Martin, Glen P

Calculating the sample size required for developing a clinical prediction model

Riley, R. D., Ensor, J., Snell, K. I. E., Harrell, F. E., Martin, G. P., Reitsma, J. B., Moons, K. G. M., Collins, G., & van Smeden, M. (2020) · BMJ

Statistical Methods in EpidemiologyMeta-analysis and systematic reviews

Cited by 2,917

Evaluation of clinical prediction models (part 1): from development to external validation

Collins, G. S., Dhiman, P., Ma, J., Schlussel, M. M., Archer, L., Van Calster, B., Harrell, F. E., Martin, G. P., Moons, K. G. M., van Smeden, M., et al. (2024) · BMJ

Statistical Methods in EpidemiologyArtificial Intelligence in Healthcare

Cited by 798

Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small

Riley, R. D., Snell, K. I. E., Martin, G. P., Whittle, R., Archer, L., Sperrin, M., & Collins, G. S. (2021) · Journal of Clinical Epidemiology

Statistical Methods and InferenceStatistical Methods in Epidemiology

Cited by 142

Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?

Jenkins, D. A., Martin, G. P., Sperrin, M., Riley, R. D., Debray, T. P. A., Collins, G. S., & Peek, N. (2021) · Diagnostic and Prognostic Research

Statistical Methods in EpidemiologyMachine Learning in Healthcare

Cited by 131

Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: A simulation study

Sisk, R., Sperrin, M., Peek, N., van Smeden, M., & Martin, G. P. (2023) · Statistical Methods in Medical Research

Statistical Methods and Bayesian InferenceStatistical Methods in Epidemiology

Cited by 99