References & articles

  • Below is a reference list of key articles that promote good practice and methods for IPD meta-analysis projects.

  • The list will never be exhaustive, but please let us know of any omissions that should be added.

  • To help, we have grouped by the following topic areas:

Introductory texts & overviews    Power    Data repositories    Statistical methods    One-stage versus two-stage    Bias & reporting   

Treatment-covariate interactions    Combining IPD & aggregate data    Multivariate meta-analysis    Network meta-analysis    Diagnosis, prognosis & prediction     Missing data        

Textbook

  • Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chichester; 2021 

Introductory articles and overviews of IPD meta-analysis projects

  • Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: conduct, rationale and reporting. BMJ 2010; 340: c221 

  • Stewart LA, Parmar MK. Meta-analysis of the literature or of individual patient data: is there a difference? Lancet 1993;341(8842):418-22.

  • Oxman AD, Clarke MJ, Stewart LA. From science to practice. Meta-analyses using individual patient data are needed. JAMA 1995; 274 (10):845-6.

  • Clarke M, Stewart L, Pignon JP, et al. Individual patient data meta-analysis in cancer. Br J Cancer 1998;77(11):2036-44.

  • Clarke M, Godwin J. Systematic reviews using individual patient data: a map for the minefields? Annals of oncology : 1998;9(8):827-33.

  • Stewart LA, Clarke MJ, on behalf of the Cochrane Working Party Group on Meta-analysis using Individual Patient Data. Practical methodology of meta-analyses (overviews) using updated individual patient data. Statistics in Medicine 1995; 14: 2057-2079.

  • Vale CL, Rydzewska LHM, Rovers MM, Emberson JR, Gueyffier F, Stewart LA. Uptake of systematic reviews and meta-analyses based on individual participant data in clinical practice guidelines: descriptive study. BMJ 2015; 350:h1088.

  • Stewart LA, Tierney JF. To IPD or Not to IPD? Advantages and disadvantages of systematic reviews using individual patient data. Evaluation & the Health Professions 2002; 25: 76-97.

  • Simmonds MC, Higgins JPT, Stewart LA, Tierney JF, Clarke MJ, et al. Meta-analysis of individual patient data from randomised trials -a review of methods used in practice. Clinical Trials 2005; 2: 209-217.

  • Debray TPA, et al. Get real in individual participant data (IPD) meta-analysis: a review of the methodology. Research Synthesis Methods 2015; 6(4):293-309.

  • Simmonds M, Stewart G, Stewart L. A decade of individual participant data meta-analyses: a review of current practice. Contemporary Clinical Trial 2015; 45:76-83.

  • Thomas D, Radji S, Benedetti A. Systematic review of methods for individual patient data meta-analysis with binary outcomes. BMC Med Res Method 2014; 14:79.

  • Tierney JF, et al. Individual participant data (IPD) meta-analyses of randomised controlled trials: guidance on their use. PLOS Med 2015; 12(7):e1001855.

  • Tudur Smith C, Williamson PR, Marson AG. An overview of methods and empirical comparison of aggregate data and individual patient data results for investigating heterogeneity in meta-analysis of time-to-event outcomes. J Eval Clin Pract 2004; 11(5):468-478.

  • Tierney JF, Stewart LA, Clarke M. Individual Participant Data. In: Higgins JPT, Chandler TJ, Cumpston M, et al., eds. Cochrane Handbook for Systematic Reviews of Interventions. London: Cochrane 2019

  • Tudur Smith C, et al. Individual participant data meta-analyses compared with meta-analyses based on aggregate data. Cochrane Database of Systematic Reviews 2016; 9 MR000007. 

  • Tierney JF, Fisher DJ, Burdett S, et al. Comparison of aggregate and individual participant data approaches to meta-analysis of randomised trials: An observational study. PLoS Med 2020;17(1):e1003019

  • Nevitt SJ, Marson AG, Davie B, et al. Exploring changes over time and characteristics associated with data retrieval across individual participant data meta-analyses: systematic review. BMJ 2017;357:j1390

  • Seidler AL, Hunter KE, Cheyne S, et al. A guide to prospective meta-analysis. BMJ 2019;367:l5342.

  • Wang et al. The methodological quality of individual participant data meta-analysis on intervention effects: systematic review BMJ 2021;373:n736

Data cleaning, checking and harmonisation

  • Chapters 2 to 4 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Dewidar et al. PRIME-IPD SERIES Part 1. The PRIME-IPD tool promoted verification and standardization of study datasets retrieved for IPD meta-analysis. J Clin Epi 2021; 136: 227-234

  • Levis B, Riley RD. PRIME-IPD SERIES Part 2. Retrieving, checking, and harmonizing data are underappreciated challenges in individual participant data meta-analyses. J Clin Epi 2021; 136:221-223.

Power calculations

  • Chapter 12 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Simmonds MC and Higgins JP. Covariate heterogeneity in meta-analysis: criteria for deciding between meta-regression and individual patient data. Stat Med. 2007; 26: 2982-99.

  • Kovalchik SA. Aggregate-data estimation of an individual patient data linear random effects meta-analysis with a patient covariate-treatment interaction term. Biostatistics. 2013; 14: 273-83.

  • Kovalchik SA and Cumberland WG. Using aggregate data to estimate the standard error of a treatment-covariate interaction in an individual patient data meta-analysis. Biom J. 2012; 54: 370-84.

  • Kontopantelis E, Springate DA, Parisi R and Reeves D. Simulation-Based Power Calculations for Mixed Effects Modeling: ipdpower in Stata. 2016. 2016; 74: 25.

  • Ensor J, Burke DL, Snell KIE, Hemming K, Riley RD. Simulation-based power calculations for planning a two-stage individual participant data meta-analysis: with application to randomised trials with a continuous outcome. BMC Medical Research Methodology. 2018. 18:41.

  • Burke DL, Billingham LJ, Girling AJ, et al. Meta-analysis of randomized phase II trials to inform subsequent phase III decisions. Trials 2014;15:346.

Data repository examples

  • Krumholz HM, Waldstreicher J. The Yale Open Data Access (YODA) Project--A Mechanism for Data Sharing. N Engl J Med 2016;375(5):403-5. 

  • Kalter J, Sweegers MG, Verdonck-de Leeuw IM, et al. Development and use of a flexible data harmonization platform to facilitate the harmonization of individual patient data for meta-analyses. BMC Research Notes 2019;12(1):164. 

  • Ross JS, Waldstreicher J, Bamford S, et al. Overview and experience of the YODA Project with clinical trial data sharing after 5 years. Sci Data 2018;5:180268. 

  • Hee SW, Dritsaki M, Willis A, et al. Development of a repository of individual participant data from randomized controlled trials of therapists delivered interventions for low back pain. Eur J Pain 2017;21(5):815-26. 

  • van Middelkoop M, Arden NK, Atchia I, et al. The OA Trial Bank: meta-analysis of individual patient data from knee and hip osteoarthritis trials show that patients with severe pain exhibit greater benefit from intra-articular glucocorticoids. Osteoarthritis Cartilage 2016;24(7):1143-52. 


Statistical methods for IPD meta-analysis

  • Chapters 5 to 8, and 12 to 18 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Fisher DJ. Two-stage individual participant data meta-analysis and generalized forest plots. Stata Journal 2015;15(2):369-96. 

  • Higgins JP, Whitehead A, Turner RM, Omar RZ, Thompson SG: Meta-analysis of continuous outcome data from individual patients.  Stat Med 2001; 20: 2219-4.

  • Turner RM, Omar RZ, Yang M, Goldstein H, Thompson SG: A multilevel model framework for meta-analysis of clinical trials with binary outcomes. Stat Med 2000, 19: 3417-3432.

  • Tudur-Smith C, Williamson PR, Marson AG: Investigating heterogeneity in an individual patient data meta-analysis of time to event outcomes. Stat Med 2005, 24: 1307-1319.

  • Riley RD, Kauser I, Bland M, Wang J, Gueyffier F, Thijs L, Deeks JJ. Meta-analysis of continuous outcomes according to baseline imbalance and availability of individual participant data. Stat Med 2013; 32(16):2747-66. doi: 10.1002/sim.5726

  • Crowther MJ, Riley RD, Staessen JA, Wang J, Gueyffier F, Lambert PC. Individual patient data meta-analysis of survival data using Poisson regression models. BMC MedResMeth 2012; 12:34.

  • Abo-Zaid G, et al. Individual participant data meta-analyses should not ignore clustering. Journal of Clinical Epidemiology 2013; 66:865-873.

  • Riley RD, Kauser I, Bland M, Thijs L, Staessen JA, Wang J, Gueyffier F, Deeks JJ. Meta-analysis of randomised trials with a continuous outcome according to baseline imbalance and availability of individual patient data. Statistics in Medicine 2013; 32:2747-2766.

  • Thompson S, et al. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies. Int J Epidemiol 2010; 39:1345-1359.

  • Jones AP, Riley RD, Williamson PR, Whitehead A. Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials. Clinical Trials 2009; 6:16-27.

  • Higgins JPT, Thompson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. J.R.Statist.Soc.A 2009; 172(1):137-159.

  • Riley RD, Higgins JPT, Deeks JJ. Interpretation of random-effects meta-analyses. BMJ 2011; 342:d549.

  • Simmonds MC. Statistical Methodology for Individual Patient Data Meta-Analysis. PhD Thesis, University of Cambridge 2006

  • Papadimitropoulou K, Stijnen T, Dekkers OM, et al. One-stage random effects meta-analysis using linear mixed models for aggregate continuous outcome data. Res Synth Methods 2019;10(3):360-75.

  • Papadimitropoulou K et al. Meta‐analysis of continuous outcomes: Using pseudo IPD created from aggregate data to adjust for baseline imbalance and assess treatment‐by‐baseline modification

  • Whitehead A, Omar RZ, Higgins JP, et al. Meta-analysis of ordinal outcomes using individual patient data. Stat Med 2001;20(15):2243-60

  • Legha A, Riley RD, Ensor J, et al. Individual participant data meta-analysis of continuous outcomes: A comparison of approaches for specifying and estimating one-stage models. Stat Med 2018;37(29):4404-20

  • Thomas D, Radji S, Benedetti A. Systematic review of methods for individual patient data meta-analysis with binary outcomes. BMC Med Res Methodol 2014;14 

  • Thomas D, Platt R, Benedetti A. A comparison of analytic approaches for individual patient data meta-analyses with binary outcomes. BMC Med Res Methodol 2017;17(1):28.

  • Riley RD, Legha A, Jackson D, et al. One-stage individual participant data meta-analysis models for continuous and binary outcomes: comparison of treatment coding options and estimation methods. Stat Med 2020;39(19):2536-55.

  • Crowther MJ, Look MP, Riley RD. Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis. Stat Med 2014;33(22):3844-58


Meta-analysis combining IPD and aggregate data

  • Chapters 5 to 7 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Riley RD, Steyerberg EW. Meta-analysis of a binary outcome using individual participant data and aggregate data. Research Synthesis Methods 2010; 1 (1): 2-19.

  • Riley RD, et al. Evidence synthesis combining individual patient data and aggregate data: a systematic review identified current practice and possible methods. J Clin Epidemiol 2007, 60:431-439.

  • Riley RD, et al. Meta-analysis of continuous outcomes combining individual patient data and aggregate data. Stat Med 2008; 27: 1870-93.

  • Yamaguchi Y, Sakamoto W, Goto M, et al. Meta-analysis of a continuous outcome combining individual patient data and aggregate data: a method based on simulated individual patient data. Res Synth Methods 2014;5(4):322-51

  • Sutton AJ, Kendrick D, Coupland CA. Meta-analysis of individual- and aggregate-level data. Stat Med 2008;27:651-69.

One-stage versus two-stage approaches to IPD meta-analysis

  • Chapter 8 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Burke DL, Ensor J, Riley RD. Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ. Stat Med, 2017; 36(5):855-875 doi: 10.1002/sim.7141.

  • Morris TP, Fisher DJ, Kenward MG, Carpenter JR. Meta-analysis of Gaussian individual patient data: Two-stage or not two-stage? Stat Med, 2018; 37(9):1419-1438. 

  • Olkin I, Sampson A. Comparison of meta-analysis versus analysis of variance of individual patient data. Biometrics 1998; 54 (1):317-322.

  • Mathew T, Nordstrom K. On the equivalence of meta-analysis using literature and using individual patient data. Biometrics 1999; 55(4):1221-1223.

  • Mathew T, Nordstrom K. Comparison of one-step and two-step meta-analysis models using individual patient data. Biometrical Journal 2010; 52(2):271-287.

  • Debray TPA, Moons KGM, Abo-Zaid GMA, Koffijberg H, Riley RD. Individual Participant Data Meta-Analysis for a Binary Outcome: One-Stage or Two-Stage? PLoS ONE 2013; 8(4): e60650.

  • Hamza TH, van Houwelingen HC, Stijnen T. The binomial distribution of meta-analysis was preferred to model within-study variability. Journal of Clinical Epidemiology 2008; 61:41-51.

  • Tudur-Smith C, Williamson PR. A comparison of methods for fixed-effect meta-analyses of individual patient data with time-to-event outcomes. Clinical Trials 2007; 4:621-630.

  • Bowden J, Tierney JF, Simmonds M, Copas AJ, Higgins JPT. Individual patient data meta-analysis of time-to-event outcomes: one-stage versus two-stage approaches for estimating the hazard ratio under a random-effects model. Research Synthesis Methods 2011; 2:150-162.

  • Stewart, GB, et al. Statistical analysis of individual participant data meta-analyses: a comparison of methods and recommendations for practice. PLoS One 2012; 7(10): p. e46042.

  • Jackson D, Law M, Stijnen T, et al. A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio. Stat Med 2018;37(7):1059-85

Bias and Reporting of IPD meta-analyses

  • Chapters 9 and 10 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Stewart LA, Clarke M, Rovers M, Riley RD, Simmonds M, Stewart G, et al. Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD Statement. Jama. 2015; 313:1657-1665.

  • Ahmed I, Sutton AJ, Riley RD. Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey. BMJ 2012; 344: d7762.

  • Stewart L, Tierney J, Burdett S. Do Systematic Reviews Based on Individual Patient Data Offer a Means of Circumventing Biases Associated with Trial Publications? In: Rothstein HR, Sutton AJ, Borenstein M, editors. Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments. Chichester, UK.: John Wiley & Sons, Ltd., 2006.

  • Clarke MJ, Stewart LA. Obtaining data from randomised controlled trials: how much do we need for reliable and informative meta-analyses? In: Chalmers I, Altman DG, editors. Systematic reviews. London: BMJ Publishing, 1995:37-47.

  • Veroniki AA, Ashoor HM, Le SPC, et al. Retrieval of individual patient data depended on study characteristics: a randomized controlled trial. J Clin Epidemiol 2019;113:176-88.

  • Tsujimoto et al. No consistent evidence of data availability bias existed in recent individual participant data meta-analyses: a meta-epidemiological study. J Clin Epidemiol . 2020 Feb;118:107-114.e5.

​Treatment-covariate interactions

  • Chapter 7 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Riley RD, Debray TPA, Fisher D, et al. Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning. Stat Med 2020;39(15):2115-37

  • Fisher DJ, Carpenter JR, Morris TP, et al. Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach? BMJ 2017;356:j573.

  • Altman DG, Bland JM. Interaction revisited: the difference between two estimates. BMJ 2003; 326: 219.

  • Berlin JA, et al. Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head. Stat Med 2002; 21(3):371-387.

  • Lambert PC, et al. A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis. J Clin Epidemiol 2002; 55 (1):86-94.

  • Hua H, Burke DL, Crowther MJ, Ensor J, Tudur-Smith C, Riley RD. 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, 2017; 36(5):772-789.

  • Simmonds MC, Higgins JP. Covariate heterogeneity in meta-analysis: criteria for deciding between meta-regression and individual patient data. Stat Med 2007; 26: 2982-2999.

  • Fisher, DJ, et al. A critical review of methods for the assessment of patient-level interactions in individual participant data meta-analysis of randomized trials, and guidance for practitioners. J Clin Epidemiol 2011; 64(9): 949-967.

  • Schmid CH, Stark PC, Berlin JA, Landais P, Lau J. Meta-regression detected associations between heterogeneous treatment effects and study-level, but not patient-level, factors. J Clin Epidemiol 2004; 57:683-697.

  • Huang Y, et al. Comparing the overall result and interaction in aggregate data meta-analysis and individual patient data meta-analysis. Sys Rev Meta-Analysis 2016; 95 (14):1-7.


Multivariate meta-analysis using IPD

  • Chapter 13 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Riley RD, et al. Multivariate meta-analysis using individual participant data. Res Synth Method. 2015; 6 157-74.

  • Jackson D, Riley RD, White IR. Multivariate meta-analysis: potential and promise. Stat Med 2011; 30: 2481-2498.

  • Becker BJ. Multivariate Meta-analysis. In: Tinsley HEA, Brown S, editors. Handbook of Applied Multivariate Statistics and Mathematical Modelling San Diego: Academic Press; 2000. p. 499-525.

  • Berkey CS, et al. Meta-analysis of multiple outcomes by regression with random effects. Stat Med 1998; 17(22):2537-50.

  • Riley RD, et al. An evaluation of bivariate random-effects meta-analysis for the joint synthesis of two correlated outcomes. Statistics in Medicine 2007; 26:78-97.

  • Riley RD, et al. Multivariate meta-analysis: the effect of ignoring within-study correlation. JRSS-A 2009; 172: 789-811.

  • Van Houwelingen HC, et al. Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat Med 2002; 21(4):589-624.

  • White IR. Multivariate random-effects meta-analysis. The Stata Journal 2009; 9(1):40-56.


Network meta-analysis using IPD

  • Chapter 14 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Debray TPA, et al. An overview of methods for network meta-analysis using individual participant data: when do benefits arise? Stat Methods Med Res. 2016; pii: 0962280216660741. 

  • Saramago P, Sutton AJ, Cooper NJ, et al. Mixed treatment comparisons using aggregate and individual participant level data. Stat Med 2012;31(28):3516-36

  • Phillippo DM. Calibration of Treatment Effects in Network Meta-Analysis using Individual Patient Data. University of Bristol, 2019

  • Donegan S, Williamson P, D'Alessandro U, et al. Combining individual patient data and aggregate data in mixed treatment comparison meta-analysis: Individual patient data may be beneficial if only for a subset of trials. Stat Med 2013;32(6):914-30.

  • Hong H, Fu H, Price KL, et al. Incorporation of individual-patient data in network meta-analysis for multiple continuous endpoints, with application to diabetes treatment. Stat Med 2015;34(20):2794-819

  • Jansen JP. Network meta-analysis of individual and aggregate level data. Res Synth Methods 2012;3(2):177-90.

  • Thom HH, Capkun G, Cerulli A, et al. Network meta-analysis combining individual patient and aggregate data from a mixture of study designs with an application to pulmonary arterial hypertension. BMC Med Res Methodol 2015;15:34


Diagnosis, prognosis and prediction

  • Chapters 15 to 17 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Riley RD, Dodd SR, Craig JV, et al. Meta-analysis of diagnostic test studies using individual patient data and aggregate data. Stat Med 2008;27(29):6111-36.

  • Levis B, Benedetti A, Levis AW, et al. Selective Cutoff Reporting in Studies of Diagnostic Test Accuracy: A Comparison of Conventional and Individual-Patient-Data Meta-Analyses of the Patient Health Questionnaire-9 Depression Screening Tool. Am J Epidemiol 2017;185(10):954-64. 

  • Levis B, Benedetti A, Thombs BD, et al. Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. BMJ 2019;365:l1476.

  • Riley RD, van der Windt D, Croft P, et al., editors. Prognosis Research in Healthcare: Concepts, Methods and Impact. Oxford, UK: Oxford University Press, 2019.

  • Abo-Zaid G et al. IPD meta-analysis of prognostic factor studies: state of the art? BMC Med Res 2012; 12:56.

  • Riley RD, et al. Prognosis Research Strategy (PROGRESS) 2: prognostic factor research.  PLoS Med 2013; 10(2): e1001380.

  • Sauerbrei W, et al. Evidence-based assessment and application of prognostic markers: the long way from single studies to meta-analysis. Communications in Statistics 2006; 35:1333-1342.

  • Riley RD, Elia EG, Malin G, Hemming K, Price MP. Multivariate meta-analysis of prognostic factor studies with multiple cut-points and/or methods of measurement. Statistics in Medicine 2015; 34:2481-2496.

  • Debray TP, Moons KG, Ahmed I, Koffijberg H, Riley RD: A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Stat Med 2013, 32(18):3158-80.

  • Royston P, Parmar MKB, Sylvester R: Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer. Stat Med 2004, 23:907-926.

  • Steyerberg EW, Nieboer D, Debray TPA, et al. Assessment of heterogeneity in an individual participant data meta-analysis of prediction models: An overview and illustration. Stat Med 2019;38(22):4290-309

  • Ahmed I, Debray TP, Moons KG, Riley RD. Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med Res Methodol. 2014;14:3.

  • Snell KIE, et al. Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model. Journal of Clinical Epidemiology 2016; 69: 40-50.

  • Debray TPA, Riley RD, Rovers MM, Reitsma JB, Moons KGM, Cochrane IPD Meta-analysis Methods Group. Individual participant data (IPD) meta-analyses of diagnostic and prognostic modelling studies: guidance on their use. PLOS Med 2015; 12(10):e1001886.

  • Pennells L, et al. Assessing risk prediction models using individual participant data from multiple studies. Am J Epidemiol 2014; 179(5):621-632.

  • Riley RD, Ensor J, Snell KIE, et al. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis. BMJ 2016;353:i3140

  • White IR, Kaptoge S, Royston P, et al. Meta-analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods. Stat Med 2019;38(3):326-38


Missing data in IPD meta-analysis

  • Chapter 18 of: Riley RD, Tierney J, Stewart LA (Eds). Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research. Wiley, Chicester; 2021 

  • Jolani S, Debray TPA, Koffijberg H, va Buuren S, Moons KGM. Multiple imputation of systematically missing predictors in an individual participant data meta-analysis: a generalised approach using MICE. Stat Med 2015; 34(11):1841-1863.

  • Resche-Rigon M, White IR, Bartlett JW, Peters SA, Thompson SG. Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data. Stat Med 2013; 32(28):4890-4905.

  • Burgess S, White IR, Resche-Rigon M, Wood AM. Combining multiple imputation and meta-analysis with individual participant data. Stat Med 2013; 32:4499-4514.

  • Koopman L, et al. Comparison of methods of handling missing data in individual patient data meta-analyses: an empirical example on antibiotics in children with acute otitis media. Am J Epidemiol 2008; 167(5):540-545.

  • Quartagno M, Carpenter JR. Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariate. Stat Med 2016; 35(17):2938-54.

  • Fibrinogen Studies Collaboration. Systematically missing confounders in individual participant data meta-analysis of observational cohort studies. Stat Med 2009;28(8):1218-37.

  • Audigier V, White IR, Jolani S, et al. Multiple Imputation for Multilevel Data with Continuous and Binary Variables. Statist Sci 2018;33(2):160-83