Biography
Joie is a Lecturer in Biostatistics at Keele University. He works primarily within the Centre for Prognosis Research at Keele. He previously held a post at the University of Birmingham as a Research Fellow working in the development and validation of prediction models and IPD meta-analysis methods.
Joie joined Keele in January 2015 and his research interests focus on methodological advances in prediction modelling, model validation, IPD meta-analysis, and diagnostic test evaluation. Joie is also a keen programmer, writing statistical software to enable others to use newly developed methodology.
Joie leads the Centre’s training course on ‘Statistical methods for meta-analysis of individual participant data’, and is a member of the Cochrane Prognosis Methods Group (PMG). He is also an Associate Editor of the journal Diagnostic and Prognostic Research.
Research and scholarship
Research and Scholarship
Joie has a number of software packages available in both Stata and R.
pmcalplot
Ensor, J., Snell, K.I.E., and E.C. Martin. PMCALPLOT: Stata module to produce calibration plot of prediction model performance >>link
pmsampsize
Ensor, J., Martin, E.C., Riley, R.D., pmsampsize: Calculates the Minimum Sample Size Required for Developing a Multivariable Prediction Model >>link
pmsampsize
Ensor, J., Martin, E.C., Riley, R.D., PMSAMPSIZE: Stata module to calculate the minimum sample size required for developing a multivariable prediction model >>link
Teaching
Medical statistics module lead for BSc Mathematics and MMath students
Statistics and epidemiology teaching including;
- Public health lecturing on the MBChB
- Lecturing in clinical effectiveness for School of Nursing
Teaching on statistical courses at Keele including;
• Statistical methods for meta-analysis of individual participant data
• Statistical methods for risk prediction & prognostic models
• Effective and efficient initiation and delivery of IPD meta-analyses: practical guidance
• Systematic Reviews and Meta-Analysis of Prognosis Studies
• Prognosis research in healthcare: concepts, methods and impact
Further information
We run a number of statistical methods courses here at Keele University, led by the Centre for Prognosis Research. Please see below for course dates.
- 3 day course: Statistical methods for risk prediction & prognostic models
- 3 day course: Statistical methods for meta-analysis of IPD
- 3 day course: Prognosis research in healthcare: concepts, methods and impact (PROGRESS)
Upcoming courses including;
- Effective and efficient initiation and delivery of IPD meta-analyses: practical guidance (October 2020)
- Systematic Reviews and Meta-Analysis of Prognosis Studies (December 2020)
Selected Publications
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Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome. Stat Med, 1280-1295, vol. 41(7). link> doi> full text>2022.
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Comparative effectiveness of treatment options for subacromial shoulder conditions: a systematic review and network meta-analysis. Ther Adv Musculoskelet Dis (p. 1759720X211037530, vol. 13). link> doi> full text>2021.
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Elixhauser outperformed Charlson comorbidity index in prognostic value after ACS: insights from a national registry. J Clin Epidemiol. link> doi> link> full text>2021.
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Minimum sample size for external validation of a clinical prediction model with a binary outcome. Stat Med, 4230-4251, vol. 40(19). link> doi> full text>2021.
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Prognostic impact of comorbidity measures on outcomes following acute coronary syndrome: A systematic review. Int J Clin Pract, e14345, vol. 75(10). link> doi> full text>2021.
Full Publications Listshow
Journal Articles
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Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome. Stat Med, 1280-1295, vol. 41(7). link> doi> full text>2022.
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Elixhauser outperformed Charlson comorbidity index in prognostic value after ACS: insights from a national registry. J Clin Epidemiol. link> doi> link> full text>2021.
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Minimum sample size for external validation of a clinical prediction model with a binary outcome. Stat Med, 4230-4251, vol. 40(19). link> doi> full text>2021.
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Prognostic impact of comorbidity measures on outcomes following acute coronary syndrome: A systematic review. Int J Clin Pract, e14345, vol. 75(10). link> doi> full text>2021.
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Individual participant data meta-analysis for external validation, recalibration, and updating of a flexible parametric prognostic model. Stat Med, 3066-3084, vol. 40(13). link> doi> full text>2021.
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Community-based complex interventions to sustain independence in older people, stratified by frailty: a protocol for a systematic review and network meta-analysis. BMJ Open, e045637, vol. 11(2). link> doi> full text>2021.
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External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb. J Clin Epidemiol, 79-89, vol. 135. link> doi> full text>2021.
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Minimum sample size for external validation of a clinical prediction model with a continuous outcome. Stat Med, 133-146, vol. 40(1). link> doi> full text>2021.
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Impact of Charlson Co-Morbidity Index Score on Management and Outcomes After Acute Coronary Syndrome. Am J Cardiol, 15-23, vol. 130. link> doi> full text>2020.
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One-stage individual participant data meta-analysis models for continuous and binary outcomes: Comparison of treatment coding options and estimation methods. Stat Med, 2536-2555, vol. 39(19). link> doi> full text>2020.
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Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning. Stat Med, 2115-2137, vol. 39(15). link> doi> full text>2020.
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Temporal recalibration for improving prognostic model development and risk predictions in settings where survival is improving over time. Int J Epidemiol, 1316-1325, vol. 49(4). link> doi> full text>2020.
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Calculating the sample size required for developing a clinical prediction model. BMJ, m441, vol. 368. link> doi> full text>2020.
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Temporal Trends in Comorbidity Burden and Impact on Prognosis in Patients With Acute Coronary Syndrome Using the Elixhauser Comorbidity Index Score. Am J Cardiol, 1603-1611, vol. 125(11). link> doi> full text>2020.
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Wild goose chase - no predictable patient subgroups benefit from meniscal surgery: patient-reported outcomes of 641 patients 1 year after surgery. Br J Sports Med, 13-22, vol. 54(1). link> doi> full text>2020.
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A guide to systematic review and meta-analysis of prognostic factor studies. BMJ, k4597, vol. 364. link> doi> full text>2019.
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Temporal trends and predictors of time to coronary angiography following non-ST-elevation acute coronary syndrome in the USA. Coron Artery Dis, 159-170, vol. 30(3). link> doi> full text>2019.
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Temporal trends and inequalities in coronary angiography utilization in the management of non-ST-Elevation acute coronary syndromes in the U.S. Sci Rep, 240, vol. 9(1). link> doi> full text>2019.
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Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med, 1276-1296, vol. 38(7). link> doi> full text>2019.
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Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes. Stat Med, 1262-1275, vol. 38(7). link> doi> full text>2019.
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Factors affecting local regrowth after watch and wait for patients with a clinical complete response following chemoradiotherapy in rectal cancer (InterCoRe consortium): an individual participant data meta-analysis. Lancet Gastroenterol Hepatol, 825-836, vol. 3(12). link> doi> full text>2018.
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Factors affecting local regrowth after watch and wait for patients with a clinical complete response following chemoradiotherapy in rectal cancer (InterCoRe consortium): an individual participant data meta-analysis (vol 3, pg 825, 2018). LANCET GASTROENTEROLOGY & HEPATOLOGY, E1, vol. 4(2). link> doi> full text>2019.
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Persistent sex disparities in clinical outcomes with percutaneous coronary intervention: Insights from 6.6 million PCI procedures in the United States. PLoS One, e0203325, vol. 13(9). link> doi> full text>2018.
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Individual participant data meta-analysis of continuous outcomes: A comparison of approaches for specifying and estimating one-stage models. Stat Med, 4404-4420, vol. 37(29). link> doi> full text>2018.
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Temporal Changes in Co-Morbidity Burden in Patients Having Percutaneous Coronary Intervention and Impact on Prognosis. Am J Cardiol, 712-722, vol. 122(5). link> doi> full text>2018.
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Simulation-based power calculations for planning a two-stage individual participant data meta-analysis. BMC Med Res Methodol, 41, vol. 18(1). link> doi> full text>2018.
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Guidance for deriving and presenting percentage study weights in meta-analysis of test accuracy studies. Res Synth Methods, 163-178, vol. 9(2). link> doi> full text>2018.
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Meta-analysis of test accuracy studies using imputation for partial reporting of multiple thresholds. Res Synth Methods, 100-115, vol. 9(1). link> doi> full text>2018.
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Economic evaluation of strategies for restarting anticoagulation therapy after a first event of unprovoked venous thromboembolism. J Thromb Haemost, 1591-1600, vol. 15(8). link> doi> full text>2017.
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Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?. Stat Methods Med Res, 3505-3522, vol. 27(11). link> doi> full text>2018.
- 2017.
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Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models. Stat Methods Med Res, 2885-2905, vol. 27(10). link> doi> full text>2018.
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A guide to systematic review and meta-analysis of prediction model performance. BMJ, i6460, vol. 356. link> doi> full text>2017.
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One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information. Stat Med, 772-789, vol. 36(5). link> doi> full text>2017.
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Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ. Stat Med, 855-875, vol. 36(5). link> doi> full text>2017.
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External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ, i3140, vol. 353. link> doi> full text>2016.
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Systematic review of prognostic models for recurrent venous thromboembolism (VTE) post-treatment of first unprovoked VTE. BMJ Open, e011190, vol. 6(5). link> doi> full text>2016.
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Prediction of risk of recurrence of venous thromboembolism following treatment for a first unprovoked venous thromboembolism: systematic review, prognostic model and clinical decision rule, and economic evaluation. Health Technol Assess, i-190, vol. 20(12). link> doi> full text>2016.
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Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model. J Clin Epidemiol, 40-50, vol. 69. link> doi> full text>2016.
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The prognostic utility of tests of platelet function for the detection of 'aspirin resistance' in patients with established cardiovascular or cerebrovascular disease: a systematic review and economic evaluation. Health Technol Assess, 1-366, vol. 19(37). link> doi> full text>2015.
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Meta-analysis of test accuracy studies: an exploratory method for investigating the impact of missing thresholds. Syst Rev, 12, vol. 4. link> doi> full text>2015.
- 2014.
- 2013.
Other
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Comparative effectiveness of treatment options for subacromial shoulder conditions: a systematic review and network meta-analysis. Ther Adv Musculoskelet Dis (p. 1759720X211037530, vol. 13). link> doi> full text>2021.
- 2018.
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The prognostic utility of platelet function testing for the detection of 'aspirin resistance' in patients with established cardiovascular disease. JOURNAL OF THROMBOSIS AND HAEMOSTASIS (p. 515, vol. 13). link>2015.
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