bpp_1interim_continuous: Bayesian Predictive Power (BPP) for Continuous Endpoint In bpp: Computations Around Bayesian Predictive Power

Description

Compute BPP and posterior density for a continuous endpoint, e.g. mean difference, assuming either an unblinded or blinded interim result.

Usage

 1 2 3 bpp_1interim_continuous(prior = c("normal", "flat"), successmean, stDev, n1, n2, IntEffBoundary, IntFutBoundary, IntFix, priormean, propA = 0.5, thetas, ...)

Arguments

 prior Prior density on effect sizes. successmean The mean difference that defines success at the final analysis. We assume that a higher mean is better. Typically chosen to be the minimal detectable difference, i.e. the critical on the scale of the effect size of interest corresponding to the significance level at the final analysis. stDev Standard deviation of measurements in one group. Used to compute standard error at final analysis. n1 2-d vector of sample sizes in intervention arm, at interim and final analysis. n2 2-d vector of sample sizes in control arm, at interim and final analysis. IntEffBoundary Efficacy boundary at the interim analysis. IntFutBoundary Futility boundary at the interim analysis. IntFix Effect sizes observed at the interim analyis, to compute BPP for an unblinded interim analysis. priormean Prior mean. propA Proportion of subjects randomized to arm A. thetas Grid to compute posterior density on. ... Further arguments specific to the chosen prior (see bpp_1interim_continuous for examples).

Value

A list containing the following elements:

 initial BPP BPP based on the prior. BPP after not stopping at interim interval BPP after not stopping at a blinded interim, provides the results corresponding to IntEffBoundary and IntFutBoundary. BPP after not stopping at interim exact BPP after not stopping at an unblinded interim, provides the results corresponding to IntFix. posterior density interval The posterior density, interval knowledge, i.e. corresponding to IntEffBoundary and IntFutBoundary. posterior power interval The posterior power, interval knowledge, i.e. corresponding to IntEffBoundary and IntFutBoundary. posterior density exact The posterior density, exact knowledge of interim result, i.e. corresponding to IntFix.

Author(s)

Kaspar Rufibach (maintainer)
kaspar.rufibach@roche.com

References

Rufibach, K., Jordan, P., Abt, M. (2016a). Sequentially Updating the Likelihood of Success of a Phase 3 Pivotal Time-to-Event Trial based on Interim Analyses or External Information. J. Biopharm. Stat., 26(2), 191–201.

Rufibach, K., Burger, H.U., Abt, M. (2016b). Bayesian Predictive Power: Choice of Prior and some Recommendations for its Use as Probability of Success in Drug Development. Pharm. Stat., 15, 438–446.

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 # standard deviation of measurments in one group stDev <- 24 # number of patients at interim n1_int <- 46 n2_int <- 46 # number of patients at final analysis n1 <- 92 n2 <- 92 # MDD at final analysis (corresponds to delta = 10 for 80% power) mdd <- 7.023506 # efficacy boundary effi <- 15 # futility boundary --> chosen informally futi <- 0 # prior priormean <- 12.3 sd0 <- 4.2 # flat prior width1 <- 25 height1 <- 0.02 thetas <- seq(-0.65, 0.3, by = 0.01) bpp_1interim_continuous(prior = "normal", successmean = mdd, stDev = stDev, n1 = c(n1_int, n1), n2 = c(n2_int, n2), IntEffBoundary = effi, IntFutBoundary = futi, IntFix = 1, priormean = priormean, propA = 0.5, thetas, priorsigma = sd0)[] bpp_1interim_continuous(prior = "flat", successmean = mdd, stDev = stDev, n1 = c(n1_int, n1), n2 = c(n2_int, n2), IntEffBoundary = effi, IntFutBoundary = futi, IntFix = 1, priormean = 12.3, propA = 0.5, thetas = thetas, width = width1, height = height1)[]

bpp documentation built on Jan. 13, 2022, 5:09 p.m.