Journal Articles

• van Geloven N, Giardiello D, Bonneville EF, Teece L, Ramspek CL, van Smeden M, Snell KIE, van Calster B, Pohar-Perme M, Riley RD, Putter H, Steyerberg E, STRATOS initiative. 2022. Validation of prediction models in the presence of competing risks: a guide through modern methods. BMJ, e069249, vol. 377. link> doi>
• Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS. 2022. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med Res Methodol, 101, vol. 22(1). link> doi> full text>
• Farooq S, Hattle M, Dazzan P, Kingstone T, Ajnakina O, Shiers D, Nettis MA, Lawrence A, Riley R, van der Windt D. 2022. Study protocol for the development and internal validation of Schizophrenia Prediction of Resistance to Treatment (SPIRIT): a clinical tool for predicting risk of treatment resistance to antipsychotics in first-episode schizophrenia. BMJ Open, e056420, vol. 12(4). link> doi> full text>
• Siegel L, Murad MH, Riley RD, Bazerbachi F, Wang Z, Chu H. 2022. A Guide to Estimating the Reference Range From a Meta-Analysis Using Aggregate or Individual Participant Data. Am J Epidemiol, 948-956, vol. 191(5). link> doi>
• Allotey J, Whittle R, Snell KIE, Smuk M, Townsend R, von Dadelszen P, Heazell AEP, Magee L, Smith GCS, Sandall J, Thilaganathan B, Zamora J, Riley RD, Khalil A, Thangaratinam S, IPPIC Collaborative Network. 2022. External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis. Ultrasound Obstet Gynecol, 209-219, vol. 59(2). link> doi> full text>
• Andaur Navarro CL, Damen JAA, Takada T, Nijman SWJ, Dhiman P, Ma J, Collins GS, Bajpai R, Riley RD, Moons KGM, Hooft L. 2022. Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. BMC Med Res Methodol, 12, vol. 22(1). link> doi>
• Moriarty AS, Meader N, Snell KIE, Riley RD, Paton LW, Dawson S, Hendon J, Chew-Graham CA, Gilbody S, Churchill R, Phillips RS, Ali S, McMillan D. 2022. Predicting relapse or recurrence of depression: systematic review of prognostic models. Br J Psychiatry, 1-11. link> doi> full text>
• Ramspek CL, Teece L, Snell KIE, Evans M, Riley RD, van Smeden M, van Geloven N, van Diepen M. 2022. Lessons learnt when accounting for competing events in the external validation of time-to-event prognostic models. Int J Epidemiol, 615-625, vol. 51(2). link> doi> full text>
• Riley RD, Collins GS, Ensor J, Archer L, Booth S, Mozumder SI, Rutherford MJ, van Smeden M, Lambert PC, Snell KIE. 2022. 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>
• Foroutan F, Guyatt G, Trivella M, Kreuzberger N, Skoetz N, Riley RD, Roshanov PS, Alba AC, Sekercioglu N, Canelo-Aybar C, Munn Z, Brignardello-Petersen R, Schünemann HJ, Iorio A. 2022. GRADE concept paper 2: Concepts for judging certainty on the calibration of prognostic models in a body of validation studies. J Clin Epidemiol, 202-211, vol. 143. link> doi> full text>
• Adab P, Jordan RE, Fitzmaurice D, Ayres JG, Cheng KK, Cooper BG, Daley A, Dickens A, Enocson A, Greenfield S, Haroon S, Jolly K, Jowett S, Lambe T, Martin J, Miller MR, Rai K, Riley RD, Sadhra S, Sitch A, Siebert S, Stockley RA, Turner A. 2021. Case-finding and improving patient outcomes for chronic obstructive pulmonary disease in primary care: the BLISS research programme including cluster RCT. Programme Grants for Applied Research, 1-148, vol. 9(13). link> doi> link> full text>
• Andaur Navarro CL, Damen JAA, Takada T, Nijman SWJ, Dhiman P, Ma J, Collins GS, Bajpai R, Riley RD, Moons KGM, Hooft L. 2021. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ, n2281, vol. 375. link> doi> full text>
• Martin GP, Riley RD, Collins GS, Sperrin M. 2021. Developing clinical prediction models when adhering to minimum sample size recommendations: The importance of quantifying bootstrap variability in tuning parameters and predictive performance. Stat Methods Med Res, 2545-2561, vol. 30(12). link> doi> full text>
• Bullock GS, Hughes T, Sergeant JC, Callaghan MJ, Riley RD, Collins GS. 2021. Clinical Prediction Models in Sports Medicine: A Guide for Clinicians and Researchers. Journal of Orthopaedic and Sports Physical Therapy, 517-525, vol. 51(10). link> doi> link> full text>
• Wynne-Jones G, Myers H, Hall A, Littlewood C, Hennings S, Saunders B, Bucknall M, Jowett S, Riley R, Wathall S, Heneghan C, Cook J, Pincus T, Mallen C, Roddy E, Foster N, Beard D, Lewis J, Rees JL, Higginbottom A, van der Windt D. 2021. Predicting pain and function outcomes in people consulting with shoulder pain: the PANDA-S clinical cohort and qualitative study protocol. BMJ Open, e052758, vol. 11(9). link> doi> full text>
• Stock SJ, Horne M, Bruijn M, White H, Heggie R, Wotherspoon L, Boyd K, Aucott L, Morris RK, Dorling J, Jackson L, Chandiramani M, David A, Khalil A, Shennan A, Baaren G-JV, Hodgetts-Morton V, Lavender T, Schuit E, Harper-Clarke S, Mol B, Riley RD, Norman J, Norrie J. 2021. A prognostic model, including quantitative fetal fibronectin, to predict preterm labour: the QUIDS meta-analysis and prospective cohort study. Health Technol Assess, 1-168, vol. 25(52). link> doi> full text>
• Riley RD. 2021. Applied meta-analysis with R and Stata. BIOMETRICAL JOURNAL, 1745-1746, vol. 63(8). link> doi> full text>
• Hoogland J, IntHout J, Belias M, Rovers MM, Riley RD, E. Harrell Jr F, Moons KGM, Debray TPA, Reitsma JB. 2021. A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint. Statistics in Medicine, 1-21. link> doi> link> full text>
• Ramspek CL, Steyerberg EW, Riley RD, Rosendaal FR, Dekkers OM, Dekker FW, van Diepen M. 2021. Prediction or causality? A scoping review of their conflation within current observational research. European Journal of Epidemiology. doi> full text>
• Hayden JA and Riley RD. 2021. Research Note: Individual participant data (IPD) meta-analysis. J Physiother, 224-227, vol. 67(3). link> doi> full text>
• Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, Logullo P, Beam AL, Peng L, Van Calster B, van Smeden M, Riley RD, Moons KG. 2021. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open, e048008, vol. 11(7). link> doi> full text>
• Stock SJ, Horne M, Bruijn M, White H, Boyd KA, Heggie R, Wotherspoon L, Aucott L, Morris RK, Dorling J, Jackson L, Chandiramani M, David AL, Khalil A, Shennan A, van Baaren G-J, Hodgetts-Morton V, Lavender T, Schuit E, Harper-Clarke S, Mol BW, Riley RD, Norman JE, 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, vol. 18(7). link> doi> link> full text>
• Bullock GS, Hughes T, Sergeant JC, Callaghan MJ, Riley R, Collins G. 2021. Methods matter: clinical prediction models will benefit sports medicine practice, but only if they are properly developed and validated. Br J Sports Med, 1319-1321, vol. 55(23). link> doi> full text>
• Moriarty AS, Paton LW, Snell KIE, Riley RD, Buckman JEJ, Gilbody S, Chew-Graham CA, Ali S, Pilling S, Meader N, Phillips B, Coventry PA, Delgadillo J, Richards DA, Salisbury C, McMillan D. 2021. The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study. Diagnostic and Prognostic Research, 12, vol. 5(1). link> doi> full text>
• Andaur Navarro C, Damen J, Takada T, Nijman S, Dhiman P, Ma J, Collins G, Bajpai R, Riley R, Moons KGM, Hooft L. 2021. Completeness of reporting of clinical prediction models developed using supervised machine learning: A systematic review. doi> full text>
• Dhiman P, Ma J, Navarro CA, Speich B, Bullock G, Damen JA, Kirtley S, Hooft L, Riley RD, Van Calster B, Moons KGM, Collins GS. 2021. Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. J Clin Epidemiol, 60-72, vol. 138. link> doi> full text>
• Coomar D, Hazlehurst JM, Austin F, Foster C, Hitman GA, Heslehurst N, Iliodromiti S, Betran AP, Moss N, Poston L, Nirantharakumar K, Roberts T, Simpson SA, Teede HJ, Riley R, Allotey J, Thangaratinam S, International Weight Management in Pregnancy (i-WIP) Collaborative Group. 2021. Diet and physical activity in pregnancy to prevent gestational diabetes: a protocol for an individual participant data (IPD) meta-analysis on the differential effects of interventions with economic evaluation. BMJ Open, e048119, vol. 11(6). link> doi> full text>
• Ban L, Abdul Sultan A, West J, Tata LJ, Riley RD, Nelson-Piercy C, Grainge MJ. 2021. External validation of a model to predict women most at risk of postpartum venous thromboembolism: Maternity clot risk. Thromb Res, 202-210, vol. 208. link> doi> full text>
• Van Calster B, Wynants L, Riley RD, van Smeden M, Collins GS. 2021. Methodology over metrics: Current scientific standards are a disservice to patients and society. Journal of Clinical Epidemiology. link> doi> full text>
• Wynne-Jones G, Myers H, Hall A, Littlewood C, Hennings S, Saunders B, Bucknall M, Jowett S, Riley R, Wathall S, Heneghan C, Cook J, Pincus T, Mallen C, Roddy E, Foster NE, Beard D, Lewis J, Rees J, Higginbottom A, Van der Windt DA. 2021. Predicting pain and function outcomes in people consulting with shoulder pain: The PANDA-S clinical cohort and qualitative study protocol (ISRCTN 46948079). doi> full text>
• Chappell FM, Crawford F, Horne M, Leese GP, Martin A, Weller D, Boulton AJM, Abbott C, Monteiro-Soares M, Veves A, Riley RD. 2021. Development and validation of a clinical prediction rule for development of diabetic foot ulceration: an analysis of data from five cohort studies. BMJ Open Diabetes Res Care, vol. 9(1). link> doi> full text>
• Riley RD, Debray TPA, Collins GS, Archer L, Ensor J, van Smeden M, Snell KIE. 2021. 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>
• Levis B, Hattle M, Riley RD. 2021. PRIME-IPD SERIES Part 2. Retrieving, checking, and harmonizing data are underappreciated challenges in individual participant data meta-analyses. J Clin Epidemiol, 221-223, vol. 136. link> doi> full text>
• de Jong VMT, Moons KGM, Eijkemans MJC, Riley RD, Debray TPA. 2021. Developing more generalizable prediction models from pooled studies and large clustered data sets. Stat Med, 3533-3559, vol. 40(15). link> doi> full text>
• Moriarty AS, Meader N, Snell KI, Riley RD, Paton LW, Chew-Graham CA, Gilbody S, Churchill R, Phillips RS, Ali S, McMillan D. 2021. Prognostic models for predicting relapse or recurrence of major depressive disorder in adults. Cochrane Database Syst Rev, CD013491, vol. 5. link> doi> full text>
• Ensor J, Snell KIE, Debray TPA, Lambert PC, Look MP, Mamas MA, Moons KGM, Riley RD. 2021. Individual participant data meta-analysis for external validation, recalibration, and updating of a flexible parametric prognostic model. Statistics in Medicine, 3066-3084, vol. 40(13). link> doi> link> full text>
• van Smeden M, Reitsma JB, Riley RD, Collins GS, Moons KG. 2021. Clinical prediction models: diagnosis versus prognosis. J Clin Epidemiol, 142-145, vol. 132. link> doi> full text>
• Martin GP, Sperrin M, Snell KIE, Buchan I, Riley RD. 2021. Authors' reply to Sabour and Ghajari "Clinical prediction models to predict the risk of multiple binary outcomes: Methodological issues". Stat Med, 1861-1862, vol. 40(7). link> doi>
• Crocker TF, Clegg A, Riley RD, Lam N, Bajpai R, Jordão M, Patetsini E, Ramiz R, Ensor J, Forster A, Gladman JRF. 2021. 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>
• Snell KIE, Archer L, Ensor J, Bonnett LJ, Debray TPA, Phillips B, Collins GS, Riley RD. 2021. 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>
• Albasri A, Hattle M, Koshiaris C, Dunnigan A, Paxton B, Fox SE, Smith M, Archer L, Levis B, Payne RA, Riley RD, Roberts N, Snell KIE, Lay-Flurrie S, Usher-Smith J, Stevens R, Hobbs FDR, McManus RJ, Sheppard JP, STRATIFY investigators. 2021. Association between antihypertensive treatment and adverse events: systematic review and meta-analysis. BMJ, n189, vol. 372. link> doi> full text>
• Archer L, Snell KIE, Ensor J, Hudda MT, Collins GS, Riley RD. 2021. 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>
• Jenkins DA, Martin GP, Sperrin M, Riley RD, Debray TPA, Collins GS, Peek N. 2021. Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?. Diagn Progn Res, 1, vol. 5(1). link> doi> full text>
• Collins SD, Peek N, Riley RD, Martin GP. 2021. Sample sizes of prediction model studies in prostate cancer were rarely justified and often insufficient. J Clin Epidemiol, 53-60, vol. 133. link> doi> full text>
• Riley RD, Van Calster B, Collins GS. 2021. A note on estimating the Cox-Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome. Stat Med, 859-864, vol. 40(4). link> doi> full text>
• Riley RD, Snell KIE, Martin GP, Whittle R, Archer L, Sperrin M, Collins GS. 2021. Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small. J Clin Epidemiol, 88-96, vol. 132. link> doi> full text>
• Allotey J, Snell KI, Smuk M, Hooper R, Chan CL, Ahmed A, Chappell LC, von Dadelszen P, Dodds J, Green M, Kenny L, Khalil A, Khan KS, Mol BW, Myers J, Poston L, Thilaganathan B, Staff AC, Smith GC, Ganzevoort W, Laivuori H, Odibo AO, Ramírez JA, Kingdom J, Daskalakis G, Farrar D, Baschat AA, Seed PT, Prefumo F, da Silva Costa F, Groen H, Audibert F, Masse J, Skråstad RB, Salvesen KÅ, Haavaldsen C, Nagata C, Rumbold AR, Heinonen S, Askie LM, Smits LJ, Vinter CA, Magnus PM, Eero K, Villa PM, Jenum AK, Andersen LB, Norman JE, Ohkuchi A, Eskild A, Bhattacharya S, McAuliffe FM, Galindo A, Herraiz I, Carbillon L, Klipstein-Grobusch K, Yeo S, Teede HJ, Browne JL, Moons KG, Riley RD, Thangaratinam S. 2020. Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis. Health Technol Assess, 1-252, vol. 24(72). link> doi> full text>
• Andaur Navarro CL, Damen JAAG, Takada T, Nijman SWJ, Dhiman P, Ma J, Collins GS, Bajpai R, Riley RD, Moons KG, Hooft L. 2020. Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques. BMJ Open, e038832, vol. 10(11). link> doi> full text>
• Snell KIE, Allotey J, Smuk M, Hooper R, Chan C, Ahmed A, Chappell LC, Von Dadelszen P, Green M, Kenny L, Khalil A, Khan KS, Mol BW, Myers J, Poston L, Thilaganathan B, Staff AC, Smith GCS, Ganzevoort W, Laivuori H, Odibo AO, Arenas Ramírez J, Kingdom J, Daskalakis G, Farrar D, Baschat AA, Seed PT, Prefumo F, da Silva Costa F, Groen H, Audibert F, Masse J, Skråstad RB, Salvesen KÅ, Haavaldsen C, Nagata C, Rumbold AR, Heinonen S, Askie LM, Smits LJM, Vinter CA, Magnus P, Eero K, Villa PM, Jenum AK, Andersen LB, Norman JE, Ohkuchi A, Eskild A, Bhattacharya S, McAuliffe FM, Galindo A, Herraiz I, Carbillon L, Klipstein-Grobusch K, Yeo SA, Browne JL, Moons KGM, Riley RD, Thangaratinam S, IPPIC Collaborative Network. 2020. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis. BMC Med, 302, vol. 18(1). link> doi> full text>
• Crawford F, Chappell FM, Lewsey J, Riley R, Hawkins N, Nicolson D, Heggie R, Smith M, Horne M, Amanna A, Martin A, Gupta S, Gray K, Weller D, Brittenden J, Leese G. 2020. Risk assessments and structured care interventions for prevention of foot ulceration in diabetes: development and validation of a prognostic model. Health Technology Assessment, 1-198, vol. 24(62). link> doi> link> full text>
• Martin GP, Sperrin M, Snell KIE, Buchan I, Riley RD. 2021. Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches. Stat Med, 498-517, vol. 40(2). link> doi> full text>
• Collins GS, Riley RD, van Smeden M. 2020. Flaws in the development and validation of a covid-19 prediction model. Clinical Infectious Diseases. link> doi> link> full text>
• Townsend R, Manji A, Allotey J, Heazell A, Jorgensen L, Magee LA, Mol BW, Snell K, Riley RD, Sandall J, Smith G, Patel M, Thilaganathan B, von Dadelszen P, Thangaratinam S, Khalil A. 2021. Can risk prediction models help us individualise stillbirth prevention? A systematic review and critical appraisal of published risk models. BJOG, 214-224, vol. 128(2). link> doi> full text>
• Hong C, Salanti G, Morton SC, Riley RD, Chu H, Kimmel SE, Chen Y. 2020. Testing small study effects in multivariate meta-analysis. Biometrics, 1240-1250, vol. 76(4). link> doi> full text>
• Collins GS, van Smeden M, Riley RD. 2020. COVID-19 prediction models should adhere to methodological and reporting standards. Eur Respir J. link> doi> full text>
• Rücker G, Nikolakopoulou A, Papakonstantinou T, Salanti G, Riley RD, Schwarzer G. 2020. The statistical importance of a study for a network meta-analysis estimate. BMC Med Res Methodol, 190, vol. 20(1). link> doi> full text>
• Sauerbrei W, Bland M, Evans SJW, Riley RD, Royston P, Schumacher M, Collins GS. 2021. Doug Altman: Driving critical appraisal and improvements in the quality of methodological and medical research. Biom J, 226-246, vol. 63(2). link> doi> full text>
• Papadimitropoulou K, Stijnen T, Riley RD, Dekkers OM, le Cessie S. 2020. Meta-analysis of continuous outcomes: using pseudo IPD created from aggregate data to adjust for baseline imbalance and assess treatment-by-baseline modification. Research Synthesis Methods. link> doi> link> full text>
• Hughes T, Riley RD, Callaghan MJ, Sergeant JC. 2020. The Value of Preseason Screening for Injury Prediction: The Development and Internal Validation of a Multivariable Prognostic Model to Predict Indirect Muscle Injury Risk in Elite Football (Soccer) Players. Sports Med Open, 22, vol. 6(1). link> doi> full text>
• Riley RD, Legha A, Jackson D, Morris TP, Ensor J, Snell KIE, White IR, Burke DL. 2020. 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>
• Riley RD, Debray TPA, Fisher D, Hattle M, Marlin N, Hoogland J, Gueyffier F, Staessen JA, Wang J, Moons KGM, Reitsma JB, Ensor J. 2020. 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>
• Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. 2020. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ, m1328, vol. 369. link> doi> full text>
• Booth S, Riley RD, Ensor J, Lambert PC, Rutherford MJ. 2020. 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>
• Riley RD, Ensor J, Snell KIE, Harrell FE, Martin GP, Reitsma JB, Moons KGM, Collins G, van Smeden M. 2020. Calculating the sample size required for developing a clinical prediction model. BMJ, m441, vol. 368. link> doi> full text>
• de Jong VMT, Moons KGM, Riley RD, Tudur Smith C, Marson AG, Eijkemans MJC, Debray TPA. 2020. Individual participant data meta-analysis of intervention studies with time-to-event outcomes: A review of the methodology and an applied example. Res Synth Methods, 148-168, vol. 11(2). link> doi> full text>
• Moriarty AS, Castleton J, Gilbody S, McMillan D, Ali S, Riley RD, Chew-Graham CA. 2020. Predicting and preventing relapse of depression in primary care. Br J Gen Pract, 54-55, vol. 70(691). link> doi> full text>
• Foroutan F, Guyatt G, Zuk V, Vandvik PO, Alba AC, Mustafa R, Vernooij R, Arevalo-Rodriguez I, Munn Z, Roshanov P, Riley R, Schandelmaier S, Kuijpers T, Siemieniuk R, Canelo-Aybar C, Schunemann H, Iorio A. 2020. GRADE Guidelines 28: Use of GRADE for the assessment of evidence about prognostic factors: rating certainty in identification of groups of patients with different absolute risks. J Clin Epidemiol, 62-70, vol. 121. link> doi> full text>
• Hong C, Duan R, Zeng L, Hubbard RA, Lumley T, Riley R, Chu H, Kimmel SE, Chen Y. 2020. Galaxy Plot: A New Visualization Tool of Bivariate Meta-Analysis Studies. Am J Epidemiol. link> doi> full text>
• Dickens AP, Fitzmaurice DA, Adab P, Sitch A, Riley RD, Enocson A, Jordan RE. 2020. Accuracy of Vitalograph lung monitor as a screening test for COPD in primary care. npj Primary Care Respiratory Medicine, 2, vol. 30(1). link> doi> link> full text>
• Takwoingi Y, Partlett C, Riley RD, Hyde C, Deeks JJ. 2020. Methods and reporting of systematic reviews of comparative accuracy were deficient: a methodological survey and proposed guidance. J Clin Epidemiol, 1-14, vol. 121. link> doi> full text>
• Moriarty AS, Meader N, Gilbody S, Chew-Graham CA, Churchill R, Ali S, Phillips RS, Riley RD, McMillan D. 2019. Prognostic models for predicting relapse or recurrence of depression. Cochrane Database of Systematic Reviews. doi> link> full text>
• Hayden JA, Wilson MN, Stewart S, Cartwright JL, Smith AO, Riley RD, van Tulder M, Bendix T, Cecchi F, Costa LOP, Dufour N, Ferreira ML, Foster NE, Gudavalli MR, Hartvigsen J, Helmhout P, Kool J, Koumantakis GA, Kovacs FM, Kuukkanen T, Long A, Macedo LG, Machado LAC, Maher CG, Mehling W, Morone G, Peterson T, Rasmussen-Barr E, Ryan CG, Sjögren T, Smeets R, Staal JB, Unsgaard-Tøndel M, Wajswelner H, Yeung EW, Chronic Low Back Pain IPD Meta-Analysis Group. 2020. Exercise treatment effect modifiers in persistent low back pain: an individual participant data meta-analysis of 3514 participants from 27 randomised controlled trials. Br J Sports Med, 1277-1278, vol. 54(21). link> doi> full text>
• Hayden JA, Wilson MN, Riley RD, Iles R, Pincus T, Ogilvie R. 2019. Individual recovery expectations and prognosis of outcomes in non-specific low back pain: prognostic factor review. Cochrane Database Syst Rev, vol. 2019(11). link> doi> full text>
• Phillips B, Morgan JE, Haeusler GM, Riley RD, PICNICC Collaborative. 2020. Individual participant data validation of the PICNICC prediction model for febrile neutropenia. Arch Dis Child, 439-445, vol. 105(5). link> doi> full text>
• Riley RD. 2019. CORRECTION: Minimum sample size for developing a multivariable prediction model: Part II-binary and time-to-event outcomes by Riley RD, Snell KI, Ensor J, et al. (vol 38, pg 1276, 2019). STATISTICS IN MEDICINE. link> doi> full text>
• Hughes T, Riley R, Sergeant JC, Callaghan MJ. 2019. A study protocol for the development and internal validation of a multivariable prognostic model to determine lower extremity muscle injury risk in elite football (soccer) players, with further exploration of prognostic factors. Diagnostic and Prognostic Research, 19, vol. 3. link> doi> link> full text>
• 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. Diagnostic and Prognostic Research, 15, vol. 3. link> doi> link> 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, l4293, vol. 366. link> doi> full text>
• Debray TPA, de Jong VMT, Moons KGM, Riley RD. 2019. Evidence synthesis in prognosis research. Diagn Progn Res, 13, vol. 3. 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, 730-739, vol. 74(8). link> doi> link> 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, 9, vol. 3. 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, 440-451, vol. 10(3). 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, l737, vol. 365. link> doi> full text>
• 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, k4597, vol. 364. 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, W1-W33, vol. 170(1). 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, 51-58, vol. 170(1). 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, 91-99, vol. 78(1). 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, 1276-1296, vol. 38(7). 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, 1262-1275, vol. 38(7). link> doi> full text>
• 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, 825-836, vol. 3(12). link> doi> full text>
• Chadi SA, Malcomson L, Ensor J. 2019. 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>
• 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, ED000129, vol. 10. link> doi> 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, e0203325, vol. 13(9). 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, CD012841, vol. 9. 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. link> doi> link> full text>
• 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, 4404-4420, vol. 37(29). link> doi> full text>
• Debray TP, Damen JA, Riley RD, Snell K, Reitsma JB, Hooft L, Collins GS, Moons KG. 2019. A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes. Stat Methods Med Res, 2768-2786, vol. 28(9). 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, 712-722, vol. 122(5). 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, 38-49, vol. 102. 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, 41, vol. 18(1). 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, 2443-2448, vol. 48(11). 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, e020795, vol. 8(4). 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, e020796, vol. 8(4). 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, 2034-2052, vol. 37(12). 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, 1177-1205, vol. 67(5). 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, e019584, vol. 8(1). 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, e018971, vol. 7(12). 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, 73-88, vol. 9(1). 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, 41-50, vol. 9(1). 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, 163-178, vol. 9(2). link> doi> full text>
• 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, 100-115, vol. 9(1). link> doi> full text>
• 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, 16, vol. 1. link> doi> full text>
• 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, 3283-3301, vol. 36(21). 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, j3932, vol. 358. 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, 548-556, vol. 74(2). link> doi> full text>
• 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, 1-158, vol. 21(41). 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, 109, vol. 17(1). link> doi> full text>
• Rogozinska E, Marlin N, Betran AP, Astrup A, Bogaerts A, Cecatti JG, Devlieger R, Dodd JM, El Beltagy N, Facchinetti F, Geiker NRW, Guelfi KJ, Haakstad LAH, Harrison CL, Hauner H, Jensen DM, Kinnunen TI, Khoury J, Luoto R, McAuliffe F, Motahari N, Morkved S, Owens J, Perales M, Petrella E, Phelan S, Poston L, Rauh K, Renault KM, Sagedal LR, Salvesen KA, Shen GX, Shub A, Scudeller T, Surita FG, 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, Martin AA, Rayanagoudar G, Ruifrok AE, Roberts T, de Groot CJM, Coomarasamy A, Mol BWJ, Zamora J, Khan KS, Riley RD, Thangaratinam S, Pregnancy IWM. 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 The International Weight Management in Pregnancy (i-WIP) Collaborative Group. BMJ-BRITISH MEDICAL JOURNAL, Article ARTN j3119, vol. 358. link> doi> full text>
• 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 Technology Assessment, 1-100, vol. 21(18). link> doi> link> full text>
• 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, 68, vol. 15(1). 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>
• 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, 6, vol. 1. link> doi> 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, 1, vol. 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, 2885-2905, vol. 27(10). 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, i6460, vol. 356. link> doi> full text>
• 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, i6253, vol. 355. 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, 772-789, vol. 36(5). 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, 855-875, vol. 36(5). 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, 301-317, vol. 36(2). 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, MR000007, vol. 9. 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, 720-730, vol. 4(9). 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, 23, vol. 46(1). link> doi> full text>
• Phillips RS, Sung L, Ammann RA, Riley RD, Castagnola E, Haeusler GM, Klaassen R, Tissing WJ, Lehrnbecher T, Chisholm J, Hakim H, Ranasinghe N, Paesmans M, Hann IM, Stewart LA. 2016. Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis. Br J Cancer, e17, vol. 114(12). 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, i3140, vol. 353. 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, e011190, vol. 6(5). 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, 90-100, vol. 78. 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, 683-694, vol. 62(6). 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, 623-630, vol. 114(6). 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, 305-326, vol. 11. 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, i-190, vol. 20(12). 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, 1060-1064, vol. 123(7). 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, S409, vol. 214(1). 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, 1-7, vol. 92. 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, 2853-2868, vol. 26(6). 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, 1063-1089, vol. 35(7). link> doi> full text>
• Takwoingi Y, Riley RD, Deeks JJ. 2015. Meta-analysis of diagnostic accuracy studies in mental health. Evid Based Ment Health, 103-109, vol. 18(4). 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, e1001886, vol. 12(10). 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, 101, vol. 4. 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, 2481-2496, vol. 34(17). 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, e1001855, vol. 12(7). 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, 1896-1911, vol. 26(4). 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, 2081-2103, vol. 34(13). 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, 157-174, vol. 6(2). 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, 40-50, vol. 69. 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, 542-550, vol. 68(5). 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, 1-516, vol. 19(36). 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, 1-366, vol. 19(37). 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, 853-867, vol. 10. 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, 1657-1665, vol. 313(16). 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, 634-642, vol. 122(5). 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, e000060, vol. 2(1). 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, 12, vol. 4. 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, 84-97, vol. 16(1). 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, 20, vol. 13. 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, 140, vol. 3. 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, 322-351, vol. 5(4). 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, 131, vol. 3. link> doi>
• Chen Y, Hong C, Riley RD. 2015. An alternative pseudolikelihood method for multivariate random-effects meta-analysis. Stat Med, 361-380, vol. 34(3). 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, 30-41, vol. 28(1). 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, 157, vol. 14. 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, 346, vol. 15. link> doi>
• Hayden JA, Tougas ME, Riley R, Iles R, Pincus T. Individual recovery expectations and prognosis of outcomes in non-specific low back pain: prognostic factor exemplar review. Cochrane Database of Systematic Reviews. 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, e1001671, vol. 11(7). link> doi> full text>
• Rannard A, Gabbay M, Sen D, Riley R, Britt D. 2014. Feasibility trial of GP and case-managed support for workplace sickness absence. Primary Health Care Research and Development, 252-261, vol. 15(3). link> doi> link> 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, e1001664, vol. 11(6). link> doi> full text>
• 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, e97886, vol. 9(5). link> doi>
• 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, 686-699, vol. 121(6). link> doi>
• Crowther MJ, Look MP, Riley RD. 2014. Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis. Stat Med, 3844-3858, vol. 33(22). 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, e004188, vol. 4(1). 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, 3, vol. 14. link> doi>
• Malin GL, Morris RK, Riley R, Teune MJ, Khan KS. 2014. When is birthweight at term abnormally low? A systematic review and meta-analysis of the association and predictive ability of current birthweight standards for neonatal outcomes. BJOG, 515-526, vol. 121(5). 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, 91, vol. 2. link> doi>
• Jackson D and Riley RD. 2014. A refined method for multivariate meta-analysis and meta-regression. Stat Med, 541-554, vol. 33(4). link> doi>
• Abo-Zaid G, Guo B, Deeks JJ, Debray TPA, Steyerberg EW, Moons KGM, Riley RD. 2013. Individual participant data meta-analyses should not ignore clustering. J Clin Epidemiol, 865-873.e4, vol. 66(8). 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, 2747-2766, vol. 32(16). link> doi>
• Debray TPA, Moons KGM, Abo-Zaid GMA, Koffijberg H, Riley RD. 2013. Individual participant data meta-analysis for a binary outcome: one-stage or two-stage?. PLoS One, e60650, vol. 8(4). link> doi>
• Jackson D, White IR, Riley RD. 2013. A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression. Biom J, 231-245, vol. 55(2). link> doi>
• Lin A, Yung AR, Nelson B, Brewer WJ, Riley R, Simmons M, Pantelis C, Wood SJ. 2013. Neurocognitive predictors of transition to psychosis: medium- to long-term findings from a sample at ultra-high risk for psychosis. Psychol Med, 2349-2360, vol. 43(11). 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, e1001381, vol. 10(2). link> doi> full text>
• 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, e5595, vol. 346. 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, e5793, vol. 346. 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, e1001380, vol. 10(2). link> doi>
• 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, 16, vol. 2. link> doi>
• Schuit E, Groenwold RHH, Harrell FE, de Kort WLAM, Kwee A, Mol BWJ, Riley RD, Moons KGM. 2013. Unexpected predictor-outcome associations in clinical prediction research: causes and solutions. CMAJ, E499-E505, vol. 185(10). link> doi>
• Debray TPA, Moons KGM, Ahmed I, Koffijberg H, Riley RD. 2013. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med, 3158-3180, vol. 32(18). link> doi>
• Hayden JA, Cartwright JL, Riley RD, Vantulder MW, Chronic Low Back Pain IPD Meta-Analysis Group. 2012. Exercise therapy for chronic low back pain: protocol for an individual participant data meta-analysis. Syst Rev, 64, vol. 1. link> doi>
• 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 EDITORIAL COMMENT. OBSTETRICAL & GYNECOLOGICAL SURVEY, 681-682, vol. 67(11). link> doi>
• McMinn DJW, Daniel J, Treacy RBC, Riley RD. 2012. Authors' reply to Jameson and Breusch. BMJ, e7239, vol. 345. link> doi>
• McMinn DJW, Daniel J, Treacy RBC, Riley RD. 2012. Authors' reply to National Joint Registry. BMJ, e7246, vol. 345. 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, e4342, vol. 345. link> doi>
• Jackson D, White IR, Riley RD. 2012. Quantifying the impact of between-study heterogeneity in multivariate meta-analyses. Stat Med, 3805-3820, vol. 31(29). 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, e3319, vol. 344. link> doi>
• Kirkham JJ, Riley RD, Williamson PR. 2012. A multivariate meta-analysis approach for reducing the impact of outcome reporting bias in systematic reviews. Stat Med, 2179-2195, vol. 31(20). link> doi>
• Abo-Zaid G, Sauerbrei W, Riley RD. 2012. Individual participant data meta-analysis of prognostic factor studies: state of the art?. BMC Med Res Methodol, 56, vol. 12. link> doi>
• Crowther MJ, Riley RD, Staessen JA, Wang J, Gueyffier F, Lambert PC. 2012. Individual patient data meta-analysis of survival data using Poisson regression models. BMC Med Res Methodol, 34, vol. 12. link> doi>
• Phillips RS, Sutton AJ, Riley RD, Chisholm JC, Picton SV, Stewart LA, PICNICC Collaboration. 2012. Predicting infectious complications in neutropenic children and young people with cancer (IPD protocol). Syst Rev, 8, vol. 1. 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, d7762, vol. 344. link> doi>
• Phillips B, Wade R, Westwood M, Riley R, Sutton AJ. 2012. Systematic review and meta-analysis of the value of clinical features to exclude radiographic pneumonia in febrile neutropenic episodes in children and young people. J Paediatr Child Health, 641-648, vol. 48(8). link> doi>
• Meads C, Ahmed I, Riley RD. 2012. A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance. Breast Cancer Res Treat, 365-377, vol. 132(2). link> doi>
• Hubner RA, Riley RD, Billingham LJ, Popat S. 2011. Excision repair cross-complementation group 1 (ERCC1) status and lung cancer outcomes: a meta-analysis of published studies and recommendations. PLoS One, e25164, vol. 6(10). link> doi>
• Jackson D, White IR, Riley RD. 2011. Rejoinder to commentaries on 'Multivariate meta-analysis: Potential and promise'. STATISTICS IN MEDICINE, 2509-2510, vol. 30(20). 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, 608-618, vol. 64(6). link> doi>
• Riley RD, Higgins JPT, Deeks JJ. 2011. Interpretation of random effects meta-analyses. BMJ, d549, vol. 342. link> doi>
• Jackson D, Riley R, White IR. 2011. Multivariate meta-analysis: potential and promise. Stat Med, 2481-2498, vol. 30(20). link> doi>
• Riley RD. 2011. An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes (vol 26, pg 78, 2007). STATISTICS IN MEDICINE, 400, vol. 30(4). link> doi>
• Riley RD. 2010. Commentary: like it and lump it? Meta-analysis using individual participant data. Int J Epidemiol, 1359-1361, vol. 39(5). link> doi>
• Thomas MJ, Simpson J, Riley R, Grant E. 2010. The impact of home-based physiotherapy interventions on breathlessness during activities of daily living in severe COPD: a systematic review. Physiotherapy, 108-119, vol. 96(2). link> doi>
• Tandon S, Tudur-Smith C, Riley RD, Boyd MT, Jones TM. 2010. A systematic review of p53 as a prognostic factor of survival in squamous cell carcinoma of the four main anatomical subsites of the head and neck. Cancer Epidemiol Biomarkers Prev, 574-587, vol. 19(2). link> doi>
• Riley RD, Lambert PC, Abo-Zaid G. 2010. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ, c221, vol. 340. link> doi>
• Riley RD and Steyerberg EW. 2010. Meta-analysis of a binary outcome using individual participant data and aggregate data. Res Synth Methods, 2-19, vol. 1(1). link> doi>
• Hemingway H, Riley RD, Altman DG. 2009. Ten steps towards improving prognosis research. BMJ, b4184, vol. 339. link> doi>
• Riley RD. 2009. Multivariate meta-analysis: the effect of ignoring within-study correlation. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 789-811, vol. 172. 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, 223-228, vol. 26(3). link> doi>
• Whitehead A. 2009. Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials (vol 6, pg 16, 2009). CLINICAL TRIALS, 288, vol. 6(3). 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, 1219-1229, vol. 100(8). link> doi>
• Jones AP, Riley RD, Williamson PR, Whitehead A. 2009. Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials. Clin Trials, 16-27, vol. 6(1). link> doi>
• Riley RD, Dodd SR, Craig JV, Thompson JR, Williamson PR. 2008. Meta-analysis of diagnostic test studies using individual patient data and aggregate data. Stat Med, 6111-6136, vol. 27(29). link> doi>
• Riley RD, Lambert PC, Staessen JA, Wang J, Gueyffier F, Thijs L, Boutitie F. 2008. Meta-analysis of continuous outcomes combining individual patient data and aggregate data. Stat Med, 1870-1893, vol. 27(11). link> doi>
• Riley RD, Ridley G, Williams K, Altman DG, Hayden J, de Vet HCW. 2007. Prognosis research: toward evidence-based results and a Cochrane methods group. J Clin Epidemiol, 863-865, vol. 60(8). link> doi>
• Riley RD, Thompson JR, Abrams KR. 2008. An alternative model for bivariate random-effects meta-analysis when the within-study correlations are unknown. Biostatistics, 172-186, vol. 9(1). link> doi>
• Riley RD, Simmonds MC, Look MP. 2007. Evidence synthesis combining individual patient data and aggregate data: a systematic review identified current practice and possible methods. J Clin Epidemiol, 431-439, vol. 60(5). link> doi>
• Riley RD, Abrams KR, Sutton AJ, Lambert PC, Thompson JR. 2007. Bivariate random-effects meta-analysis and the estimation of between-study correlation. BMC Med Res Methodol, 3, vol. 7. link> doi>
• Sauerbrei W, Hollaender N, Riley RD, Altman DG. 2006. Evidence-based assessment and application of prognostic markers: The long way from single studies to meta-analysis. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 1333-1342, vol. 35(7). link> doi>
• Dixon-Woods M, Cavers D, Agarwal S, Annandale E, Arthur A, Harvey J, Hsu R, Katbamna S, Olsen R, Smith L, Riley R, Sutton AJ. 2006. Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Med Res Methodol, 35, vol. 6. link> doi>
• Riley RD, Abrams KR, Lambert PC, Sutton AJ, Thompson JR. 2007. An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes. Stat Med, 78-97, vol. 26(1). link> doi>
• Altman DG and Riley RD. 2005. Primer: an evidence-based approach to prognostic markers. Nat Clin Pract Oncol, 466-472, vol. 2(9). link> doi>
• Thompson JR, Minelli C, Abrams KR, Tobin MD, Riley RD. 2005. Meta-analysis of genetic studies using Mendelian randomization--a multivariate approach. Stat Med, 2241-2254, vol. 24(14). link> doi>
• Riley RD, Sutton AJ, Abrams KR, Lambert PC. 2004. Sensitivity analyses allowed more appropriate and reliable meta-analysis conclusions for multiple outcomes when missing data was present. J Clin Epidemiol, 911-924, vol. 57(9). link> doi>
• Riley RD, Heney D, Jones DR, Sutton AJ, Lambert PC, Abrams KR, Young B, Wailoo AJ, Burchill SA. 2004. A systematic review of molecular and biological tumor markers in neuroblastoma. Clin Cancer Res, 4-12, vol. 10(1 Pt 1). link> doi>
• Riley RD, Abrams KR, Sutton AJ, Lambert PC, Jones DR, Heney D, Burchill SA. 2003. Reporting of prognostic markers: current problems and development of guidelines for evidence-based practice in the future. Br J Cancer, 1191-1198, vol. 88(8). link> doi>
• Riley RD, Burchill SA, Abrams KR, Heney D, Sutton AJ, Jones DR, Lambert PC, Young B, Wailoo AJ, Lewis IJ. 2003. A systematic review of molecular and biological markers in tumours of the Ewing's sarcoma family. Eur J Cancer, 19-30, vol. 39(1). link> doi>
• Burchill SA, Lewis IJ, Abrams KR, Riley R, Imeson J, Pearson AD, Pinkerton R, Selby P. 2001. Circulating neuroblastoma cells detected by reverse transcriptase polymerase chain reaction for tyrosine hydroxylase mRNA are an independent poor prognostic indicator in stage 4 neuroblastoma in children over 1 year. J Clin Oncol, 1795-1801, vol. 19(6). link> doi>
• Hughes T, Riley RD, Callaghan MJ, Sergeant JC. 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. doi> full text>

Other

• Holden MA, Hattle M, Runhaar J, Riley R, Healey E, Quicke J, van Der Windt D, Dziedzic K, van Middelkoop M, Burke D, Brown J, Ingram C, Taylor R, Walker C, Corp N, Legha A, Bierma-Zeinstra S, Foster NE. 2022. MODERATORS OF THE EFFECT OF THERAPEUTIC EXERCISE FOR PEOPLE WITH KNEE AND/OR HIP OSTEOARTHRITIS: AN INDIVIDUAL PARTICIPANT DATA META-ANALYSIS. OSTEOARTHRITIS AND CARTILAGE (pp. S402-S403, vol. 30). link> doi> full text>
• Wu F, Lamberg-Allardt C, Cai G, Viljakainen H, El-Hajj G, Rahme FM, Gronborg I, Andersen R, Khadilkar A, Zulf M, Molgaard C, Larnkjaer A, Zhu K, Riley R, Winzenberg T. 2022. Vitamin D supplementation for improving bone density in vitamin D-deficient children: a systematic review and individual participant data meta-analysis of randomized controlled trials. JOURNAL OF BONE AND MINERAL RESEARCH (p. 65, vol. 37). link> doi> full text>
• Navarro CLA, Damen J, Takada T, Dhiman P, Collins GS, Ma J, Bajpai R, Riley RD, Hooft L, Moons KG. 2021. Why are Machine Learning-based prediction models still unpopular in clinical practice?. Diagnostic and Prognostic Research. doi> link> full text>
• Jordan R, Fitzmaurice D, Martin J, Ayres J, Cheng K, Cooper B, Daley A, Dickens A, Enocson A, Greenfield S, Miller M, Riley R, Siebert S, Stockley R, Turner A, Adab P. 2019. Development of the Birmingham Lung Improvement Studies (BLISS) prognostic score for COPD patients in primary care: data from the Birmingham COPD cohort. European Respiratory Journal (vol. 54). European Respiratory Society. link> doi> link>
• Adab P, Fitzmaurice D, Martin J, Sitch A, Cheng K, Daley A, Dickens A, Enocson A, Greenfield S, Jolly K, Jowett S, Miller M, Riley R, Siebert S, Stockley R, Turner A, Haroon S, Jordan R. 2019. Effect of screening for undiagnosed COPD on respiratory hospitalisation and mortality; 4 year follow up of the TargetCOPD trial. EUROPEAN RESPIRATORY JOURNAL (vol. 54). link> doi>
• 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 (p. S409, vol. 214). 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>
• Lordkipanidze M, Dretzke J, Riley R, Jowett S, O'Donnell J, Ensor J, Moloney E, Price M, Raichand S, Hodgkinson J, Bayliss S, Moore D, Fitzmaurice D. 2015. 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>
• Jordan RE, Adab P, Sitch A, Enocson A, Blissett D, Jowett S, Marsh J, Riley R, Miller M, Cooper B, Turner A, Ayres J, Cheng K, Jolly K, Stockley RA, 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. AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE (vol. 191). link>
• Jordan RE, Majothi S, Heneghan NR, Turner A, Moore D, O'Brien D, Jowett S, Singh S, Adab P, Fitzmaurice D, Bayliss S, Riley R, Price M, Ayres J, Jolly CB. 2013. SUPPORTED SELF-MANAGEMENT FOR PATIENTS WITH MODERATE TO SEVERE COPD AT OR SHORTLY AFTER DISCHARGE FROM HOSPITAL: A SYSTEMATIC REVIEW OF THE EVIDENCE. THORAX (pp. A95-A96, vol. 68). link> doi>
• Sheppard JP, Hodgkinson J, Riley R, Martin U, McManus RJ. 2013. Is it safe to wake up? Defining the prognostic value of the morning blood pressure surge in clinical practice. JOURNAL OF HUMAN HYPERTENSION (p. 647, vol. 27). link>
• Corbett C, Armstrong MJ, Bayliss SE, Moore D, Riley R, Newsome PN. 2013. Use of Liver Fibrosis biomarkers to predict clinical outcomes- A Systematic Review and Meta-analysis. HEPATOLOGY (pp. 1196A-1197A, vol. 58). link>
• Haroon S, Jordan R, Riley R, Lancashire R, Marshall T, Adab P. 2013. Detecting undiagnosed COPD: Using routine primary care data to select and validate candidate variables for COPD risk prediction. EUROPEAN RESPIRATORY JOURNAL (vol. 42). link>
• Ginart A, Riley R, Hardman B, Ernst M, IEEE. 2013. Closed-Forms Solution for Simplified PV Modeling and Voltage Evaluation Including Irradiation and Temperature Dependence. 2013 IEEE GREEN TECHNOLOGIES CONFERENCE (pp. 105-112). link> doi>
• Popat S, Riley RD, Billingham LJ, Hubner RA. 2011. EXCISION REPAIR CROSS-COMPLEMENTATION GROUP 1 (ERCC1) STATUS AND NON-SMALL CELL LUNG CANCER (NSCLC) OUTCOMES: A META-ANALYSIS OF PUBLISHED STUDIES AND RECOMMENDATIONS. JOURNAL OF THORACIC ONCOLOGY (pp. S438-S439, vol. 6). link>
• Billingham L and Riley RD. 2009. How to design a meta-analysis in biomarkers. JOURNAL OF THORACIC ONCOLOGY (pp. S78-S79, vol. 4). link>
• Riley RD, Abrams KR, Lambert PC, Sutton AJ, Altman DG. 2007. Where next for evidence synthesis of prognostic marker studies? Improving the quality and reporting of primary studies to facilitate clinically relevant evidence-based results. ADVANCES IN STATISTICAL METHODS FOR THE HEALTH SCIENCES: APPLICATIONS TO CANCER AND AIDS STUDIES, GENOME SEQUENCE ANALYSIS, AND SURVIVAL ANALYSIS (pp. 39-+). link> doi>