Description Usage Arguments Details Value Author(s) References See Also
Inverse ("dose-finding") point estimation of a dose (x) for a specified target y value (e.g., a response rate), using a user-specified forward-estimation algorithm (default is CIR).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
y |
can be either of the following: y values (response rates), a 2-column matrix with positive/negative response counts by dose, a |
x |
dose levels (if not included in y). |
wt |
weights (if not included in y). |
estfun |
the name of the dose-response estimation function. Default |
target |
A vector of target response rate(s), for which the percentile dose estimate is needed. See Note. |
full |
logical, is a more complete output desired (relevant only for doseFind)? if |
dec |
(relevant only for doseFind) logical, is the true function is assumed to be monotone decreasing? Default |
extrapolate |
logical: should extrapolation beyond the range of estimated y values be allowed? Default |
errOnFlat |
logical: in case the forward estimate is completely flat making dose-finding infeasible, should an error be returned? Under default ( |
adaptiveShrink |
logical, should the y-values be pre-shrunk towards an experiment's target? Recommended if data were obtained via an adaptive dose-finding design. See |
starget |
The shrinkage target. Defaults to |
... |
Other arguments passed on to |
The function works by calling estfun
for forward estimation of the x-y relationship, then using approx
with the x and y roles reversed for inverse estimation. The extrapolate
option sets the rule
argumet for this second call:
extrapolate=TRUE
translates to rule=2
, which actually means that the x value on the edge of the estimated y range will be assigned.
extrapolate=FALSE
(default) translates to rule=1
, which means an NA
will be returned for any target y value lying outside the estimated y range.
Note also that the function is set up to work with a vector of targets.
If the data were obtained from an adaptive dose-finding design and you seek to estimate a dose other than the experiment's target, note that away from the target the estimates are likely biased (Flournoy and Oron, 2019). Use adaptiveShrink=TRUE
to mitigate the bias. In addition, either provide the true target as starget
, or a vector of values to target
, with the first value being the true target.
under default, returns point estimate(s) of the dose (x) for the provided target rate(s). With full=TRUE
, returns a list with
targest The said point estimate of x
input a doseResponse
object summarizing the input data
output a doseResponse
object with the forward estimate at design points
shrinkage a doseResponse
object which is the alg
output of the forward-estimation function
Assaf P. Oron <assaf.oron.at.gmail.com>
Flournoy N and Oron AP, 2020. Bias Induced by Adaptive Dose-Finding Designs. Journal of Applied Statistics 47, 2431-2442.
oldPAVA
,cirPAVA
. If you'd like point and interval estimates together, use quickInverse
.
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