Description Usage Arguments Author(s) References See Also Examples

Compute the least informative prior variance for the adaptive prior based on the assumption that every dose has the same probability to be the maximum tolerated dose (MTD), i.e. uniform distribution.

1 |

`wm` |
The selected working model; for example the skeleton of toxicity; must be a vector. |

`meanbeta` |
The mean value of variable beta. |

`a` |
The variable a; defaults to NULL. |

`model` |
A valid model; for example the "power_log" model. |

`tau` |
The target of toxicity. |

Artemis Toumazi artemis.toumazi@gmail.com Caroline Petit caroline.petit@crc.jussieu.fr Sarah Zohar sarah.zohar@inserm.fr

Petit, C., et al, (2016) Unified approach for extrapolation and bridging of adult information in early phase dose-finding paediatric studies, Statistical Methods in Medical Research, <doi:10.1177/0962280216671348>.

Zhang J., Braun T., and J. Taylor. Adaptive prior variance calibration in the bayesian continual reassessment method. Stat. Med., 32:2221-34, 2013.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
targetTox <- 0.25 # target of toxicity
####### Skeleton ###########
skeleton_tox1 <- c(0.10, 0.21, 0.33, 0.55, 0.76)
skeleton_tox2 <- c(0.21, 0.33, 0.55, 0.76, 0.88)
skeleton_tox3 <- c(0.05, 0.10, 0.21, 0.33, 0.55)
skeleton_tox4 <- c(0.025, 0.05, 0.1, 0.21, 0.33)
skeleton_tox5 <- c(0.0125, 0.025, 0.05, 0.1, 0.21)
skeletonTox <- data.frame(skeleton_tox1, skeleton_tox2, skeleton_tox3,
skeleton_tox4, skeleton_tox5)
mu <- -0.34
sigmaLI <- sigmaLI(skeletonTox[ ,1], mu, a = NULL, "power_log", targetTox)
``` |

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