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Indirect and mixed treatment comparisons

Indirect and mixed treatment comparisons. Hannah Buckley Co-authors: Hannah Ainsworth, Clare Heaps, Catherine Hewitt, Laura Jefferson, Natasha Mitchell, Carole Torgerson, David Torgerson. Overview. Exemplar trials Direct comparisons Indirect comparisons Methodological approaches

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Indirect and mixed treatment comparisons

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  1. Indirect and mixed treatment comparisons Hannah Buckley Co-authors: Hannah Ainsworth, Clare Heaps, Catherine Hewitt, Laura Jefferson, Natasha Mitchell, Carole Torgerson, David Torgerson

  2. Overview • Exemplar trials • Direct comparisons • Indirect comparisons • Methodological approaches • Mixed treatment comparisons • Assumptions

  3. Exemplar trials • 2 RCTs of literacy interventions • EEF ‘writing bundle’ • Grammar for writing (GfW) 1 • Improving writing quality (IWQ) 2 • Improving writing quality of struggling year 6 pupils

  4. Grammar for writing • 15 guided writing sessions over 4 weeks • 53 schools • Progress in English 11 Long Form • Split plot design https://educationendowmentfoundation.org.uk/uploads/pdf/FINAL_EEF_Evaluation_Report_-_Grammar_for_Writing_-_February_2014.pdf

  5. Grammar for writing

  6. Grammar for writing

  7. Improving writing quality • Self-regulated strategy development combined with memorable experiences • 23 primary schools, 3 secondary school • Progress in English 11 Long Form • Cluster trial https://educationendowmentfoundation.org.uk/uploads/pdf/EEF_Evaluation_Report_-_Improving_Writing_Quality_-_May_2014.pdf

  8. Direct comparison • Treatment effect estimates usually from direct comparisons between two treatments in an RCT

  9. Direct comparisons MD= 0.78 95% CI: (0.00, 1.56) MD= 2.53 95% CI: (0.90, 4.16) MD = mean difference

  10. Indirect comparisons • Used to provide estimates when evidence from direct comparisons not available • Adjusted indirect comparison (IC) - common comparator required

  11. IC methods - overview • Frequentist/Bayesian approach • Naïve IC (no advantages of RCT) • Adjusted IC • Meta-regression • Generalised linear mixed models (IPD data) • Confidence profile method • Bayesian Markov chain Monte Carlo (MCMC)

  12. IC methods – simple adjusted • Frequentist approach • Uses aggregate trial data • Adjusted based on common comparison • Estimates from trials extracted • If more than one trial for each comparison then use a weighted combination as in meta-analysis

  13. IC methods –adjusted

  14. IC methods – meta-regression • Frequentist approach • Uses aggregate data • Fixed/random effects • ӨBC modelled as a function of one or more study characteristics as predictor variable(s) • Co-efficient of indicator for comparison gives effect estimate

  15. Indirect comparison • Comparing GfW and IWQ

  16. IC - example Effect estimate for B vs C: ӨBC = ӨAB –ӨAC = 2.53–0.78 = 1.75

  17. IC - example Variance of effect estimate for B vs C: var(ӨBC) = var(ӨAB)+var(ӨAC) = 0.69 + 0.16 = 0.85

  18. IC - example

  19. IC - example • No evidence of a difference in means between pupils receiving each intervention • with a non-significant increase of 1.75 marks (95% CI: -0.06, 3.56) in writing score for those receiving the IWQ intervention compared with those receiving the GfW intervention

  20. Assumptions – IC • Homogeneity assumption: • 2 test • I2 • Similarity assumption in terms of effect moderators • populations should be similar in both sets of trials • participants in trial AB could have been randomised in trial AC • Same estimate would be obtained in trial ABC

  21. Mixed treatment comparison • Combining direct and indirect evidence • Indirect evidence supplements direct evidence • 1 RCT of direct evidence is as precise as indirect evidence based on 4 RCTs 3

  22. Assumptions - MTC • Consistency • indirect estimate would be the same as estimate from direct evidence

  23. Conclusions • Indirect comparisons can provide of relative effectiveness • MTC may provide gains in precision • Methods may be particularly applicable in an education setting where BAU frequently used as a comparator • Caution must be taken with interpretation

  24. References • Torgerson, D., Torgerson, C., Mitchell, N., Buckley, H., Ainsworth, H., et al. (2014). Grammar for Writing Evaluation Report and Executive Summary. Published by the Education Endowment Foundation on educationendowmentfoundation.org.uk. Last accessed 09 Sep 2015. • Torgerson, D., Torgerson, C., Ainsworth, H., Buckley, H., Heaps, C., et al. (2014). Improving Writing Quality Evaluation Report and Executive Summary. Published by the Education Endowment Foundation on educationendowmentfoundation.org.uk. Last accessed 09 Sep 2015. • Glenny, A, D Altman, et al. (2005) Indirect Comparisons of Competing Interventions. Health Technology Assessment vol. 9, no. 26.

  25. Resources • Bucher, Heiner C. et al. (1997) The Results of Direct and Indirect Treatment Comparisons in Meta-Analysis of Randomized Controlled Trials. Journal of Clinical Epidemiology, vol. 50, no. 6: 683–91. • Miladinovic, B., et al (2014). Indirect Treatment Comparison. Stata Journal vol 14, no. 1: 76–86. • Jansen JP, et al (2011). Interpreting indirect treatment comparisons and network meta-analysis for health-care decision making: report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: part 1. Value in Health vol 14, no. 4: 417-28.

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