Sharing information across patient subgroups to draw conclusions from sparse treatment networks.
informative priors
mixed treatment comparisons
sharing information
sparse data
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
Apr 2024
Apr 2024
Historique:
revised:
07
11
2023
received:
26
11
2022
accepted:
26
12
2023
medline:
19
4
2024
pubmed:
19
4
2024
entrez:
18
4
2024
Statut:
ppublish
Résumé
Network meta-analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large-sample approximations that are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two-stage approach where at the first stage, we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks.
Identifiants
pubmed: 38637311
doi: 10.1002/bimj.202200316
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2200316Subventions
Organisme : French National Research Agency
ID : ANR-22-CE36-0013-01
Organisme : UK Medical Research Council
ID : MC_UU_00004/06
Informations de copyright
© 2024 The Authors. Biometrical Journal published by Wiley‐VCH GmbH.
Références
Baker, R. D., & Jackson, D. (2013). Meta‐analysis inside and outside particle physics: Two traditions that should converge? Research Synthesis Methods, 4(2), 109–124. https://doi.org/10.1002/jrsm.1065
Brooks, S. P., & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7(4), 434–455.
Chaimani, A., Caldwell, D. M., Li, T., Higgins, J. P., & Salanti, G. (2019). Undertaking network meta‐analyses. In: Higgins, J. P. T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., Welch, V. A. (Eds.), Cochrane handbook for systematic reviews of interventions (pp. 285–320). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119536604.ch11
Chen, M. H., & Ibrahim, J G. (2000). Power prior distributions for regression models. Statistical Science, 15(1), 46–60. https://doi.org/10.1214/ss/1009212673
Dias, S., Welton, N. J., & Caldwell, D. M. (2010). Ades AE. Checking consistency in mixed treatment comparison meta‐analysis. Statistics in Medicine, 29(7–8), 932–944. https://doi.org/10.1002/sim.3767
Dunne, J., Rodriguez, W. J., Murphy, M. D., Beasley, B. N., Burckart, G. J., Filie, J. D., Lewis, L. L., Sachs, H. C., Sheridan, P. H., Starke, P., & Yao, L. P. (2011). Extrapolation of adult data and other data in pediatric drug‐development programs. Pediatrics, 128(5), e1242–e1249. https://doi.org/10.1542/peds.2010‐3487
Dunoyer, M. (2011). Accelerating access to treatments for rare diseases. Orphanet Journal of Rare Diseases, 10(7), 475–476. https://doi.org/10.1038/nrd3493
Efthimiou, O., Mavridis, D., Debray, T. P. A., Samara, M., Belger, M., Siontis, G. C. M., Leucht, S., & Salanti, G. (2017). Combining randomized and non‐randomized evidence in network meta‐analysis. Statistics in Medicine, 36(8), 1210–1226. https://doi.org/10.1002/sim.7223
Hampson, L. V., Whitehead, J., Eleftheriou, D., & Brogan, P. (2014). Bayesian methods for the design and interpretation of clinical trials in very rare diseases. Statistics in Medicine, 33(24), 4186–4201. https://doi.org/10.1002/sim.6225
Higgins, J. P. T., Thompson, S. G., & Spiegelhalter, D. J. (2009). A re‐evaluation of random‐effects meta‐analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society), 172, 137–159. https://doi.org/10.1111/j.1467‐985X.2008.00552.x
Hlavin, G., Koenig, F., Male, C., & Posch, M., & Bauer, P. (2016). Evidence, eminence and extrapolation. Statistics in Medicine, 35(13), 2117–2132. https://doi.org/10.1002/sim.6865
Ibrahim, J. G., Chen, M. H., Gwon, Y., & Chen, F. (2015). The power prior: Theory and applications. Statistics in Medicine, 34(28), 3724–3749. https://doi.org/10.1002/sim.6728
Jackson, D., Barrett, J. K., Rice, S., White, I. R., & Higgins, J. P. T. (2014). A design‐by‐treatment interaction model for network meta‐analysis with random inconsistency effects. Statistics in Medicine, 33(21), 3639–3654. https://doi.org/10.1002/sim.6188
Law, M., Alam, N., Veroniki, A. A., Yu, Y., & Jackson, D. (2019). Two new approaches for the visualisation of models for network meta‐analysis. BMC Medical Research Methodology [Electronic Resource], 19(1), 61. https://doi.org/10.1186/s12874‐019‐0689‐9
Leucht, S., Chaimani, A., Krause, M., Schneider‐Thoma, J., Wang, D., Dong, S., Samara, M., Peter, N., Huhn, M., Priller, J., & Davis, J. M. (2022). The response of schizophrenia subgroups to different antipsychotic drugs: A systematic review and meta‐analysis. Lancet Psychiatry, 9, 884–893. https://doi.org/10.1016/S2215‐0366(22)00304‐2
Nikolakopoulou, A., Mavridis, D., Furukawa, T. A., Cipriani, A., Tricco, A. C., Straus, S. E., Siontis, G. C. M., Egger, M., & Salanti, G. (2018). Living network meta‐analysis compared with pairwise meta‐analysis in comparative effectiveness research: Empirical study. British Medical Journal, 360, k585. https://doi.org/10.1136/bmj.k585
Parmar, M. K., Sydes, M. R., Cafferty, F. H., Choodari‐Oskooei, B., Langley, R. E., Brown, L., Phillips, P. P., Spears, M. R., Rowley, S., Kaplan, R., James, N. D., Maughan, T., Paton, N., & Royston, P. J. (2017). Testing many treatments within a single protocol over 10 years at MRC Clinical Trials Unit at UCL: Multi‐arm, multi‐stage platform, umbrella and basket protocols. Clinical Trials, 14(5), 451–461. https://doi.org/10.1177/1740774517725697
Phillippo, D. M., Dias, S., Ades, A. E., & Welton, N. J. (2020). Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study. Statistics in Medicine, 39(30), 4885–4911. https://doi.org/10.1002/sim.8759
Plummer, M. (2015). rjags: Bayesian Graphical Models using MCMC. R package version 4–10. https://CRAN.R‐project.org/package=rjags Published online 2015
R Core Team. (2016). R: A Language and Environment for Statistical Computing. Published online 2016. https://www.R‐project.org/
Renfro, L. A., & Sargent, D J. (2017). Statistical controversies in clinical research: Basket trials, umbrella trials, and other master protocols: a review and examples. Annals of Oncology, 28(1), 34–43. https://doi.org/10.1093/annonc/mdw413
Rhodes, K. M., Turner, R. M., & Higgins, J. P. T. (2015). Predictive distributions were developed for the extent of heterogeneity in meta‐analyses of continuous outcome data. Journal of Clinical Epidemiology, 68(1), 52–60. https://doi.org/10.1016/j.jclinepi.2014.08.012
Rhodes, K. M., Turner, R. M., White, I. R., Jackson, D., Spiegelhalter, D. J., & Higgins, J. P. T. (2016). Implementing informative priors for heterogeneity in meta‐analysis using meta‐regression and pseudo data. Statistics in Medicine, 35(29), 5495–5511. https://doi.org/10.1002/sim.7090
Röver, C., Bender, R., Dias, S., Schmid, C. H., Schmidli, H., Sturtz, S., Weber, S., & Friede, T. (2021). On weakly informative prior distributions for the heterogeneity parameter in Bayesian random‐effects meta‐analysis. Research Synthesis Methods, 12(4), 448–474. https://doi.org/10.1002/jrsm.1475
Röver, C., Wandel, S., & Friede, T. (2019). Model averaging for robust extrapolation in evidence synthesis. Statistics in Medicine, 38(4), 674–694. https://doi.org/10.1002/sim.7991
Salanti, G., Ades, A. E., & Ioannidis, J P A. (2011). Graphical methods and numerical summaries for presenting results from multiple‐treatment meta‐analysis: An overview and tutorial. Journal of Clinical Epidemiology, 64(2), 163–171. https://doi.org/10.1016/j.jclinepi.2010.03.016
Salanti, G., Higgins, J. P. T., Ades, A. E., & Ioannidis, J. P. A. (2008). Evaluation of networks of randomized trials. Statistical Methods in Medical Research, 17(3), 279–301. https://doi.org/10.1177/0962280207080643
Sterne, J. A. C., Savović, J., Page, M. J., Blencowe, N. S., Boutron, I., Cates, C. J., Cheng, H.‐Y., Corbett, M. S., Eldridge, S. M., Emberson, J. R., Hernán, M. A., Hopewell, S., Hróbjartsson, A., Junqueira, D. R., Jüni, P., Kirkham, J. J., Lasserson, T., Li, T., … Higgins, J. P. T. (2019). RoB 2: A revised tool for assessing risk of bias in randomised trials. Bmj, 366, l4898. https://doi.org/10.1136/bmj.l4898
Turner, R. M., Davey, J., Clarke, M. J., Thompson, S. G., & Higgins, J P. (2012). Predicting the extent of heterogeneity in meta‐analysis, using empirical data from the Cochrane Database of Systematic Reviews. International Journal of Epidemiology, 41(3), 818–827. https://doi.org/10.1093/ije/dys041
Turner, R. M., Domínguez‐Islas, C. P., Jackson, D., Rhodes, K. M., & White, I. R. (2019). Incorporating external evidence on between‐trial heterogeneity in network meta‐analysis. Statistics in Medicine, 38(8), 1321–1335. https://doi.org/10.1002/sim.8044
Turner, R. M., Jackson, D., Wei, Y., Thompson, S. G., & Higgins, J. P. T. (2015). Predictive distributions for between‐study heterogeneity and simple methods for their application in Bayesian meta‐analysis. Statistics in Medicine, 34(6), 984–998. https://doi.org/10.1002/sim.6381
Turner, R. M., Turkova, A., Moore, C. L., Bamford, A., Archary, M., Barlow‐Mosha, L. N., Cotton, M. F., Cressey, T. R., Kaudha, E., Lugemwa, A., Lyall, H., Mujuru, H. A., Mulenga, V., Musiime, V., Rojo, P., Tudor‐Williams, G., Welch, S. B., Gibb, D. M., … Ford, D. (2022). Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial. BMC Medical Research Methodology, 22(1), 49. https://doi.org/10.1186/s12874‐022‐01539‐3
U.S. Department of Health and Human Services Food and Drug Administration. (2016). Leveraging existing clinical data for extrapolation to pediatric uses of medical devices—Guidance for industry and Food and Drug Administration staff draft guidance.
Veroniki, A. A., Tsokani, S., Zevgiti, S., Pagkalidou, I., Kontouli, K.‐M., Ambarcioglu, P., Pandis, N., Lunny, C., Nikolakopoulou, A., Theodoros, P., Chaimani, A., Straus, S. E., Brian, H., Tricco, A. C., Mavridis, D., & Salanti, G. (2021). Do reporting guidelines have an impact? Empirical assessment of changes in reporting before and after the PRISMA extension statement for network meta‐analysis. Systematic Reviews, 10(1), 246. https://doi.org/10.1186/s13643‐021‐01780‐9
Wadsworth, I., Hampson, L. V., & Jaki, T. (2018). Extrapolation of efficacy and other data to support the development of new medicines for children: A systematic review of methods. Statistical Methods in Medical Research, 27(2), 398–413. https://doi.org/10.1177/0962280216631359
Yoon, J. H., Dias, S., & Hahn, S. (2021). A method for assessing robustness of the results of a star‐shaped network meta‐analysis under the unidentifiable consistency assumption. BMC Medical Research Methodology [Electronic Resource], 21(1), 113. https://doi.org/10.1186/s12874‐021‐01290‐1