Skip to main content
SpringerLink
Log in

Individual Patient Data Meta-Analysis and Network Meta-Analysis

  • Protocol
  • First Online:
Meta-Research

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2345))

Abstract

Meta-analyses are often conducted using trial-level summary data. However, when individual patient data (IPD ) is available, there is greater flexibility in the analysis and a wider range of statistical models that can be fitted. There are two approaches to fitting IPD models. The traditional two-stage approach involves analyzing each trial individually in the first stage and then combining trial estimates of treatment effectiveness in the second stage using methods developed for aggregate data meta-analysis. Growing in popularity is the one-stage approach in which trials are analyzed and synthesized within one statistical model whilst the clustering of patients within trials is accounted for. This chapter outlines both fixed effect and random effects one- and two-stage meta-analysis models for continuous, binary, and time-to-event outcomes. The meta-analysis framework is then extended to the scenario where there are more than two treatments and network meta-analysis models are described.

The availability of IPD provides greater statistical power for investigating interactions between treatments and covariates. Treatment–covariate interactions contain both within- and across-trial information where the across-trial information may be subject to ecological bias. This chapter presents network meta-analysis models separating out the within- and across-trial information and finishes by considering practical solutions for dealing with missing covariate data, assessing the consistency assumption, combining IPD and aggregate data and specific considerations for time-to-event outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 149.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chalmers I (1993) The Cochrane collaboration: preparing, maintaining and disseminating systematic reviews of the effects of health care. Ann N Y Acad Sci 703:156–165

    Article  CAS  Google Scholar 

  2. Stewart L, Tierney J (2002) To IPD or not to IPD? Advantages and disadvantages of systematic reviews using individual patient data. Eval Health Prof 25(1):76–97

    Article  Google Scholar 

  3. Simmonds MC, Higgins J, Stewart L, Tierney JF, Clarke M, Thompson S (2005) Meta-analysis of individual patient data from randomized trials: a review of methods used in practice. Clin Trials 2:209–217

    Article  Google Scholar 

  4. Jansen JP (2012) Network meta-analysis of individual and aggregate level data. Res Synth Methods 3(2):177–190. https://doi.org/10.1002/jrsm.1048

    Article  PubMed  Google Scholar 

  5. Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, Welch V (2019) Cochrane handbook for systematic reviews of interventions, 2nd edition edn. John Wiley & Sons, Chichester (UK)

    Book  Google Scholar 

  6. Simmonds M, Stewart G, Stewart L (2015) A decade of indiviudal participant data meta-analyses: a review of current practice. Contemp Clin Trials 45:76–83

    Article  Google Scholar 

  7. 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 36(5):855–875. https://doi.org/10.1002/sim.7141

    Article  PubMed  Google Scholar 

  8. Morris T, Fisher D, Kenward M, Carpenter J (2018) Meta-analysis of Gaussian individual patient data: two-stage or not two-stage? Stat Med 37(9):1419–1438

    Article  Google Scholar 

  9. Riley RD, Lambert PC, Abo-Zaid G (2010) Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ 340(feb05 1):c221. https://doi.org/10.1136/bmj.c221

    Article  PubMed  Google Scholar 

  10. Debray TP, Schuit E, Efthimiou O, Reitsma JB, Ioannidis J, Salanti G, Moons KG (2018) An overview of methods for network meta-analysis using individual participant data: when do benefits arise? Stat Methods Med Res 27(5):1351–1364

    Article  Google Scholar 

  11. Debray TP, Moons KG, Abo-Zaid GM, Koffijberg H, Riley RD (2013) Individual participant data meta-analysis for a binary outcome: one-stage or two-stage? PLoS One 8(4):e60650. https://doi.org/10.1371/journal.pone.0060650

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Abo-Zaid G, Guo B, Deeks J, Debray TPA, Steyerberg E, Moons KGM, Riley RD (2013) Individual participant data meta-analyses should not ignore clustering. J Clin Epidemiol 66:865–873

    Article  Google Scholar 

  13. Legha A, Riley RD, Ensor J, Snell K, Morris TP, Burke DL (2018) Individual particiapnt data meta-analysis of continuous outcomes: a comparison of approaches for specifying and estimating one-stage models. Stat Med 37:4404–4420

    Article  Google Scholar 

  14. Debray TPA, Moons KGM, van Valkenhoef G, Efthimiou O, Hummel N, Groenwold RHH, Reitsma JB (2015) Get real in individual participant data (IPD) meta-analysis: a review of the methodology. Res Synth Methods 6(4):293–309. https://doi.org/10.1002/jrsm.1160

    Article  PubMed  PubMed Central  Google Scholar 

  15. Higgins J, Whitehead A (1996) Borrowing strength from external trials in a meta-analysis. Stat Med 15:2733–2749

    Article  CAS  Google Scholar 

  16. Freeman SC, Fisher D, Tierney JF, Carpenter JR (2018) A framework for identifying treatment-covariate interactions in individual participant data network meta-analysis. Res Synth Methods 9(3):393–407. https://doi.org/10.1002/jrsm.1300

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Rothwell P (2005) Subgroup analysis in randomised controlled trials: importance indications, and interpretation. Lancet 365:176–186

    Article  Google Scholar 

  18. Fisher DJ, Carpenter JR, Morris TP, Freeman SC, Tierney JF (2017) Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach? BMJ 356:j573. https://doi.org/10.1136/bmj.j573

    Article  PubMed  PubMed Central  Google Scholar 

  19. Dias S, Ades A, Welton NJ, Jansen JP, Sutton A (2018) Network meta-analysis for decision making. John Wiley & Sons, Hoboken, NJ, USA

    Book  Google Scholar 

  20. Donegan S, Williamson P, D'Alessandro U, Smith CT (2012) Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta-analysis: individual patient-level covariates versus aggregate trial-level covariates. Stat Med 31(29):3840–3857. https://doi.org/10.1002/sim.5470

    Article  PubMed  Google Scholar 

  21. Fisher DJ, Copas A, Tierney JF, Parmar MK (2011) A critical review of methids for the assessment of patient-level interactions in individual particiapnt data meta-analysis of randomized trials, and guidance for practitioners. J Clin Epidemiol 64:949–967

    Article  CAS  Google Scholar 

  22. Riley RD, Lambert PC, Staessen J, Wang J, Gueyffier F, Thijs L, Boutitie F (2008) Meta-analysis of continuous outcomes combining individual patient data and aggregate data. Stat Med 27:1870–1893

    Article  Google Scholar 

  23. Berlin J, Santanna J, Schmid C, Szczech L, Feldman H (2002) Individual patient-versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head. Stat Med 21:371–387

    Article  Google Scholar 

  24. 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 36(5):772–789. https://doi.org/10.1002/sim.7171

    Article  PubMed  Google Scholar 

  25. Simmonds M, Higgins J (2007) Covariate heterogeneity in meta-analysis: criteria for deciding between meta-regression and individual aptient data. Stat Med 26:2982–2999

    Article  CAS  Google Scholar 

  26. Thompson S, Higgins J (2005) Can meta-analysis help target interventions at individuals most likely to benefit? Lancet 365:341–346

    Article  Google Scholar 

  27. Donegan S, Welton NJ, Tudur Smith C, D'Alessandro U, Dias S (2017) Network meta-analysis including treatment by covariate interactions: consistency can vary across covariate values. Res Synth Methods 8(4):485–495. https://doi.org/10.1002/jrsm.1257

    Article  PubMed  PubMed Central  Google Scholar 

  28. Pigott T (2001) A review of methods for missing data. Educ Res Eval 7:353–383

    Article  Google Scholar 

  29. Carpenter JR, Kenward M (2007) Missing data in randomised controlled trials: a practical guide. Health Technology Assessment Methodology Programme, Birmingham, p 199. https://researchonlinelshtmacuk/id/eprint/4018500

    Google Scholar 

  30. Higgins J, White I, Wood A (2008) Imputation methods for missing outcome data in meta-analysis of clinical trials. Clin Trials 5(3):225–239

    Article  Google Scholar 

  31. Mavridis D, Chaimani A, Efthimiou O, Leucht S, Salanti G (2014) Addressing missing outcome data in meta-analysis. Evid Based Ment Health 17:85–89

    Article  Google Scholar 

  32. Burgess S, White IR, Resche-Rigon M, Wood AM (2013) Combining multiple imputation and meta-analysis with individual participant data. Stat Med 32(26):4499–4514. https://doi.org/10.1002/sim.5844

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lunn D, Jackson C, Best N, Thomas A, Spiegelhalter D (2013) The BUGS book. A practical introduction to Bayesian analysis. Texts in statistical science. CRC Press, Boca Raton, FL, USA

    Google Scholar 

  34. Quartagno M, Carpenter JR (2016) Multiple imputation for IPD meta-analysis: allowing for heteroegneity and studies with missing covariates. Stat Med 35:2938–2954

    Article  CAS  Google Scholar 

  35. Donegan S, Williamson P, D'Alessandro U, Garner P, Smith CT (2013) 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 32(6):914–930. https://doi.org/10.1002/sim.5584

    Article  PubMed  Google Scholar 

  36. Saramago P, Sutton AJ, Cooper NJ, Manca A (2012) Mixed treatment comparisons using aggregate and individual participant level data. Stat Med 31(28):3516–3536. https://doi.org/10.1002/sim.5442

    Article  PubMed  Google Scholar 

  37. Saramago P, Chuang L, Soares MO (2014) Network meta-analysis of (individual patient) time to event data alongside (aggregate) count data. BMC Med Res Methodol 14:105

    Article  Google Scholar 

  38. Sutton A, Kendrick D, Coupland C (2008) Meta-analysis of individual- and aggregate-level data. Stat Med 27:651–669

    Article  CAS  Google Scholar 

  39. Riley RD, Steyerberg E (2010) Meta-analysis of a binary outcome using individual participant data and aggregate data. Res Synth Methods 1:2–19

    Article  Google Scholar 

  40. Dias S, Welton NJ, Sutton A, Ades A (2011, Last updated 2016) NICE DSU technical support document 2: a generalised linear modelling framework for pairwise and network meta-analysis of randomised controlled trials. Available from www.nicedsu.org.uk

    Google Scholar 

  41. Royston P, Parmar MK (2016) Augmenting the logrank test in the design of clinical trials in which non-proportional hazards of the treatment effect may be anticipated. BMC Med Res Methodol 16:16

    Article  Google Scholar 

  42. Trinquart L, Jacot J, Conner S, Porcher R (2016) Comparison of treatment effects measured by the hazard ratio and by the ratio of restricted mean survival times in oncology randomized controlled trials. J Clin Oncol 34(15):1813–1819

    Article  Google Scholar 

  43. Freeman SC, Carpenter JR (2017) Bayesian one-step IPD network meta-analysis of time-to-event data using Royston-Parmar models. Res Synth Methods 8(4):451–464. https://doi.org/10.1002/jrsm.1253

    Article  PubMed  PubMed Central  Google Scholar 

  44. Lu G, Ades A, Sutton A, Cooper N, Briggs A, Caldwell D (2007) Meta-analysis of mixed treatment comparisons at multiple follow-up times. Stat Med 26(20):3681–3699

    Article  CAS  Google Scholar 

  45. Royston P, Parmar MK (2002) Flexible parametric proportional-hazards and proportional-odds models for censored survival data with application to prognostic modelling and estimation of treatment effects. Stat Med 21(15):2175–2197

    Article  Google Scholar 

  46. Royston P, Altman D (1994) Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling (with discussion). Appl Stat 43(3):429–467

    Article  Google Scholar 

  47. Jansen JP (2011) Network meta-analysis of survival data with fractional polynomials. BMC Med Res Methodol 11:61

    Article  Google Scholar 

  48. Crowther MJ, Riley RD, Staessen J, Wang J, Gueyffier F, Lambert PC (2012) Individual patient data meta-analysis of survival data using Poisson regression models. BMC Med Res Methodol 12:34

    Article  Google Scholar 

  49. de Jong VMT, Moons KGM, Riley RD, Smith CT, Marson AG, Eijkemans MJC, Debray TPA (2019) 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 11(2):148–168. https://doi.org/10.1002/jrsm.1384

    Article  Google Scholar 

  50. Parmar MK, Torri V, Stewart L (1998) Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat Med 17:2815–2834

    Article  CAS  Google Scholar 

  51. Williamson P, Tudur Smith C, Hutton J, Marson A (2002) Aggregate data meta-analysis with time-to-event outcomes. Stat Med 21:3337–3351

    Article  Google Scholar 

  52. Guyot P, Ades A, Ouwens M, Welton N (2012) Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Med Res Methodol 12:9

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Health Sciences, University of Leicester, Leicester, UK

    Suzanne C. Freeman

Authors
  1. Suzanne C. Freeman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suzanne C. Freeman .

Editor information

Editors and Affiliations

  1. Department of Hygiene and Epidemiology University of Ioannina Medical School, Department of Epidemiology and Biostatistics Imperial College London London, UK, Ioannina, Greece

    Evangelos Evangelou

  2. Knowledge Translation Program Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Department of Surgery and Cancer Faculty of Medicine Institute of Reproductive and Developmental Biology Imperial College London, UK ISSN, Toronto, ON, Canada

    Areti Angeliki Veroniki

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Freeman, S.C. (2022). Individual Patient Data Meta-Analysis and Network Meta-Analysis. In: Evangelou, E., Veroniki, A.A. (eds) Meta-Research. Methods in Molecular Biology, vol 2345. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1566-9_17

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1566-9_17

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1565-2

  • Online ISBN: 978-1-0716-1566-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation