krow: Up-and-Down Design Rules for use in Dose-Finding Simulator
In upndown: Utilities and Design Aids for Up-and-Down Dose-Finding Studies

View source: R/simulate.r

krow	R Documentation

Up-and-Down Design Rules for use in Dose-Finding Simulator

Description

Rules for k-in-a-row, Biased-Coin UD, and Group UD, coded as functions compatible with the generic dose-finding simulator dfsim()

These functions work on each virtual experimental run individually.

Usage

krow(doses, responses, k, lowTarget = NULL, fastStart = FALSE, ...)

bcd(doses, responses, coin, lowTarget, fastStart = FALSE, ...)

groupUD(doses, responses, s, ll, ul, ...)

Arguments

`doses`, `responses`	(mandatory arguments) vectors of the run's current sequence of doses (in ordinal/index scale) and responses
`k`	the number of consecutive identical responses required for dose transitions (k-in-a-row functions only).
`lowTarget`	(`krow` and `bcd`) logical: is the target below 0.5 (median threshold)?
`fastStart`	(`krow` and `bcd`) logical: should the experiment begin with a classical-UD-like stage until the first "minority" response is observed (i.e., a 1 for below-median targets and vice versa)? Even though `TRUE` delivers better experimental performance and is recommended when allowed, default is `FALSE` because toxicity/safety studies are unlikely to allow it.
`...`	Technical pass-through argument, to allow for flexibility when constructing design-comparison simulation ensembles.
`coin`	(`bcd` only) the biased-coin probability. Note that unlike `bcdmat()`, here the function does not figure out automatically the coin probability and upper/lower target location from the provided target.
`s`	(`groupUD` only) the group/cohort size, analogous to `cohort` in `gudmat()`. We use a different name here because `cohort` is already used in `dfsim` that calls these utilities.
`ll`, `ul`	(`groupUD` only) how many positive responses are allowed for a move upward, and how many are required for a move downward, respectively. Analogous to `⁠lower, upper⁠` in `gudmat()`. For example `⁠s=3, ll=0, ul=2⁠` evaluates groups of 3 observations at a time, moves up if none are positive, down if `>=2` are positive, and repeats the same dose with 1 positive.