Description Usage Arguments Details Value
This function allows the user to implement the MCPMod function on a Cox
proportional hazards regression model and a parametric survival model. The
function works very similarly to
MCPModGen
, but is unique enough in
terms of the data and the parameters to warrant its own function.
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  MCPModSurv(
model = c("coxph", "parametric"),
dist = NULL,
returnS = FALSE,
dose,
resp,
status,
data = NULL,
models,
placAdj = FALSE,
selModel = c("AIC", "maxT", "aveAIC"),
alpha = 0.025,
df = NULL,
critV = NULL,
doseType = c("TD", "ED"),
Delta,
p,
pVal = TRUE,
alternative = c("one.sided", "two.sided"),
na.action = na.fail,
mvtcontrol = mvtnorm.control(),
bnds,
control = NULL,
...
)

model 
A character string containing the survival regression model. 
dist 
A character string for the distribution, in the case when

returnS 
Logical determining whether muHat and SHat should be returned, in additional to the MCPMod output. 
dose, resp, status 
Either character strings specifying the names of the
respective columns in the 
data 
Data frame with names specified in 'dose', 'resp', and optionally 'w'. If data is not specified, it is assumed that 'dose' and 'resp' are numerical vectors 
models 
An object of class "Mods", see 
placAdj 
Logical specifying whether the provided by 'resp' are to be treated as placeboadjusted estimates. 
selModel 
Optional character vector specifying the model selection criterion for dose estimation. Possible values are
For type = "general" the "gAIC" is used. 
alpha 
Significance level for the multiple contrast test 
df 
An optional numeric value specifying the degrees of freedom. Infinite degrees of freedom ('df=Inf', the default), correspond to the multivariate normal distribution. 
critV 
Supply a precalculated critical value. If this argument is NULL, no critical value will be calculated and the test decision is based on the pvalues. If critV = TRUE the critical value will be calculated. 
doseType 
doseType determines the dose to estimate, ED or TD (see also

Delta 
doseType determines the dose to estimate, ED or TD (see also

p 
doseType determines the dose to estimate, ED or TD (see also

pVal 
Logical determining, whether pvalues should be calculated. 
alternative 
Character determining the alternative for the multiple contrast trend test. 
na.action 
A function which indicates what should happen when the data contain NAs. 
mvtcontrol 
A list specifying additional control parameters for the qmvt
and pmvt calls in the code, see also 
bnds 
Bounds for nonlinear parameters. This needs to be a list with list
entries corresponding to the selected bounds. The names of the list
entries need to correspond to the model names. The

control 
Control list for the optimization. The entry nlminbcontrol needs to be a list and is passed directly to control argument in the nlminb function, that is used internally for models with 2 nonlinear parameters (e.g. sigmoid Emax or beta model). The entry optimizetol is passed directly to the tol argument of the optimize function, which is used for models with 1 nonlinear parameters (e.g. Emax or exponential model). The entry gridSize needs to be a list with entries dim1 and dim2 giving the size of the grid for the gridsearch in 1d or 2d models. 
... 
Additional arguments to be passed to 
'MCPModSurv' works by making calls to 'coxph', 'survreg', and 'Surv' from the 'survival' package. After retrieving coefficient estimates and the estimated covariance matrix, values are passed into the 'MCPMod' function from the 'DoseFinding' package.
An object of class MCPMod if returnS = FALSE. Otherwise, a list containing an object of class MCPMod, the numeric vector μ, and the numeric matrix S.
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