Can prognostic factors for indirect muscle injuries in elite football (soccer) players be identified using data from preseason screening? An exploratory analysis using routinely collected periodic health examination records (2023)
Journal Article
Hughes, T., Riley, R., Callaghan, M. J., & Sergeant, J. C. (2023). Can prognostic factors for indirect muscle injuries in elite football (soccer) players be identified using data from preseason screening? An exploratory analysis using routinely collected periodic health examination records. BMJ Open, 13(1), Article e052772. https://doi.org/10.1136/bmjopen-2021-052772

Background: In elite football, periodic health examination (PHE) may be useful for injury risk prediction.

Objective: To explore whether PHE-derived variables are prognostic factors for indirect muscle injuries (IMIs) in elite players.

Design:... Read More about Can prognostic factors for indirect muscle injuries in elite football (soccer) players be identified using data from preseason screening? An exploratory analysis using routinely collected periodic health examination records.

Study protocol for the development and internal validation of SPIRIT (Schizophrenia Prediction of Resistance to Treatment): A clinical tool for predicting risk of treatment resistance to anti-psychotics in First Episode Schizophrenia (2022)
Journal Article
Farooq, S., Hattle, M., Dazzan, P., Kingstone, T., Ajnakina, O., Shiers, D., …Van Der Windt, D. (in press). Study protocol for the development and internal validation of SPIRIT (Schizophrenia Prediction of Resistance to Treatment): A clinical tool for predicting risk of treatment resistance to anti-psychotics in First Episode Schizophrenia. BMJ Open, 12(4), https://doi.org/10.1136/bmjopen-2021-056420

<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>Treatment Resistant Schizophrenia (TRS) is associated with significant impairment of functioning and high treatment costs. Identification of patients at high risk... Read More about Study protocol for the development and internal validation of SPIRIT (Schizophrenia Prediction of Resistance to Treatment): A clinical tool for predicting risk of treatment resistance to anti-psychotics in First Episode Schizophrenia.

Lessons learnt when accounting for competing events in the external validation of time-to-event prognostic models. (2021)
Journal Article
Ramspek, C. L., Teece, L., Snell, K. I. E., Evans, M., Riley, R. D., van Smeden, M., …van Diepen, M. (2021). Lessons learnt when accounting for competing events in the external validation of time-to-event prognostic models. International Journal of Epidemiology, 615-625. https://doi.org/10.1093/ije/dyab256

BACKGROUND: External validation of prognostic models is necessary to assess the accuracy and generalizability of the model to new patients. If models are validated in a setting in which competing events occur, these competing risks should be accounte... Read More about Lessons learnt when accounting for competing events in the external validation of time-to-event prognostic models..

Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome. (2021)
Journal Article
Riley, R. D., Collins, G. S., Ensor, J., Archer, L., Booth, S., Mozumder, S. I., …Snell, K. I. E. (2022). Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome. Statistics in Medicine, 41(7), 1280-1295. https://doi.org/10.1002/sim.9275

Previous articles in Statistics in Medicine describe how to calculate the sample size required for external validation of prediction models with continuous and binary outcomes. The minimum sample size criteria aim to ensure precise estimation of key... Read More about Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome..

External validation of prognostic models to predict stillbirth using the International Prediction of Pregnancy Complications (IPPIC) Network database: an individual participant data meta-analysis. (2021)
Journal Article
Allotey, J., Whittle, R., Snell, K. I. E., Smuk, M., Townsend, R., von Dadelszen, P., …Thangaratinam, S. (2021). External validation of prognostic models to predict stillbirth using the International Prediction of Pregnancy Complications (IPPIC) Network database: an individual participant data meta-analysis. Ultrasound in Obstetrics & Gynecology, 59(2), 209-219. https://doi.org/10.1002/uog.23757

OBJECTIVE: Stillbirth is a potentially preventable complication of pregnancy. Identifying women at risk can guide decisions on closer surveillance or timing of birth to prevent fetal death. Prognostic models have been developed to predict the risk of... Read More about External validation of prognostic models to predict stillbirth using the International Prediction of Pregnancy Complications (IPPIC) Network database: an individual participant data meta-analysis..

Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis. (2021)
Journal Article
Stock, S. J., Horne, M., Bruijn, M., White, H., Boyd, K. A., Heggie, R., …Norrie, J. (2021). Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis. PLoS Medicine, e1003686 - ?. https://doi.org/10.1371/journal.pmed.1003686

BACKGROUND: Timely interventions in women presenting with preterm labour can substantially improve health outcomes for preterm babies. However, establishing such a diagnosis is very challenging, as signs and symptoms of preterm labour are common and... Read More about Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis..

Developing more generalizable prediction models from pooled studies and large clustered data sets (2021)
Journal Article
de Jong, V. M. T., Moons, K. G. M., Eijkemans, M. J. C., Riley, R. D., & Debray, T. P. A. (2021). Developing more generalizable prediction models from pooled studies and large clustered data sets. Statistics in Medicine, 40(15), 3533-3559. https://doi.org/10.1002/sim.8981

Prediction models often yield inaccurate predictions for new individuals. Large data sets from pooled studies or electronic healthcare records may alleviate this with an increased sample size and variability in sample characteristics. However, existi... Read More about Developing more generalizable prediction models from pooled studies and large clustered data sets.

Prognostic models for predicting relapse or recurrence of major depressive disorder in adults (2021)
Journal Article
Moriarty, A. S., Meader, N., Snell, K. I., Riley, R. D., Paton, L. W., Chew-Graham, C. A., …McMillan, D. (2021). Prognostic models for predicting relapse or recurrence of major depressive disorder in adults. Cochrane Database of Systematic Reviews, 2021(5), Article ARTN CD013491. https://doi.org/10.1002/14651858.cd013491.pub2

BACKGROUND: Relapse (the re-emergence of depressive symptoms after some level of improvement but preceding recovery) and recurrence (onset of a new depressive episode after recovery) are common in depression, lead to worse outcomes and quality of lif... Read More about Prognostic models for predicting relapse or recurrence of major depressive disorder in adults.

Individual participant data meta-analysis for external validation, recalibration, and updating of a flexible parametric prognostic model. (2021)
Journal Article
Ensor, J., Snell, K. I. E., Debray, T. P. A., Lambert, P. C., Look, M. P., Mamas, M. A., …Riley, R. D. (2021). Individual participant data meta-analysis for external validation, recalibration, and updating of a flexible parametric prognostic model. Statistics in Medicine, 40(13), 3066-3084. https://doi.org/10.1002/sim.8959

Individual participant data (IPD) from multiple sources allows external validation of a prognostic model across multiple populations. Often this reveals poor calibration, potentially causing poor predictive performance in some populations. However, r... Read More about Individual participant data meta-analysis for external validation, recalibration, and updating of a flexible parametric prognostic model..

External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb. (2021)
Journal Article
Snell, K. I., Archer, L., Ensor, J., Bonnett, L. J., Debray, T. P., Phillips, B., …Riley, R. D. (2021). External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb. Journal of Clinical Epidemiology, https://doi.org/10.1016/j.jclinepi.2021.02.011

INTRODUCTION: Sample size 'rules-of-thumb' for external validation of clinical prediction models suggest at least 100 events and 100 non-events. Such blanket guidance is imprecise, and not specific to the model or validation setting. We investigate f... Read More about External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb..

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques (2020)
Journal Article
Andaur Navarro, C. L., Damen, J. A. A. G., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., …Hooft, L. (2020). Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques. BMJ Open, 10(11), Article e038832. https://doi.org/10.1136/bmjopen-2020-038832

INTRODUCTION: Studies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model s... Read More about Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.