Individual participant data meta-analysis to examine linear or non-linear treatment-covariate interactions at multiple time-points for a continuous outcome.
individual participant data (IPD) meta‐analysis
longitudinal data
multivariate meta‐analysis
non‐linear analysis
treatment‐effect moderators
treatment‐effect modifiers
Journal
Research synthesis methods
ISSN: 1759-2887
Titre abrégé: Res Synth Methods
Pays: England
ID NLM: 101543738
Informations de publication
Date de publication:
16 Sep 2024
16 Sep 2024
Historique:
revised:
01
07
2024
received:
19
01
2024
accepted:
05
08
2024
medline:
17
9
2024
pubmed:
17
9
2024
entrez:
16
9
2024
Statut:
aheadofprint
Résumé
Individual participant data (IPD) meta-analysis projects obtain, harmonise, and synthesise original data from multiple studies. Many IPD meta-analyses of randomised trials are initiated to identify treatment effect modifiers at the individual level, thus requiring statistical modelling of interactions between treatment effect and participant-level covariates. Using a two-stage approach, the interaction is estimated in each trial separately and combined in a meta-analysis. In practice, two complications often arise with continuous outcomes: examining non-linear relationships for continuous covariates and dealing with multiple time-points. We propose a two-stage multivariate IPD meta-analysis approach that summarises non-linear treatment-covariate interaction functions at multiple time-points for continuous outcomes. A set-up phase is required to identify a small set of time-points; relevant knot positions for a spline function, at identical locations in each trial; and a common reference group for each covariate. Crucially, the multivariate approach can include participants or trials with missing outcomes at some time-points. In the first stage, restricted cubic spline functions are fitted and their interaction with each discrete time-point is estimated in each trial separately. In the second stage, the parameter estimates defining these multiple interaction functions are jointly synthesised in a multivariate random-effects meta-analysis model accounting for within-trial and across-trial correlation. These meta-analysis estimates define the summary non-linear interactions at each time-point, which can be displayed graphically alongside confidence intervals. The approach is illustrated using an IPD meta-analysis examining effect modifiers for exercise interventions in osteoarthritis, which shows evidence of non-linear relationships and small gains in precision by analysing all time-points jointly.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : MRC-NIHR Better Methods Better Research
Organisme : Birmingham Biomedical Research Centre
Organisme : Chartered Society of Physiotherapy Charitable Trust
ID : PRF/16/A07
Organisme : NIHR School for Primary Care Research
Organisme : MRC-NIHR Better Methods Better Research panel
ID : MR/V038168/1
Organisme : NIHR Birmingham Biomedical Research Centre
Organisme : National Institute for Health and Care Research (NIHR) School of Primary Care Research
ID : 351
Informations de copyright
© 2024 The Author(s). Research Synthesis Methods published by John Wiley & Sons Ltd.
Références
Hingorani AD, Windt DA, Riley RD, et al. Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ. 2013;346:e5793.
Riley RD, van der Windt D, Croft P, Moons KGM, eds. Prognosis Research in Healthcare: Concepts, Methods and Impact. Oxford University Press; 2019.
Hudis CA. Trastuzumab – mechanism of action and use in clinical practice. N Engl J Med. 2007;357(1):39‐51.
Brookes ST, Whitely E, Egger M, Smith GD, Mulheran PA, Peters TJ. Subgroup analyses in randomized trials: risks of subgroup‐specific analyses; power and sample size for the interaction test. J Clin Epidemiol. 2004;57(3):229‐236.
Riley RD, Tierney JF, Stewart LA, eds. Individual Participant Data Meta‐Analysis: A Handbook for Healthcare Research. Wiley; 2021.
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(7):683‐697.
Berlin JA, Santanna J, Schmid CH, Szczech LA, Feldman HI. 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.
Thompson SG, Higgins JP. Treating individuals 4: can meta‐analysis help target interventions at individuals most likely to benefit? Lancet. 2005;365(9456):341‐346.
Kent DM, Paulus JK, van Klaveren D, et al. The predictive approaches to treatment effect heterogeneity (PATH) statement. Ann Intern Med. 2019;172:35.
Kent DM, Steyerberg E, van Klaveren D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ. 2018;363:k4245.
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):2115‐2137.
Fisher DJ, Carpenter JR, Morris TP, Freeman SC, Tierney JF. Meta‐analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach? BMJ. 2017;356:j573.
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.
Riley RD, Lambert PC, Staessen JA, et al. Meta‐analysis of continuous outcomes combining individual patient data and aggregate data. Stat Med. 2008;27(11):1870‐1893.
Belias M, Rovers MM, Reitsma JB, Debray TPA, IntHout J. Statistical approaches to identify subgroups in meta‐analysis of individual participant data: a simulation study. BMC Med Res Methodol. 2019;19(1):183.
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.
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.
Riley RD, Ensor J, Hattle M, Papadimitropoulou K, Morris TP. Two‐stage or not two‐stage? That is the question for IPD meta‐analysis projects. Res Synth Methods. 2023;14(6):903‐910.
White IR, Kaptoge S, Royston P, Sauerbrei W, Emerging Risk Factors Collaboration. Meta‐analysis of non‐linear exposure‐outcome relationships using individual participant data: a comparison of two methods. Stat Med. 2019;38(3):326‐338.
Royston P, Sauerbrei W. A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials. Stat Med. 2004;23(16):2509‐2525.
Sauerbrei W, Royston P. Investigating treatment‐effect modification by a continuous covariate in IPD meta‐analysis: an approach using fractional polynomials. BMC Med Res Methodol. 2022;22(1):98.
Gasparrini A, Armstrong B, Kenward MG. Multivariate meta‐analysis for non‐linear and other multi‐parameter associations. Stat Med. 2012;31:3821‐3839.
Belias M, Rovers MM, Hoogland J, Reitsma JB, Debray TPA, IntHout J. Predicting personalised absolute treatment effects in individual participant data meta‐analysis: an introduction to splines. Res Synth Methods. 2022;13(2):255‐283.
Riley RD, Price MJ, Jackson D, et al. Multivariate meta‐analysis using individual participant data. Res Synth Methods. 2015;6:157‐174.
Simmonds MC, Higgins JP. Covariate heterogeneity in meta‐analysis: criteria for deciding between meta‐regression and individual patient data. Stat Med. 2007;26(15):2982‐2999.
Fisher DJ. Two‐stage individual participant data meta‐analysis and generalized forest plots. Stata J. 2015;15(2):369‐396.
Higgins JP, Thompson SG, Spiegelhalter DJ. A re‐evaluation of random‐effects meta‐analysis. J R Stat Soc Ser A Stat Soc. 2009;172:137‐159.
Holden MA, Hattle M, Runhaar J, et al. Moderators of the effect of therapeutic exercise for knee and hip osteoarthritis: a systematic review and individual participant data meta‐analysis. Lancet Rheumatol. 2023;5(7):e386‐e400.
Hartung J, Knapp G. A refined method for the meta‐analysis of controlled clinical trials with binary outcome. Stat Med. 2001;20(24):3875‐3889.
Jackson D, Law M, Rucker G, Schwarzer G. The Hartung‐Knapp modification for random‐effects meta‐analysis: a useful refinement but are there any residual concerns? Stat Med. 2017;36(25):3923‐3934.
Royston P, Sauerbrei W. Interactions between treatment and continuous covariates: a step toward individualizing therapy. J Clin Oncol. 2008;26(9):1397‐1399.
Kasenda B, Sauerbrei W, Royston P, et al. Multivariable fractional polynomial interaction to investigate continuous effect modifiers in a meta‐analysis on higher versus lower PEEP for patients with ARDS. BMJ Open. 2016;6(9):e011148.
Kasenda B, Sauerbrei W, Royston P, Briel M. Investigation of continuous effect modifiers in a meta‐analysis on higher versus lower PEEP in patients requiring mechanical ventilation – protocol of the ICEM study. Syst Rev. 2014;3:46.
Harrell FE Jr. Regression Modeling Strategies: with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Second Edition). Springer; 2015.
de Boor C. A practical guide to splines. Vol xviii. Springer; 2001:346.
Royston P, Sauerbrei W. Multivariable modeling with cubic regression splines: a principled approach. Stata J. 2007;7(1):45‐70.
Binder H, Sauerbrei W, Royston P. Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response. Stat Med. 2013;32(13):2262‐2277.
White IR. Multivariate random‐effects meta‐regression: updates to mvmeta. Stata J. 2011;11:255‐270.
White IR. Multivariate random‐effects meta‐analysis. Stata J. 2009;9:40‐56.
Riley RD, Jackson D, Salanti G, et al. Multivariate and network meta‐analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples. BMJ. 2017;358:j3932.
Jackson D, Riley RD, White IR. Multivariate meta‐analysis: potential and promise. Stat Med. 2011;30:2481‐2498.
Jackson D, White IR, Price M, Copas J, Riley RD. Borrowing of strength and study weights in multivariate and network meta‐analysis. Stat Methods Med Res. 2017;26(6):2853‐2868.
Altman DG, Lausen B, Sauerbrei W, Schumacher M. Dangers of using "optimal" cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst. 1994;86(11):829‐835.
Walker R, Stewart L, Simmonds M. Estimating interactions in individual participant data meta‐analysis: a comparison of methods in practice. Syst Rev. 2022;11(1):211.
Baker RD, Jackson D. Statistical application of barycentric rational interpolants: an alternative to splines. Comput Stat. 2014;29(5):1065‐1081.
Sauerbrei W, Royston P. A new strategy for meta‐analysis of continuous covariates in observational studies. Stat Med. 2011;30(28):3341‐3360.
Huber C, Benda N, Friede T. Subgroup identification in individual participant data meta‐analysis using model‐based recursive partitioning. Adv Data Anal Classif. 2022;16(3):797‐815.
Jones AP, Riley RD, Williamson PR, Whitehead A. Meta‐analysis of individual patient data versus aggregate data from longitudinal clinical trials. Clin Trials. 2009;6(1):16‐27.
Riley RD. Multivariate meta‐analysis: the effect of ignoring within‐study correlation. J R Stat Soc A Stat Soc. 2009;172:172‐811.
Trikalinos TA, Hoaglin DC, Schmid CH. Empirical and Simulation‐Based Comparison of Univariate and Multivariate Meta‐Analysis for Binary Outcomes. Methods Research Report Agency for Healthcare Research and Quality. 2013.
Gao Y, Liu M, Shi S, et al. Prespecification of subgroup analyses and examination of treatment‐subgroup interactions in cancer individual participant data meta‐analyses are suboptimal. J Clin Epidemiol. 2021;138:156‐167.
Godolphin PJ, Marlin N, Cornett C, et al. Use of multiple covariates in assessing treatment‐effect modifiers: a methodological review of individual participant data meta‐analyses. Res synth. Methods. 2023;15:107‐116.
Hoogland J, IntHout J, Belias M, et al. A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint. Stat Med. 2021;40(26):5961‐5981.
Efthimiou O, Mavridis D, Cipriani A, Leucht S, Bagos P, Salanti G. An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios. Stat Med. 2014;33(13):2275‐2287.
Dagne GA, Brown CH, Howe G, Kellam SG, Liu L. Testing moderation in network meta‐analysis with individual participant data. Stat Med. 2016;35(15):2485‐2502.
Jansen JP, Vieira MC, Cope S. Network meta‐analysis of longitudinal data using fractional polynomials. Stat Med. 2015;34(15):2294‐2311.
Groenwold RH, White IR, Donders AR, Carpenter JR, Altman DG, Moons KG. Missing covariate data in clinical research: when and when not to use the missing‐indicator method for analysis. CMAJ. 2012;184(11):1265‐1269.
Sullivan TR, White IR, Salter AB, Ryan P, Lee KJ. Should multiple imputation be the method of choice for handling missing data in randomized trials? Stat Methods Med Res. 2018;27(9):2610‐2626.