Biography

Richard Riley is a Professor of Biostatistics at Keele University, having previous held posts at the Universities of Birmingham, Liverpool and Leicester. He joined Keele in October 2014 and his role focuses on statistical and methodological research for prognosis and meta-analysis, whilst supporting clinical projects in these areas. He is also a Statistics Editor for the BMJ and a co-convenor of the Cochrane Prognosis Methods Group. 

Richard co-leads a summer school in Prognosis Research Methods, and leads a number of statistical training courses for risk prediction and meta-analysis (see 'courses'). 

Research and scholarship

Richard is an expert on statistical methods for meta-analysis (the combination of results across multiple studies) and prognosis research (the study of future outcomes in those with existing disease). In meta-analysis, he specialises in methods for dealing with multiple correlated outcomes, and for synthesising individual participant data (IPD). In prognosis, Richard co-leads the PROGRESS initiative (PROGnosis RESearch Strategy), that seeks to improve the standards of prognosis research. This includes statistical methods to identify prognostic factors (markers), to develop and validate prognostic models (risk prediction models), and to identify predictors of treatment response for stratified medicine (predictive markers).  He combines his meta-analysis and prognosis interests through the development and validation of prognostic models using IPD from multiple studies, and through meta-analysis of prognostic and predictive marker studies, especially in the cancer field. He has received numerous healthcare related grants, from funders including the MRC and NIHR, and has published over 100 applied and methodological research articles. He currently supervises 5 PhD students and co-supervises 3 others

Selected Publications

  • van der Windt DA, Burke DL, Babatunde O, Hattle M, McRobert C, Littlewood C, Wynne-Jones G, Chesterton L, van der Heijden GJMG, Winters JC, Rhon DI, Bennell K, Roddy E, Heneghan C, Beard D, Rees JL, Riley RD. 2019. Predictors of the effects of treatment for shoulder pain: protocol of an individual participant data meta-analysis. Diagn Progn Res, vol. 3, 15. link> doi> full text>
  • Lambe T, Adab P, Jordan RE, Sitch A, Enocson A, Jolly K, Marsh J, Riley R, Miller M, Cooper BG, Turner AM, Ayres JG, Stockley R, Greenfield S, Siebert S, Daley A, Cheng KK, Fitzmaurice D, Jowett S. 2019. Model-based evaluation of the long-term cost-effectiveness of systematic case-finding for COPD in primary care. Thorax, vol. 74(8), 730-739. link> doi> full text>
  • Hudda MT, Fewtrell MS, Haroun D, Lum S, Williams JE, Wells JCK, Riley RD, Owen CG, Cook DG, Rudnicka AR, Whincup PH, Nightingale CM. 2019. Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using individual participant data. BMJ, vol. 366, l4293. link> doi> full text>
  • Debray TPA, de Jong VMT, Moons KGM, Riley RD. 2019. Evidence synthesis in prognosis research. Diagn Progn Res, vol. 3, 13. link> doi> full text>
  • Mackie FL, Whittle R, Morris RK, Hyett J, Riley RD, Kilby MD. 2019. First-trimester ultrasound measurements and maternal serum biomarkers as prognostic factors in monochorionic twins: a cohort study. Diagn Progn Res, vol. 3, 9. link> doi> full text>

Full Publications List show

Journal Articles

  • van der Windt DA, Burke DL, Babatunde O, Hattle M, McRobert C, Littlewood C, Wynne-Jones G, Chesterton L, van der Heijden GJMG, Winters JC, Rhon DI, Bennell K, Roddy E, Heneghan C, Beard D, Rees JL, Riley RD. 2019. Predictors of the effects of treatment for shoulder pain: protocol of an individual participant data meta-analysis. Diagn Progn Res, vol. 3, 15. link> doi> full text>
  • Lambe T, Adab P, Jordan RE, Sitch A, Enocson A, Jolly K, Marsh J, Riley R, Miller M, Cooper BG, Turner AM, Ayres JG, Stockley R, Greenfield S, Siebert S, Daley A, Cheng KK, Fitzmaurice D, Jowett S. 2019. Model-based evaluation of the long-term cost-effectiveness of systematic case-finding for COPD in primary care. Thorax, vol. 74(8), 730-739. link> doi> full text>
  • Hudda MT, Fewtrell MS, Haroun D, Lum S, Williams JE, Wells JCK, Riley RD, Owen CG, Cook DG, Rudnicka AR, Whincup PH, Nightingale CM. 2019. Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using individual participant data. BMJ, vol. 366, l4293. link> doi> full text>
  • Debray TPA, de Jong VMT, Moons KGM, Riley RD. 2019. Evidence synthesis in prognosis research. Diagn Progn Res, vol. 3, 13. link> doi> full text>
  • Mackie FL, Whittle R, Morris RK, Hyett J, Riley RD, Kilby MD. 2019. First-trimester ultrasound measurements and maternal serum biomarkers as prognostic factors in monochorionic twins: a cohort study. Diagn Progn Res, vol. 3, 9. link> doi> full text>
  • Price MJ, Blake HA, Kenyon S, White IR, Jackson D, Kirkham JJ, Neilson JP, Deeks JJ, Riley RD. 2019. Empirical comparison of univariate and multivariate meta-analyses in Cochrane Pregnancy and Childbirth reviews with multiple binary outcomes. Res Synth Methods. link> doi> full text>
  • Bonnett LJ, Snell KIE, Collins GS, Riley RD. 2019. Guide to presenting clinical prediction models for use in clinical settings. BMJ, vol. 365, l737. link> doi>
  • Riley RD, Moons KGM, Snell KIE, Ensor J, Hooft L, Altman DG, Hayden J, Collins GS, Debray TPA. 2019. A guide to systematic review and meta-analysis of prognostic factor studies. BMJ, vol. 364, k4597. link> doi> full text>
  • Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S. 2019. PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration. Ann Intern Med, vol. 170(1), W1-W33. link> doi> full text>
  • Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S, PROBAST Group†. 2019. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Annals of Internal Medicine, vol. 170(1), 51-58. link> doi> full text>
  • Yu D, Jordan KP, Snell KIE, Riley RD, Bedson J, Edwards JJ, Mallen CD, Tan V, Ukachukwu V, Prieto-Alhambra D, Walker C, Peat G. 2019. Development and validation of prediction models to estimate risk of primary total hip and knee replacements using data from the UK: two prospective open cohorts using the UK Clinical Practice Research Datalink. Ann Rheum Dis, vol. 78(1), 91-99. link> doi> full text>
  • Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE, Moons KG, Collins GS. 2019. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med, vol. 38(7), 1276-1296. link> doi> full text>
  • Riley RD, Snell KIE, Ensor J, Burke DL, Harrell FE, Moons KGM, Collins GS. 2019. Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes. Stat Med, vol. 38(7), 1262-1275. link> doi>
  • Yu D, Jordan K, Snell K, Riley R, Bedson J, Edwards J, Mallen C, Tan V, Ukachukwu V, Prieto-Alhambra D, Walker C, Peat G. 2018. Development and validation of prediction models to estimate risk of primary total hip and knee replacements using data from the UK: two prospective open cohorts using the UK Clinical Practice Research Datalink. Annals of the Rheumatic Diseases. doi> full text>
  • Chadi SA, Malcomson L, Ensor J, Riley RD, Vaccaro CA, Rossi GL, Daniels IR, Smart NJ, Osborne ME, Beets GL, Maas M, Bitterman DS, Du K, Gollins S, Sun Myint A, Smith FM, Saunders MP, Scott N, O'Dwyer ST, de Castro Araujo RO, Valadao M, Lopes A, Hsiao C-W, Lai C-L, Smith RK, Paulson EC, Appelt A, Jakobsen A, Wexner SD, Habr-Gama A, Sao Julião G, Perez R, Renehan AG. 2018. 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, vol. 3(12), 825-836. link> doi>
  • Moons KG, Hooft L, Williams K, Hayden JA, Damen JA, Riley RD. 2018. Implementing systematic reviews of prognosis studies in Cochrane. Cochrane Database Syst Rev, vol. 10, ED000129. link> doi>
  • Panagioti M, Geraghty K, Johnson J, Zhou A, Panagopoulou E, Chew-Graham C, Peters D, Hodkinson A, Riley R, Esmail A. 2018. Association Between Physician Burnout and Patient Safety, Professionalism, and Patient Satisfaction: A Systematic Review and Meta-analysis. JAMA Intern Med, vol. 178(10), 1317-1330. link> doi> full text>
  • Chadi S, Malcolmson L, Ensor J, Riley RD, Vaccaro C, Rossi G, Daniels I, Smart N, Osborne M, Beets G, Maas M, Bitterman D, Du K, Gollins S, Sun Myint A, Smith F, Saunders M, Scott N, O'Dwyer S, Otavio de Castro Araujo R, Valadao M, Lopes A, Hsiao CW, Lai CL, Smith R, Carter Poulson E, Appelt A, Jakobsen A, Wexner S, Habr-Gama A, Sao Juliao G, Perez R, Renehan A. Factor influencing local regrowth after watch-and-wait for clinical complete response following chemoradiotherapy in rectal cancer: an individual participant data meta-analysis (InterCoRe consortium). The Lancet Gastroenterology and Hepatology. full text>
  • Potts J, Sirker A, Martinez SC, Gulati M, Alasnag M, Rashid M, Kwok CS, Ensor J, Burke DL, Riley RD, Holmvang L, Mamas MA. 2018. Persistent sex disparities in clinical outcomes with percutaneous coronary intervention: Insights from 6.6 million PCI procedures in the United States. PLoS One, vol. 13(9), e0203325. link> doi> full text>
  • Westby MJ, Dumville JC, Stubbs N, Norman G, Wong JK, Cullum N, Riley RD. 2018. Protease activity as a prognostic factor for wound healing in venous leg ulcers. Cochrane Database Syst Rev, vol. 9, CD012841. link> doi>
  • Legha A, Riley RD, Ensor J, Snell KIE, Morris TP, Burke DL. 2018. Individual participant data meta-analysis of continuous outcomes: A comparison of approaches for specifying and estimating one-stage models. Stat Med, vol. 37(29), 4404-4420. link> doi> full text>
  • Debray TP, Damen JA, Riley RD, Snell K, Reitsma JB, Hooft L, Collins GS, Moons KG. 2018. A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes. Stat Methods Med Res, 962280218785504. link> doi> full text>
  • Potts J, Kwok CS, Ensor J, Rashid M, Kadam U, Kinnaird T, Curzen N, Pancholy SB, Van der Windt D, Riley RD, Bagur R, Mamas MA. 2018. Temporal Changes in Co-Morbidity Burden in Patients Having Percutaneous Coronary Intervention and Impact on Prognosis. Am J Cardiol, vol. 122(5), 712-722. link> doi> full text>
  • Whittle R, Peat G, Belcher J, Collins GS, Riley RD. 2018. Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported. J Clin Epidemiol, vol. 102, 38-49. link> doi> full text>
  • Ensor J, Burke DL, Snell KIE, Hemming K, Riley RD. 2018. Simulation-based power calculations for planning a two-stage individual participant data meta-analysis. BMC Med Res Methodol, vol. 18(1), 41. link> doi> full text>
  • Hughes T, Sergeant JC, van der Windt DA, Riley R, Callaghan MJ. 2018. Periodic Health Examination and Injury Prediction in Professional Football (Soccer): Theoretically, the Prognosis is Good. Sports Med, vol. 48(11), 2443-2448. link> doi> full text>
  • Stock SJ, Wotherspoon LM, Boyd KA, Morris RK, Dorling J, Jackson L, Chandiramani M, David AL, Khalil A, Shennan A, Hodgetts Morton V, Lavender T, Khan K, Harper-Clarke S, Mol B, Riley RD, Norrie J, Norman J. 2018. Study protocol: quantitative fibronectin to help decision-making in women with symptoms of preterm labour (QUIDS) part 2, UK Prospective Cohort Study. BMJ Open, vol. 8(4), e020795. link> doi> full text>
  • Stock SJ, Wotherspoon LM, Boyd KA, Morris RK, Dorling J, Jackson L, Chandiramani M, David AL, Khalil A, Shennan A, Hodgetts Morton V, Lavender T, Khan K, Harper-Clarke S, Mol BW, Riley RD, Norrie J, Norman JE. 2018. Quantitative fibronectin to help decision-making in women with symptoms of preterm labour (QUIDS) part 1: Individual participant data meta-analysis and health economic analysis. BMJ Open, vol. 8(4), e020796. link> doi> full text>
  • Wynants L, Riley RD, Timmerman D, Van Calster B. 2018. Random-effects meta-analysis of the clinical utility of tests and prediction models. Stat Med, vol. 37(12), 2034-2052. link> doi> full text>
  • Copas JB, Jackson D, White IR, Riley RD. 2018. The role of secondary outcomes in multivariate meta-analysis. J R Stat Soc Ser C Appl Stat, vol. 67(5), 1177-1205. link> doi> full text>
  • Winzenberg T, Lamberg-Allardt C, El-Hajj Fuleihan G, Mølgaard C, Zhu K, Wu F, Riley RD. 2018. Does vitamin D supplementation improve bone density in vitamin D-deficient children? Protocol for an individual patient data meta-analysis. BMJ Open, vol. 8(1), e019584. link> doi> full text>
  • Holden MA, Burke DL, Runhaar J, van Der Windt D, Riley RD, Dziedzic K, Legha A, Evans AL, Abbott JH, Baker K, Brown J, Bennell KL, Bossen D, Brosseau L, Chaipinyo K, Christensen R, Cochrane T, de Rooij M, Doherty M, French HP, Hickson S, Hinman RS, Hopman-Rock M, Hurley MV, Ingram C, Knoop J, Krauss I, McCarthy C, Messier SP, Patrick DL, Sahin N, Talbot LA, Taylor R, Teirlinck CH, van Middelkoop M, Walker C, Foster NE, OA Trial Bank. 2017. Subgrouping and TargetEd Exercise pRogrammes for knee and hip OsteoArthritis (STEER OA): a systematic review update and individual participant data meta-analysis protocol. BMJ Open, vol. 7(12), e018971. link> doi> full text>
  • Burke DL, Ensor J, Snell KIE, van der Windt D, Riley RD. 2018. Guidance for deriving and presenting percentage study weights in meta-analysis of test accuracy studies. Res Synth Methods, vol. 9(2), 163-178. link> doi> full text>
  • Hong C, D Riley R, Chen Y. 2018. An improved method for bivariate meta-analysis when within-study correlations are unknown. Res Synth Methods, vol. 9(1), 73-88. link> doi>
  • Ensor J, Deeks JJ, Martin EC, Riley RD. 2018. Meta-analysis of test accuracy studies using imputation for partial reporting of multiple thresholds. Res Synth Methods, vol. 9(1), 100-115. link> doi> full text>
  • Debray TPA, Moons KGM, Riley RD. 2018. Detecting small-study effects and funnel plot asymmetry in meta-analysis of survival data: A comparison of new and existing tests. Res Synth Methods, vol. 9(1), 41-50. link> doi>
  • Willis BH and Riley RD. 2017. Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice. Stat Med, vol. 36(21), 3283-3301. link> doi> full text>
  • Riley RD, Jackson D, Salanti G, Burke DL, Price M, Kirkham J, White IR. 2017. Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples. BMJ, vol. 358, j3932. link> doi> full text>
  • Jackson D, Bujkiewicz S, Law M, Riley RD, White IR. 2018. A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects. Biometrics, vol. 74(2), 548-556. link> doi>
  • Rogozińska E, Marlin N, Jackson L, Rayanagoudar G, Ruifrok AE, Dodds J, Molyneaux E, van Poppel MN, Poston L, Vinter CA, McAuliffe F, Dodd JM, Owens J, Barakat R, Perales M, Cecatti JG, Surita F, Yeo S, Bogaerts A, Devlieger R, Teede H, Harrison C, Haakstad L, Shen GX, Shub A, Beltagy NE, Motahari N, Khoury J, Tonstad S, Luoto R, Kinnunen TI, Guelfi K, Facchinetti F, Petrella E, Phelan S, Scudeller TT, Rauh K, Hauner H, Renault K, de Groot CJ, Sagedal LR, Vistad I, Stafne SN, Mørkved S, Salvesen KÅ, Jensen DM, Vitolo M, Astrup A, Geiker NR, Kerry S, Barton P, Roberts T, Riley RD, Coomarasamy A, Mol BW, Khan KS, Thangaratinam S. 2017. Effects of antenatal diet and physical activity on maternal and fetal outcomes: individual patient data meta-analysis and health economic evaluation. Health Technol Assess, vol. 21(41), 1-158. link> doi> full text>
  • Yamaguchi Y, Maruo K, Partlett C, Riley RD. 2017. A random effects meta-analysis model with Box-Cox transformation. BMC Med Res Methodol, vol. 17(1), 109. link> doi> full text>
  • Rogozinska E, Marlin N, Pilar A, Astrup A, Barakat R, Bogaerts A, Ceccati JG, Devlieger R, El Beltagy N, Facchinetti F, Geiker NRW, Guelfi K, Haakstad LAH, Cheryce H, Hans H, Jensen DM, Kinnunen TI, Khoury J, Luoto R, McAuliffe F, Motahari N, Morkved S, Owens J, Perales M, Elisabetta P, Phelan S, Poston L, Rauh K, Renault KM, Sagedal LR, Salvesen K, Shen GX, Shub A, Scudeller T, Surita F, Stafne SN, Teede H, Tonstad S, van Poppel MNM, Vinter CA, Vistad I, Yeo S, Dodds J, Kerry S, Jackson L, Barton P, Molyneaux E, Alba A, Rayanagoudar G, Ruifrok AE, Roberts T, de Groot CGM, Coomarasamy A, Mol BW, Zamora J, Khan KS, Riley RD. 2017. Effect of diet and physical activity based interventions in pregnancy on gestational weight gain and pregnancy outcomes: meta-analysis of individual participant data from randomised trials. BMJ, vol. 358. doi>
  • Haroon S, Adab P, Riley RD, Fitzmaurice D, Jordan RE. 2017. Predicting risk of undiagnosed COPD: development and validation of the TargetCOPD score. Eur Respir J, vol. 49(6). link> doi>
  • Bilagi A, Burke DL, Riley RD, Mills I, Kilby MD, Morris RK. 2017. Association of maternal serum PAPP-A levels, nuchal translucency and crown rump length in first trimester with adverse pregnancy outcomes: Retrospective cohort study. Prenatal Diagnosis. link> doi> full text>
  • Snell KI, Ensor J, Debray TP, Moons KG, Riley RD. 2017. 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, 962280217705678. link> doi> link> full text>
  • Thangaratinam S, Allotey J, Marlin N, Mol BW, Von Dadelszen P, Ganzevoort W, Akkermans J, Ahmed A, Daniels J, Deeks J, Ismail K, Barnard AM, Dodds J, Kerry S, Moons C, Riley RD, Khan KS. 2017. Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP): a prospective cohort study. Health Technol Assess, vol. 21(18), 1-100. link> doi>
  • Thangaratinam S, Allotey J, Marlin N, Dodds J, Cheong-See F, von Dadelszen P, Ganzevoort W, Akkermans J, Kerry S, Mol BW, Moons KGM, Riley RD, Khan KS, PREP Collaborative Network. 2017. Prediction of complications in early-onset pre-eclampsia (PREP): development and external multinational validation of prognostic models. BMC Med, vol. 15(1), 68. link> doi> full text>
  • Elia EG, Robb AO, Hemming K, Price MJ, Riley RD, French-Constant A, Denison FC, Kilby MD, Morris RK, Stock SJ. 2017. Is the first urinary albumin/creatinine ratio (ACR) in women with suspected pre-eclampsia a prognostic factor for maternal and neonatal adverse outcome? A retrospective cohort study. Acta Obstetricia et Gynecologica Scandinavica. link> doi> link> full text>
  • Whittle R, Royle K-L, Jordan KP, Riley RD, Mallen CD, Peat G. 2017. Prognosis research ideally should measure time-varying predictors at their intended moment of use. Diagn Progn Res, vol. 1, 1. link> doi> full text>
  • Riley RD, Ensor J, Jackson D, Burke DL. 2018. 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, vol. 27(10), 2885-2905. link> doi> full text>
  • Debray TPA, Damen JAAG, Snell KIE, Ensor J, Hooft L, Reitsma JB, Riley RD, Moons KGM. 2017. A guide to systematic review and meta-analysis of prediction model performance. BMJ, vol. 356, i6460. link> doi> full text>
  • Allotey J, Marlin N, Mol BW, Von Dadelszen P, Ganzevoort W, Akkermans J, Ahmed A, Daniels J, Deeks J, Ismail K, Barnard AM, Dodds J, Kerry S, Moons C, Khan KS, Riley RD, Thangaratinam S. 2017. Development and validation of prediction models for risk of adverse outcomes in women with early-onset pre-eclampsia: protocol of the prospective cohort PREP study. Diagn Progn Res, vol. 1, 6. link> doi>
  • Gopalakrishna G, Langendam M, Scholten R, Bossuyt P, Leeflang M, Noel-Storr A, Thomas J, Marshall I, Wallace B, Whiting P, Davenport C, Leeflang M, GopalaKrishna G, de Salis I, Mallett S, Wolff R, Whiting P, Riley R, Westwood M, Kleinen J, Collins G, Reitsma H, Moons K, Zapf A, Hoyer A, Kramer K, Kuss O, Ensor J, Deeks JJ, Martin EC, Riley RD, Rücker G, Steinhauser S, Schumacher M, Riley R, Ensor J, Snell K, Willis B, Debray T, Moons K, Deeks J, Collins G, di Ruffano LF, Willis B, Davenport C, Mallett S, Taylor-Phillips S, Hyde C, Deeks J, Mallett S, Taylor SA, Batnagar G, STREAMLINE COLON Investigators, STREAMLINE LUNG Investigators, METRIC Investigators, Taylor-Phillips S, Di Ruffano LF, Seedat F, Clarke A, Deeks J, Byron S, Nixon F, Albrow R, Walker T, Deakin C, Hyde C, Zhelev Z, Hunt H, di Ruffano LF, Yang Y, Abel L, Buchanan J, Fanshawe T, Shinkins B, Wynants L, Verbakel J, Van Huffel S, Timmerman D, Van Calster B, Leeflang M, Zwinderman A, Bossuyt P, Oke J, O'Sullivan J, Perera R, Nicholson B, Bromley HL, Roberts TE, Francis A, Petrie D, Mann GB, Malottki K, Smith H, Deeks J, Billingham L, Sitch A, Mallett S, Deeks J, Gerke O, Holm-Vilstrup M, Segtnan EA, Halekoh U, Høilund-Carlsen PF, Francq BG, Deeks J, Sitch A, Dinnes J, Parkes J, Gregory W, Hewison J, Altman D, Rosenberg W, Selby P, Asselineau J, Perez P, Paye A, Bessede E, Proust-Lima C, Naaktgeboren C, de Groot J, Rutjes A, Bossuyt P, Reitsma J, Moons K, Collins G, Ogundimu E, Cook J, Le Manach Y, Altman D, Wynants L, Vergouwe Y, Van Huffel S, Timmerman D, Van Calster B, Pajouheshnia R, Groenwold R, Moons K, Reitsma J, Peelen L, Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW, Cooper J, Taylor-Phillips S, Parsons N, Stinton C, Smith S, Dickens A, Jordan R, Enocson A, Fitzmaurice D, Sitch A, Adab P, Francq BG, Boachie C, Vidmar G, Freeman K, Connock M, Taylor-Phillips S, Court R, Clarke A, de Groot J, Naaktgeboren C, Reitsma H, Moons C, Harris J, Mumford A, Plummer Z, Lee K, Reeves B, Rogers C, Verheyden V, Angelini GD, Murphy GJ, Huddy J, Ni M, Good K, Cooke G, Bossuyt P, Hanna G, Ma J, Altman D, Collins G, Moons KGMC, de Groot JAH, Mallett S, Altman DG, Reitsma JB, Collins GS, Moons KGM, Altman DG, Reitsma JB, Collins GS, Kamarudin AN, Kolamunnage-Dona R, Cox T, Ni M, Huddy J, Borsci S, Hanna G, Pérez T, Pardo MC, Candela-Toha A, Muriel A, Zamora J, Sanghera S, Mohiuddin S, Martin R, Donovan J, Coast J, Seo MK, Cairns J, Mitchell E, Smith A, Wright J, Hall P, Messenger M, Calder N, Wickramasekera N, Vinall-Collier K, Lewington A, Pajouheshnia R, Damen J, Groenwold R, Moons K, Peelen L, Messenger M, Cairns D, Smith A, Hutchinson M, Wright J, Hall P, Calder N, Sturgeon C, Mitchel L, Kift R, Christakoudi S, Rungall M, Mobillo P, Montero R, Tsui T-L, Kon SP, Tucker B, Sacks S, Farmer C, Strom T, Chowdhury P, Rebollo-Mesa I, Hernandez-Fuentes M, Damen JAAG, Debray TPA, Heus P, Hooft L, Moons KGM, Pajouheshnia R, Reitsma JB, Scholten RJPM, Damen JAAG, Hooft L, Schuit E, Debray TPA, Collins GS, Tzoulaki I, Lassale CM, Siontis GCM, Chiocchia V, Roberts C, Schlüssel MM, Gerry S, Black JA, Heus P, van der Schouw YT, Peelen LM, Moons KGM, Damen JAAG, Debray TPA, Heus P, Hooft L, Moons KGM, Pajouheshnia R, Reitsma JB, Scholten RJPM, Ma J, Altman D, Collins G, Spence G, McCartney D, van den Bruel A, Lasserson D, Hayward G, Vach W, de Jong A, Burggraaff C, Hoekstra O, Zijlstra J, de Vet H, Hunt H, Hyde C, Graziadio S, Allen J, Johnston L, O'Leary R, Power M, Allen J, Graziadio S, Johnson L, O'Leary R, Power M, Waters R, Simpson J, Johnston L, Allen J, Graziadio S, O'Leary R, Waters R, Power M, Mallett S, Fanshawe TR, Phillips P, Plumb A, Helbren E, Halligan S, Taylor SA, Gale A, Mallett S, Sekula P, Altman DG, Sauerbrei W, Mallett S, Fanshawe TR, Forman JR, Dutton SJ, Takwoingi Y, Hensor EM, Nichols TE, Shinkins B, Yang Y, Abel L, Di Ruffano LF, Fanshawe T, Kempf E, Porcher R, de Beyer J, Moons K, Altman D, Reitsma H, Hopewell S, Sauerbrei W, Collins G, Dennis J, Shields B, Jones A, Henley W, Pearson E, Hattersley A, MASTERMIND consortium, Heus P, Damen JAAG, Pajouheshnia R, Scholten RJPM, Reitsma JB, Collins GS, Altman DG, Moons KGM, Hooft L, Shields B, Dennis J, Jones A, Henley W, Pearson E, Hattersley A, MASTERMIND consortium, Scheibler F, Rummer A, Sturtz S, Großelfinger R, Banister K, Ramsay C, Azuara-Blanco A, Cook J, Boachie C, Burr J, Kumarasamy M, Bourne R, Uchegbu I, Borsci S, Murphy J, Hanna G, Uchegbu I, Carter A, Murphy J, Ni M, Marti J, Eatock J, Uchegbu I, Robotham J, Dudareva M, Gilchrist M, Holmes A, Uchegbu I, Borsci S, Monaghan P, Lord S, StJohn A, Sandberg S, Cobbaert C, Lennartz L, Verhagen-Kamerbeek W, Ebert C, Bossuyt P, Horvath A, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Jenniskens K, Naaktgeboren C, Reitsma J, Moons K, de Groot J, Hyde C, Peters J, Grigore B, Peters J, Hyde C, Hyde C, Ukoumunne O, Peters J, Zhelev Z, Levis B, Benedetti A, Levis AW, Ioannidis JPA, Shrier I, Cuijpers P, Gilbody S, Kloda LA, McMillan D, Patten SB, Steele RJ, Ziegelstein RC, Bombardier CH, Osório FDL, Fann JR, Gjerdingen D, Lamers F, Lotrakul M, Loureiro SR, Löwe B, Shaaban J, Stafford L, van Weert HCPM, Whooley MA, Williams LS, Wittkampf KA, Yeung AS, Thombs BD, Peters J, Cooper C, Buchanan J, Nieto T, Smith C, Tucker O, Dretzke J, Beggs A, Rai N, Davenport C, Bayliss S, Stevens S, Snell K, Mallet S, Deeks J, Sundar S, Hall E, Porta N, Estelles DL, de Bono J, CTC-STOP protocol development group. 2017. Erratum to: Methods for evaluating medical tests and biomarkers. Diagn Progn Res, vol. 1, 11. link> doi>
  • Allotey J, Snell KIE, Chan C, Hooper R, Dodds J, Rogozinska E, Khan KS, Poston L, Kenny L, Myers J, Thilaganathan B, Chappell L, Mol BW, Von Dadelszen P, Ahmed A, Green M, Poon L, Khalil A, Moons KGM, Riley RD, Thangaratinam S, IPPIC Collaborative Network. 2017. External validation, update and development of prediction models for pre-eclampsia using an Individual Participant Data (IPD) meta-analysis: the International Prediction of Pregnancy Complication Network (IPPIC pre-eclampsia) protocol. Diagn Progn Res, vol. 1, 16. link> doi>
  • Sultan AA, West J, Grainge MJ, Riley RD, Tata LJ, Stephansson O, Fleming KM, Nelson-Piercy C, Ludvigsson JF. 2016. Development and validation of risk prediction model for venous thromboembolism in postpartum women: multinational cohort study. BMJ, vol. 355, i6253. link> doi> full text>
  • Hua H, Burke DL, Crowther MJ, Ensor J, Tudur Smith C, Riley RD. 2017. 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, vol. 36(5), 772-789. link> doi> full text>
  • Burke DL, Ensor J, Riley RD. 2017. Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ. Stat Med, vol. 36(5), 855-875. link> doi> full text>
  • Partlett C and Riley RD. 2017. Random effects meta-analysis: Coverage performance of 95% confidence and prediction intervals following REML estimation. Stat Med, vol. 36(2), 301-317. link> doi> full text>
  • Tudur Smith C, Marcucci M, Nolan SJ, Iorio A, Sudell M, Riley R, Rovers MM, Williamson PR. 2016. Individual participant data meta-analyses compared with meta-analyses based on aggregate data. Cochrane Database Syst Rev, vol. 9, MR000007. link> doi>
  • Rees F, Doherty M, Lanyon P, Davenport G, Riley RD, Zhang W, Grainge MJ. 2016. Early clinical features in Systemic Lupus Erythematosus: can they be used to achieve earlier diagnosis? A risk prediction model. Arthritis care & research. doi> link> full text>
  • Jordan RE, Adab P, Sitch A, Enocson A, Blissett D, Jowett S, Marsh J, Riley RD, Miller MR, Cooper BG, Turner AM, Jolly K, Ayres JG, Haroon S, Stockley R, Greenfield S, Siebert S, Daley AJ, Cheng KK, Fitzmaurice D. 2016. Targeted case finding for chronic obstructive pulmonary disease versus routine practice in primary care (TargetCOPD): a cluster-randomised controlled trial. Lancet Respir Med, vol. 4(9), 720-730. link> doi> full text>
  • Adab P, Fitzmaurice DA, Dickens AP, Ayres JG, Buni H, Cooper BG, Daley AJ, Enocson A, Greenfield S, Jolly K, Jowett S, Kalirai K, Marsh JL, Miller MR, Riley RD, Siebert WS, Stockley RA, Turner AM, Cheng KK, Jordan RE. 2017. Cohort Profile: The Birmingham Chronic Obstructive Pulmonary Disease (COPD) Cohort Study. Int J Epidemiol, vol. 46(1), 23. link> doi> full text>
  • Riley RD, Ensor J, Snell KIE, Debray TPA, Altman DG, Moons KGM, Collins GS. 2016. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ, vol. 353, i3140. link> doi> full text>
  • Ensor J, Riley RD, Moore D, Snell KIE, Bayliss S, Fitzmaurice D. 2016. Systematic review of prognostic models for recurrent venous thromboembolism (VTE) post-treatment of first unprovoked VTE. BMJ Open, vol. 6(5), e011190. link> doi> full text>
  • Groenwold RHH, Moons KGM, Pajouheshnia R, Altman DG, Collins GS, Debray TPA, Reitsma JB, Riley RD, Peelen LM. 2016. Explicit inclusion of treatment in prognostic modeling was recommended in observational and randomized settings. J Clin Epidemiol, vol. 78, 90-100. link> doi> full text>
  • Burke DL, Bujkiewicz S, Riley RD. 2016. Bayesian bivariate meta-analysis of correlated effects: Impact of the prior distributions on the between-study correlation, borrowing of strength, and joint inferences. Statistical methods in medical research. doi> link> full text>
  • Simmons B, Saleem J, Hill A, Riley RD, Cooke GS. 2016. Risk of Late Relapse or Reinfection With Hepatitis C Virus After Achieving a Sustained Virological Response: A Systematic Review and Meta-analysis. Clin Infect Dis, vol. 62(6), 683-694. link> doi> full text>
  • Phillips RS, Sung L, Ammann RA, Riley RD, Castagnola E, Haeusler GM, Klaassen R, Tissing WJE, Lehrnbecher T, Chisholm J, Hakim H, Ranasinghe N, Paesmans M, Hann IM, Stewart LA, PICNICC Collaboration. 2016. Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis. British Journal of Cancer, vol. 114(6), 623-630. link> doi> link> full text>
  • Jolly K, Majothi S, Sitch AJ, Heneghan NR, Riley RD, Moore DJ, Bates EJ, Turner AM, Bayliss SE, Price MJ, Singh SJ, Adab P, Fitzmaurice DA, Jordan RE. 2016. Self-management of health care behaviors for COPD: a systematic review and meta-analysis. Int J Chron Obstruct Pulmon Dis, vol. 11, 305-326. link> doi> full text>
  • Ensor J, Riley RD, Jowett S, Monahan M, Snell KI, Bayliss S, Moore D, Fitzmaurice D, PIT-STOP collaborative group. 2016. 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, vol. 20(12), i-190. link> doi> full text>
  • Cheong-See F, Allotey J, Marlin N, Mol BW, Schuit E, Ter Riet G, Riley RD, Moons K, Khan KS, Thangaratinam S. 2016. Prediction models in obstetrics: understanding the treatment paradox and potential solutions to the threat it poses. BJOG : an international journal of obstetrics and gynaecology, vol. 123(7), 1060-1064. doi> link>
  • Allotey J, Thangaratinam S, Marlin N, Mol B, Von Dadelszen P, Ganzevoort W, Akkermans J, Ahmed A, Daniels J, Deeks J, Ismail K, Barnard AM, Dodds J, Kerry S, Moons C, Riley RD, Khan KS. 2015. 782: Development and validation of a prediction model for the risk of adverse outcomes in women with early onset preeclampsia (PREP): a prospective cohort study. American Journal of Obstetrics and Gynecology, vol. 214(1), S409. doi> link>
  • Malottki K, Popat S, Deeks JJ, Riley RD, Nicholson AG, Billingham L. 2015. Problems of variable biomarker evaluation in stratified medicine research-A case study of ERCC1 in non-small-cell lung cancer. LUNG CANCER, vol. 92, 1-7. link> doi> link>
  • Jackson D, White IR, Price M, Copas J, Riley RD. 2017. Borrowing of strength and study weights in multivariate and network meta-analysis. Stat Methods Med Res, vol. 26(6), 2853-2868. link> doi>
  • Bujkiewicz S, Thompson JR, Riley RD, Abrams KR. 2016. Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process. Stat Med, vol. 35(7), 1063-1089. link> doi> full text>
  • Takwoingi Y, Riley RD, Deeks JJ. 2015. Meta-analysis of diagnostic accuracy studies in mental health. Evid Based Ment Health, vol. 18(4), 103-109. link> doi>
  • Debray TPA, Riley RD, Rovers MM, Reitsma JB, Moons KGM, Cochrane IPD Meta-analysis Methods group. 2015. Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use. PLoS Med, vol. 12(10), e1001886. link> doi>
  • Ruifrok AE, Rogozinska E, van Poppel MNM, Rayanagoudar G, Kerry S, de Groot CJM, Yeo S, Molyneaux E, Barakat Carballo R, Perales M, Bogaerts A, Cecatti JG, Surita F, Dodd J, Owens J, El Beltagy N, Devlieger R, Teede H, Harrison C, Haakstad L, Shen GX, Shub A, Motahari N, Khoury J, Tonstad S, Luoto R, Kinnunen TI, Guelfi K, Facchinetti F, Petrella E, Phelan S, Scudeller TT, Rauh K, Hauner H, Renault K, Sagedal LR, Vistad I, Stafne SN, Mørkved S, Salvesen KÅ, Vinter C, Vitolo M, Astrup A, Geiker NRW, McAuliffe F, Poston L, Roberts T, Riley RD, Coomarasamy A, Khan KS, Mol BW, Thangaratinam S. 2015. Erratum to: Study protocol: differential effects of diet and physical activity based interventions in pregnancy on maternal and fetal outcomes: individual patient data (IPD) meta-analysis and health economic evaluation. Syst Rev, vol. 4, 101. link> doi>
  • Riley RD, Elia EG, Malin G, Hemming K, Price MP. 2015. Multivariate meta-analysis of prognostic factor studies with multiple cut-points and/or methods of measurement. Stat Med, vol. 34(17), 2481-2496. link> doi> full text>
  • Tierney JF, Vale C, Riley R, Smith CT, Stewart L, Clarke M, Rovers M. 2015. Individual Participant Data (IPD) Meta-analyses of Randomised Controlled Trials: Guidance on Their Use. PLoS Med, vol. 12(7), e1001855. link> doi> full text>
  • Takwoingi Y, Guo B, Riley RD, Deeks JJ. 2017. Performance of methods for meta-analysis of diagnostic test accuracy with few studies or sparse data. Stat Methods Med Res, vol. 26(4), 1896-1911. link> doi> full text>
  • Riley RD, Ahmed I, Debray TPA, Willis BH, Noordzij JP, Higgins JPT, Deeks JJ. 2015. Summarising and validating test accuracy results across multiple studies for use in clinical practice. Stat Med, vol. 34(13), 2081-2103. link> doi> full text>
  • Riley RD, Price MJ, Jackson D, Wardle M, Gueyffier F, Wang J, Staessen JA, White IR. 2015. Multivariate meta-analysis using individual participant data. Res Synth Methods, vol. 6(2), 157-174. link> doi> full text>
  • Snell KIE, Hua H, Debray TPA, Ensor J, Look MP, Moons KGM, Riley RD. 2016. Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model. J Clin Epidemiol, vol. 69, 40-50. link> doi> full text>
  • Frosi G, Riley RD, Williamson PR, Kirkham JJ. 2015. Multivariate meta-analysis helps examine the impact of outcome reporting bias in Cochrane rheumatoid arthritis reviews. J Clin Epidemiol, vol. 68(5), 542-550. link> doi>
  • Jordan RE, Majothi S, Heneghan NR, Blissett DB, Riley RD, Sitch AJ, Price MJ, Bates EJ, Turner AM, Bayliss S, Moore D, Singh S, Adab P, Fitzmaurice DA, Jowett S, Jolly K. 2015. Supported self-management for patients with moderate to severe chronic obstructive pulmonary disease (COPD): an evidence synthesis and economic analysis. Health Technol Assess, vol. 19(36), 1-516. link> doi>
  • Dretzke J, Riley RD, Lordkipanidzé M, Jowett S, O'Donnell J, Ensor J, Moloney E, Price M, Raichand S, Hodgkinson J, Bayliss S, Fitzmaurice D, Moore D. 2015. 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, vol. 19(37), 1-366. link> doi> full text>
  • Majothi S, Jolly K, Heneghan NR, Price MJ, Riley RD, Turner AM, Bayliss SE, Moore DJ, Singh SJ, Adab P, Fitzmaurice DA, Jordan RE. 2015. Supported self-management for patients with COPD who have recently been discharged from hospital: a systematic review and meta-analysis. Int J Chron Obstruct Pulmon Dis, vol. 10, 853-867. link> doi>
  • Stewart LA, Clarke M, Rovers M, Riley RD, Simmonds M, Stewart G, Tierney JF, PRISMA-IPD Development Group. 2015. Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD Statement. JAMA, vol. 313(16), 1657-1665. link> doi>
  • Malin GL, Morris RK, Riley RD, Teune MJ, Khan KS. 2015. When is birthweight at term (≥37 weeks' gestation) abnormally low? A systematic review and meta-analysis of the prognostic and predictive ability of current birthweight standards for childhood and adult outcomes. BJOG, vol. 122(5), 634-642. link> doi>
  • Haroon S, Adab P, Riley RD, Marshall T, Lancashire R, Jordan RE. 2015. Predicting risk of COPD in primary care: development and validation of a clinical risk score. BMJ Open Respir Res, vol. 2(1), e000060. link> doi>
  • Riley RD, Ahmed I, Ensor J, Takwoingi Y, Kirkham A, Morris RK, Noordzij JP, Deeks JJ. 2015. Meta-analysis of test accuracy studies: an exploratory method for investigating the impact of missing thresholds. Syst Rev, vol. 4, 12. link> doi> full text>
  • Efthimiou O, Mavridis D, Riley RD, Cipriani A, Salanti G. 2015. Joint synthesis of multiple correlated outcomes in networks of interventions. Biostatistics, vol. 16(1), 84-97. link> doi>
  • Sheppard JP, Hodgkinson J, Riley R, Martin U, Bayliss S, McManus RJ. 2015. Prognostic significance of the morning blood pressure surge in clinical practice: a systematic review. Am J Hypertens, vol. 28(1), 30-41. link> doi>
  • Croft P, Altman DG, Deeks JJ, Dunn KM, Hay AD, Hemingway H, LeResche L, Peat G, Perel P, Petersen SE, Riley RD, Roberts I, Sharpe M, Stevens RJ, Van Der Windt DA, Von Korff M, Timmis A. 2015. The science of clinical practice: disease diagnosis or patient prognosis? Evidence about "what is likely to happen" should shape clinical practice. BMC Med, vol. 13, 20. link> doi> full text>
  • Dretzke J, Ensor J, Bayliss S, Hodgkinson J, Lordkipanidzé M, Riley RD, Fitzmaurice D, Moore D. 2014. Methodological issues and recommendations for systematic reviews of prognostic studies: an example from cardiovascular disease. Syst Rev, vol. 3, 140. link> doi>
  • Yamaguchi Y, Sakamoto W, Goto M, Staessen JA, Wang J, Gueyffier F, Riley RD. 2014. Meta-analysis of a continuous outcome combining individual patient data and aggregate data: a method based on simulated individual patient data. Res Synth Methods, vol. 5(4), 322-351. link> doi>
  • Ruifrok AE, Rogozinska E, van Poppel MNM, Rayanagoudar G, Kerry S, de Groot CJM, Yeo S, Molyneaux E, McAuliffe FM, Poston L, Roberts T, Riley RD, Coomarasamy A, Khan K, Mol BW, Thangaratinam S, i-WIP (International Weight Management in Pregnancy) Collaborative Group. 2014. Study protocol: differential effects of diet and physical activity based interventions in pregnancy on maternal and fetal outcomes--individual patient data (IPD) meta-analysis and health economic evaluation. Syst Rev, vol. 3, 131. link> doi>
  • Jordan RE, Adab P, Jowett S, Marsh JL, Riley RD, Enocson A, Miller MR, Cooper BG, Turner AM, Ayres JG, Cheng KK, Jolly K, Stockley RA, Greenfield S, Siebert S, Daley A, Fitzmaurice DA. 2014. TargetCOPD: a pragmatic randomised controlled trial of targeted case finding for COPD versus routine practice in primary care: protocol. BMC Pulm Med, vol. 14, 157. link> doi>
  • Burke DL, Billingham LJ, Girling AJ, Riley RD. 2014. Meta-analysis of randomized phase II trials to inform subsequent phase III decisions. Trials, vol. 15, 346. link> doi>
  • Peat G, Riley RD, Croft P, Morley KI, Kyzas PA, Moons KGM, Perel P, Steyerberg EW, Schroter S, Altman DG, Hemingway H, PROGRESS Group. 2014. Improving the transparency of prognosis research: the role of reporting, data sharing, registration, and protocols. PLoS Med, vol. 11(7), e1001671. link> doi> full text>
  • Perel P, Clayton T, Altman DG, Croft P, Douglas I, Hemingway H, Hingorani A, Morley KI, Riley R, Timmis A, Van der Windt D, Roberts I, PROGRESS Partnership. 2014. Red blood cell transfusion and mortality in trauma patients: risk-stratified analysis of an observational study. PLoS Med, vol. 11(6), e1001664. link> doi> full text>
  • Morris RK, Meller CH, Tamblyn J, Malin GM, Riley RD, Kilby MD, Robson SC, Khan KS. 2014. Association and prediction of amniotic fluid measurements for adverse pregnancy outcome: systematic review and meta-analysis. BJOG, vol. 121(6), 686-699. link> doi>
  • Malottki K, Biswas M, Deeks JJ, Riley RD, Craddock C, Johnson P, Billingham L. 2014. Stratified medicine in European Medicines Agency licensing: a systematic review of predictive biomarkers. BMJ Open, vol. 4(1), e004188. link> doi>
  • Ahmed I, Debray TPA, Moons KGM, Riley RD. 2014. Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med Res Methodol, vol. 14, 3. link> doi>
  • Tudur Smith C, Dwan K, Altman DG, Clarke M, Riley R, Williamson PR. 2014. Sharing individual participant data from clinical trials: an opinion survey regarding the establishment of a central repository. PLoS One, vol. 9(5), e97886. link> doi>
  • Ensor J, Riley RD, Moore D, Bayliss S, Jowett S, Fitzmaurice DA. 2013. Protocol for a systematic review of prognostic models for the recurrence of venous thromboembolism (VTE) following treatment for a first unprovoked VTE. Syst Rev, vol. 2, 91. link> doi>
  • Riley RD, Kauser I, Bland M, Thijs L, Staessen JA, Wang J, Gueyffier F, Deeks JJ. 2013. Meta-analysis of randomised trials with a continuous outcome according to baseline imbalance and availability of individual participant data. Stat Med, vol. 32(16), 2747-2766. link> doi>
  • Hemingway H, Croft P, Perel P, Hayden JA, Abrams K, Timmis A, Briggs A, Udumyan R, Moons KGM, Steyerberg EW, Roberts I, Schroter S, Altman DG, Riley RD, PROGRESS Group. 2013. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ, vol. 346, e5595. link> doi>
  • Hingorani AD, Windt DAVD, Riley RD, Abrams K, Moons KGM, Steyerberg EW, Schroter S, Sauerbrei W, Altman DG, Hemingway H, PROGRESS Group. 2013. Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ, vol. 346, e5793. link> doi>
  • Riley RD, Hayden JA, Steyerberg EW, Moons KGM, Abrams K, Kyzas PA, Malats N, Briggs A, Schroter S, Altman DG, Hemingway H, PROGRESS Group. 2013. Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. PLoS Med, vol. 10(2), e1001380. link> doi>
  • Steyerberg EW, Moons KGM, van der Windt DA, Hayden JA, Perel P, Schroter S, Riley RD, Hemingway H, Altman DG, PROGRESS Group. 2013. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med, vol. 10(2), e1001381. link> doi> full text>
  • Raichand S, Moore D, Riley RD, Lordkipanidzé M, Dretzke J, O'Donnell J, Jowett S, Bayliss S, Fitzmaurice DA. 2013. Protocol for a systematic review of the diagnostic and prognostic utility of tests currently available for the detection of aspirin resistance in patients with established cardiovascular or cerebrovascular disease. Syst Rev, vol. 2, 16. link> doi>
  • Morris RK, Riley RD, Doug M, Deeks JJ, Kilby MD. 2012. Diagnostic accuracy of spot urinary protein and albumin to creatinine ratios for detection of significant proteinuria or adverse pregnancy outcome in patients with suspected pre-eclampsia: systematic review and meta-analysis. BMJ, vol. 345, e4342. link> doi>
  • Ahmed I, Sutton AJ, Riley RD. 2012. Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey. BMJ, vol. 344, d7762. link> doi>
  • McMinn DJW, Snell KIE, Daniel J, Treacy RBC, Pynsent PB, Riley RD. 2012. Mortality and implant revision rates of hip arthroplasty in patients with osteoarthritis: registry based cohort study. BMJ, vol. 344, e3319. link> doi>
  • Riley RD, Gates S, Neilson J, Alfirevic Z. 2011. Statistical methods can be improved within Cochrane pregnancy and childbirth reviews. J Clin Epidemiol, vol. 64(6), 608-618. link> doi>
  • Riley RD, Higgins JPT, Deeks JJ. 2011. Interpretation of random effects meta-analyses. BMJ, vol. 342, d549. link> doi>
  • Riley RD. 2010. Commentary: like it and lump it? Meta-analysis using individual participant data. Int J Epidemiol, vol. 39(5), 1359-1361. link> doi>
  • Riley RD, Lambert PC, Abo-Zaid G. 2010. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ, vol. 340, c221. link> doi>
  • Riley RD and Steyerberg EW. 2010. Meta-analysis of a binary outcome using individual participant data and aggregate data. Res Synth Methods, vol. 1(1), 2-19. link> doi>
  • Riley RD, Sauerbrei W, Altman DG. 2009. Prognostic markers in cancer: the evolution of evidence from single studies to meta-analysis, and beyond. Br J Cancer, vol. 100(8), 1219-1229. link> doi>
  • McKinley RK, Fraser RC, Baker RH, Riley RD. 2004. The relationship between measures of patient satisfaction and enablement and professional assessments of consultation competence. Med Teach, vol. 26(3), 223-228. link> doi>

Other

  • Holden MA, Runhaar J, Burke DL, van der Windt D, Riley RD, Dziedzic K, Legha A, Quicke JG, Healey EL, Bourton AL, Bierma-Zeinstra S, Abbott JH, Anwer S, Baker K, Brown J, Bennell KL, Bossen D, Chaipinyo K, Christensen R, Cochrane T, de Rooij M, Doherty M, French HP, Ganesh S, Henriksen M, Heinonen A, Hickson S, Hinman RS, Hurley MV, Ingram C, Knoop J, Kraus I, Lun V, Lacerda AC, Lund H, McCarthy C, Messier SP, Osteras H, Patrick D, Risberg MA, Sahin N, Steinhilber B, Talbot L, Taylor R, Teirlinck CH, Tsai P-F, van Middelkoop M, Walker C, Foster NE, Bank OAT. 2019. SUBGROUPING AND TARGETED EXERCISE PROGRAMMES FOR KNEE AND HIP OSTEOARTHRITIS (STEER OA) INDIVIDUAL PARTICIPANT DATA META-ANALYSIS. PROGRESS UPDATE AND SELECTION OF POTENTIAL MODERATORS FOR ANALYSES. OSTEOARTHRITIS AND CARTILAGE (vol. 27, p. S446). link> doi>
  • Holden MA, Burke DL, Runhaar J, van der Windt D, Riley RD, Dziedzic KK, Legha A, Evans AL, Abbott JH, Baker K, Brown J, Bennell KL, Bossen D, Brosseau L, Chaipinyo K, Christensen R, Cochrane T, de Rooij M, Doherty M, French HP, Hickson S, Hinman RS, Hopman-Rock M, Hurley MV, Ingram C, Knoop J, Krauss I, McCarthy C, Messier SP, Patrick DL, Sahin N, Talbot LA, Taylor R, Teirlincki CH, van Middelkoopi M, Walker C, Foster NE, Bank OAT. 2018. SUBGROUPING AND TARGETED EXERCISE PROGRAMMES FOR KNEE AND HIP OSTEOARTHRITIS (STEER OA): AN INDIVIDUAL PARTICIPANT DATA META-ANALYSIS INITIATIVE. OSTEOARTHRITIS AND CARTILAGE (vol. 26, p. S326). link> doi>
  • Mackie FL, Hall M, Hyett J, Mills I, Riley R, Morris RK, Kilby MD. 2017. First trimester prediction of adverse events in monochorionic diamniotic twins: The OMMIT study. BJOG-AN INTERNATIONAL JOURNAL OF OBSTETRICS AND GYNAECOLOGY (vol. 124, p. 6). link>
  • Thangaratinam S, Marlin N, Dodds J, Kerry S, Riley RD, Mol BW, Khan KS, Rogozinska E, Pregnancy IWM. 2017. Effects of diet and physical activity-based interventions on maternal and fetal outcomes in pregnancy - an individual patient data (ipd) meta-analysis of randomised trials. AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY (vol. 216, pp. S352-S353). link>
  • Thangaratinam S, Marlin N, Dodds J, Kerry S, Riley RD, Mol B, Khan K, Rogozinska E. 2016. 598: Effects of diet and physical activity-based interventions on maternal and fetal outcomes in pregnancy - an individual patient data (ipd) meta-analysis of randomised trials. American Journal of Obstetrics and Gynecology (vol. 216, pp. S352-S353). Mosby Inc.. doi>
  • Bilagi A, Burke D, Mills I, Riley R, Kilby MD, Morris RK. 2016. Can first trimester serum pregnancy associated plasma protein A (PAPP-A) predict adverse pregnancy outcome? Retrospective cohort study. BJOG-AN INTERNATIONAL JOURNAL OF OBSTETRICS AND GYNAECOLOGY (vol. 123, p. 94). link>
  • Thangaratinam S, Allotey J, Marlin N, Mol D, Dodds J, Kerry S, Moons C, Riley RD, Khan KS. 2016. Development and validation of a prediction model for the risk of adverse outcomes in women with early onset pre-eclampsia (PREP): Prospective cohort study. BJOG-AN INTERNATIONAL JOURNAL OF OBSTETRICS AND GYNAECOLOGY (vol. 123, p. 12). link>
  • Bilagi A, Burke D, Mills I, Riley RD, Kilby MD, Morris RK. 2016. Can first-trimester serum pregnancy-associated plasma protein A predict adverse pregnancy outcome? Retrospective cohort study. BJOG-AN INTERNATIONAL JOURNAL OF OBSTETRICS AND GYNAECOLOGY (vol. 123, p. 74). link>
  • Robb A, Elia E, Hemming K, Price M, Riley R, French-Constant A, Denison F, Kilby M, Stock S, Morris RK. 2016. Could urinary albumin: creatinine ratio be used to predict adverse outcomes in suspected pre-eclampsia?. BJOG-AN INTERNATIONAL JOURNAL OF OBSTETRICS AND GYNAECOLOGY (vol. 123, p. 17). link>
  • Allotey J, Thangaratinam S, Marlin N, Mol B, Von Dadelszen P, Ganzevoort W, Akkermans J, Ahmed A, Daniels J, Deeks J, Ismail K, Barnard AM, Dodds J, Kerry S, Moons C, Riley RD, Khan KS. 2016. Development and validation of a prediction model for the risk of adverse outcomes in women with early onset preeclampsia (PREP): prospective cohort study. AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY (vol. 214, p. S409). link> doi>
  • Riley RD. 2015. 782: Development and validation of a predication model for the risk of adverse outcomes in women with early onset preeclampsia (PREP): prospective cohort study. American Journal of Obstetrics and Gynecology. Elsevier. doi>
  • Malottki K, Billingham L, Riley R, Deeks J. 2015. Reviewing the evidence supporting predictive biomarkers in European medicines agency indications and contraindications using visual plots. TRIALS (vol. 16). link> doi> full text>
  • Jordan R, Adab P, Sitch A, Encoson A, Jowett S, Blissett D, Marsh J, Riley R, Miller M, Cooper B, Turner A, Ayres J, Cheng KK, Jolly K, Stockley R, Greenfield S, Siebert S, Daley A, Fitzmaurice D. 2015. TargetCOPD: A pragmatic randomised controlled trial of targeted case finding for COPD versus routine practice in primary care. EUROPEAN RESPIRATORY JOURNAL (vol. 46). link> doi>

Courses

Richard delivers five training courses:

(1) PROGNOSIS RESEARCH: CONCEPTS, METHODS AND CLINICAL APPLICATION

Prognosis research provides information crucial to understanding, explaining and predicting future clinical outcomes in people with existing disease or health conditions. This 3-day course is designed to introduce the key components of prognosis research to health professionals and researchers, including: (i) a framework of different prognosis research questions (overall prognosis, prognostic factors, prognostic models, and stratified medicine); (ii) key principles of study design and methods; (iii) interpretation of statistical results about prognosis; and (iv) the limitations of current prognosis research, and how the field can be improved. The course is based on 4 articles published in BMJ/PLOS Medicine (February 2013) and course combines seminars and lectures by international experts in the field (Prof. Harry Hemingway, Prof. Douglas Altman, Prof. Richard Riley, Prof. Danielle van der Windt, Dr. Kate Dunn, Dr. Pablo Perel) with group work and case studies. For more information and registration: http://progress-partnership.org/training/2016-summer-school/

(2) STATISTICAL METHODS FOR INDIVIDUAL PARTICIPANT DATA (IPD) META-ANALYSIS

The course covers the fundamental statistical methods and principles for meta-analysis when IPD (Individual Participant/Patient Data) are available from multiple related studies. The course considers continuous, binary and time-to-event outcomes. It focuses mainly on the synthesis of randomised trials of interventions, and how to estimate treatment effects and treatment-covariate interactions (effect modifiers). However, it also covers the issues for IPD meta-analysis of observational studies examining prognostic factors and risk prediction (prognostic) models. Methods for examining bias in IPD meta-analyses is also described, alongside approaches for combining IPD and non-IPD studies. Key messages are illustrated with real examples, and participants get the chance to conduct a variety of IPD analyses within STATA, to practice the key methods and to reinforce the learning points. The course assumes an understanding of regression methods (such as linear, logistic, and Cox) and the basic principles of traditional meta-analysis methods that synthesise only published results. This course is booked for December  6-7 2016.  You can register for the course here .

(3) STATISTICAL METHODS FOR RISK PREDICTION & PROGNOSTIC MODELS

Patients and their care providers often want to know the risk of developing an (adverse) health outcome over time. Estimates of future risk ('prognosis') allow patients and their families to put a clinical diagnosis in context, and inform clinical decisions and treatment strategies. For this purpose there is a growing interest in risk prediction and prognostic models, which are statistical models that estimate an individual's outcome risk. This 3-day course provides a thorough foundation of statistical methods for developing and/or validating prognostic models in clinical research. For model development, we cover the use of logistic regression, Cox regression, and flexible parametric survival models; sample size, identification of candidate predictors, and selection procedures; handling missing data, including multiple imputation; handling of continuous predictors, including restricted cubic splines and fractional polynomial approaches; bootstrapping and shrinkage to adjust for optimism in apparent model performance; and complexities in the data, such as competing risks and recurrent events. For model validation, we cover internal and external validation; statistical measures of discrimination (including the C-statistic and D-statistic); statistical measures for calibration (such as the calibration slope and calibration-in-the-large); and re-calibration techniques. 'Hot topics' will also be covered on the final day, including the development and validation of prognostic models using large databases (e.g. from e-health records) or individual participant data from multiple studies; systematic reviews of prognostic model studies, including examining risk of bias; and meta-analysis methods for summarising the performance of prognostic models across multiple studies. The course is aimed at researchers who want to understand and implement core statistical methods for the development and validation of a prognostic model. There will be a mixture of lectures and computer practical work. Participants need to bring a laptop with STATA version 12 or above. The course is planned to run in April 2017, and the course will be announced formally in September 2016,  but for more information contact: r.riley@keele.ac.uk

(4) AN INTROUDUCTION TO STATISTICAL METHODS FOR META-ANALYSIS 

This course covers the basic principles for undertaking a meta-analysis of results extracted from the literature. It focuses mainly on the synthesis of results from randomised trials of interventions, but also includes discussion of prognostic factor studies and diagnostic test studies. The course covers the process of data extraction, and the concept of fixed-effect and random-effects meta-analysis models. Continuous, binary and time-to-event outcomes are covered, and the core meta-analysis methods are covered, such as inverse-variance, Dersimonian and Laird (random effects), and exact likelihood methods for binary data. It also explains how to interpret the summary results from a meta-analysis, and display them appropriately using forest plots, confidence intervals, and prediction intervals. Methods for examining potential publication bias are covered, including contour-enhanced funnel plots and tests for asymmetry. Meta-regression is also detailed, for examining treatment-covariate interaction, and the threat of ecological bias and potential advantages of individual participant data are discussed. The course is a mixture of lectures and small group practicals. STATA will be used to demonstrate the key methods. For more information or if you would like this course run for your team, contact: r.riley@keele.ac.uk

(5) AN INTRODUCTION TO MULTIVARIATE META-ANALYSIS

Many primary studies have more than one outcome of interest, such as disease-free survival and overall survival, and researchers usually meta-analyse each outcome separately. However, such multiple outcomes are often related to each other, i.e. they are correlated. For example, a patient’s time to recurrence of disease is generally associated with their time of death. By meta-analysing each outcome independently, researchers ignore this correlation and thus lose potentially valuable information. In this course, we show how a multivariate meta-analysis can jointly analyse multiple outcomes and account for their correlation. We show why this gets the most out of the available data, and leads to advantages over standard univariate methods (which analyse each outcome separately). The course covers topics such as: univariate & multivariate model specification, model estimation, obtaining within-study correlations, multivariate heterogeneity statistics, use of individual participant data, advantages, applications, and limitations. A full demonstration of the freely available 'mvmeta' module in STATA is also provided, so as to give researchers confidence to apply the multivariate approach in practice. Application areas include diagnostic tests, prognostic factors, treatment effects, multiple outcomes, trend estimation, and trials with outcome reporting bias. Use of multivariate meta-analysis for network meta-analysis (i.e. the joint synthesis of multiple treatments) is also briefly covered. A reasonable grounding in statistics is assumed. Some knowledge of meta-analysis and randomised controlled trials would be of help, but is not essential. The day begins with an overview of the basics of univariate meta-analysis methods, before then extending to the multivariate setting. 

For more information or if you would like this course run for your team, please contact: r.riley@keele.ac.uk