R: Calculate guesstimates based on prior knowledge
guesst
R Documentation
Calculate guesstimates based on prior knowledge
Description
Calculates guesstimates for standardized model parameter(s)
using the general approach described in Pinheiro et al. (2006).
Usage
guesst(d, p, model = c("emax", "exponential", "logistic", "quadratic",
"betaMod", "sigEmax"), less = TRUE, local = FALSE,
dMax, Maxd, scal)
Arguments
d
Vector containing dose value(s).
p
Vector of expected percentages of the maximum effect achieved at d.
model
Character string. Should be one of "emax", "exponential",
"quadratic", "betaMod", "sigEmax".
less
Logical, only needed in case of quadratic model.
Determines if d is smaller (less=TRUE) or larger (less=FALSE)
than dopt (see Pinheiro et al. (2006) for details).
local
Logical indicating whether local or asymptotic version
of guesstimate should be derived (defaults to FALSE).
Only needed for emax, logistic and sigEmax model.
When local=TRUE the maximum dose must be provided via
Maxd.
dMax
Dose at which maximum effect occurs, only needed for the beta model
Maxd
Maximum dose to be administered in the trial
scal
Scale parameter, only needed for the beta model
Details
Calculates guesstimates for the parameters of the standardized
model function based on the prior expected percentage of the maximum effect at
certain dose levels. Note that this function should be used together with the plotModels
function to ensure that the guesstimates are reflecting the prior
beliefs.
For the logistic and sigmoid emax models at least two pairs (d,p) need to
be specified.
For the beta model the dose at which the maximum effect occurs (dMax)
has to be specified in addition to the (d,p) pair.
For the exponential model the maximum dose administered (Maxd) needs to
be specified in addition to the (d,p) pair.
For the quadratic model one (d,p) pair is needed. It is advisable to
specify the location of the maximum within the dose range with this
pair.
For the emax, sigmoid Emax and logistic model one can choose between a local
and an asymptotic version. In the local version one explicitly forces the
standardized model function to pass through the specified points (d,p). For the
asymptotic version it assumed that the standardized model function is equal to 1
at the largest dose (this is the approach described in Pinheiro et al. (2006)).
If the local version is used, convergence problems
with the underlying nonlinear optimization can occur.
Value
Returns a numeric vector containing the guesstimates.
References
Bornkamp B., Pinheiro J. C., and Bretz, F. (2009). MCPMod: An
R Package for the Design and Analysis of Dose-Finding
Studies, Journal of Statistical Software, 29(7), 1–23
Pinheiro, J. C., Bretz, F., and Branson, M. (2006). Analysis of dose-response studies - modeling
approaches, in N. Ting (ed.), Dose Finding in Drug Development, Springer, New York,
pp. 146–171