu, E. O., & Collins, G. S. (2018). Predictive performance of penalized beta regression model for continuous bounded outcomes. Journal of Applied Statistics, 45(6), 1030-1040. https://doi.org/10.1080/02664763.2017.1339024
• Collins, G. S., Ogundimu, E. O., Cook, J. A., Manach, Y. L., & Altman, D. G. (2016). Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model. Statistics in Medicine, 35(23), 4124-4135. https://doi.org/10.1002/sim.6986
• Ogundimu, E. O., & Hutton, J. L. (2016). A Sample Selection Model with Skew-normal Distribution. Scandinavian Journal of Statistics, 43(1), 172-190. https://doi.org/10.1111/sjos.12171
• Collins, G. S., Ogundimu, E. O., & Altman, D. G. (2016). Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Statistics in Medicine, 35(2), 214-226. https://doi.org/10.1002/sim.6787
• Ogundimu, E. O., & Hutton, J. L. (2016). A unified approach to multilevel sample selection models. Communications in Statistics - Theory and Methods, 45(9), 2592-2611. https://doi.org/10.1080/03610926.2014.887108
• Akacha, M., & Ogundimu, E. O. (2016). Sensitivity analyses for partially observed recurrent event data. Pharmaceutical Statistics, 15(1), https://doi.org/10.1002/pst.1720
• Rubio, F., Ogundimu, E., & Hutton, J. (2016). On modelling asymmetric data using two-piece sinh–arcsinh distributions. Brazilian Journal of Probability and Statistics (Impresso), 30(3), https://doi.org/10.1214/15-bjps290
• Ogundimu, E. O., Altman, D. G., & Collins, G. S. (2016). Adequate sample size for developing prediction models is not simply related to events per variable. Journal of Clinical Epidemiology, 76, https://doi.org/10.1016/j.jclinepi.2016.02.031
• Ogundimu, E. O., & Hutton, J. L. (2015). On the extended two-parameter generalized skew-normal distribution. Statistics and Probability Letters, 100, 142-148. https://doi.org/10.1016/j.spl.2015.02.016