Perform a meta-analysis with RMSTD using individual patient data. Methods include:
"mvma" a multivariate meta-analysis borrowing strength across time-points with within-trial covariance matrix derived analytically
"mvma_boot" a multivariate meta-analysis borrowing strength across time-points with within-trial covariance matrix derived by bootstrap
"uni" a univariate meta-analysis for combined effect at each time-point using only available data
"uni_flex" a univariate meta-analysis for combined effect at each time-point using estimates based on flexible parametric models as described by Wei et al (Stat Med 2015).
metaRMSTD(trialdata, time_horizons, MA_method, nboot = 500)
IPD trial data, see details for specifications
specified vector of time horizons for the meta-analysis
the desired meta-analysis method; options are: "mvma", "mvma_boot", "uni", "uni_flex"
the number of bootstrap iterations, if using the MVMA with bootstrap covariance matrix; default=500
Specify the time horizons at which to calculate the meta-analytic results.
trialdata must be formatted as a dataframe containing the IPD for each single trial.
Variable names must include Trial ID ("trialID"), Time ("Time"), Event status ("Event"), and randomization group ("Arm").
metaRMSTD function returns a list object containing the random-effects model results,
the RMSTD and SE values for each trial at each available time horizon, and the estimated within-trial covariance matrix for each RCT.
RMSTD is estimable if time horizon > minimum of last observed times across the two groups. We implement the method-of-moments estimator for MVMA (Chen et al. Biometrics 2012, Jackson et al. Biometrical Journ 2013) and Dersimonian and Laird for univariate MA.
Wei, Y, Royston, P, Tierney, JF and Parmar, MKB. (2015). Meta-analysis of time-to-event outcomes from randomized trials using restricted mean survival time: application to individual participant data. Stat Med 34(21), 2881-2898.
Chen, Han, Alisa K. Manning, and Josée Dupuis. "A method of moments estimator for random effect multivariate meta-analysis." Biometrics 68.4 (2012): 1278-1284.
Jackson, Dan, Ian R. White, and Richard D. Riley. "A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression." Biometrical Journal 55.2 (2013): 231-245.
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# read in built-in dataset data(AorticStenosisTrials) # meta-analysis to obtain combined effect by multivariate model (method="mvma") result <- metaRMSTD(AorticStenosisTrials, time_horizons=c(12,24,36), MA_method="mvma") # generate figure: obj <- RMSTcurves(AorticStenosisTrials, time_horizons=c(12,24,36), tmax=40, nboot=500) RMSTplot(obj, xlim=c(0,40), ylim=c(-0.25,2.75), yby=0.5, ylab="RMSTD (mos)", xlab="Time (mos)")
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