MCPMod {DoseFinding}  R Documentation 
Tests for a doseresponse effect using a modelbased multiple contrast
test (see MCTtest
), selects one (or several) model(s)
from the significant shapes, fits them using fitMod
.
For details on the method see Bretz et al. (2005).
MCPMod(dose, resp, data, models, S = NULL, type = c("normal", "general"),
addCovars = ~1, 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)
## S3 method for class 'MCPMod'
predict(object,
predType = c("fullmodel", "lsmeans", "effectcurve"),
newdata = NULL, doseSeq = NULL, se.fit = FALSE, ...)
## S3 method for class 'MCPMod'
plot(x, CI = FALSE, level = 0.95,
plotData = c("means", "meansCI", "raw", "none"),
plotGrid = TRUE, colMn = 1, colFit = 1, ...)
dose, resp 
Either vectors of equal length specifying dose and response values, or names of variables in the data frame specified in ‘data’. 
data 
Data frame containing the variables referenced in dose and resp if ‘data’ is not specified it is assumed that ‘dose’ and ‘resp’ are variables referenced from data (and no vectors) 
models 
An object of class ‘"Mods"’, see 
S 
The covariance matrix of ‘resp’ when ‘type = "general"’, see Description. 
type 
Determines whether inference is based on an ANCOVA model under a
homoscedastic normality assumption (when ‘type = "normal"’), or
estimates at the doses and their covariance matrix and degrees of
freedom are specified directly in ‘resp’, ‘S’ and
‘df’. See also 
addCovars 
Formula specifying additive linear covariates (for ‘type = "normal"’) 
placAdj 
Logical, if true, it is assumed that placeboadjusted estimates are specified in ‘resp’ (only possible for ‘type = "general"’). 
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 
Specify the degrees of freedom to use in case ‘type = "general"’,
for the call to 
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, Delta, 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. 
object, x 
MCPMod object 
predType, newdata, doseSeq, se.fit, ... 
predType determines whether predictions are returned for the full model (including potential covariates), the lsmeans (SAS type) or the effect curve (difference to placebo). newdata gives the covariates to use in producing the predictions (for ‘predType = "fullmodel"’), if missing the covariates used for fitting are used. doseSeq dosesequence on where to produce predictions (for ‘predType = "effectcurve"’ and ‘predType = "lsmeans"’). If missing the doses used for fitting are used. se.fit: logical determining, whether the standard error should be calculated. ...: Additional arguments, for plot.MCPMod these are passed to plot.DRMod. 
CI, level, plotData, plotGrid, colMn, colFit 
Arguments for plot method: ‘CI’ determines whether confidence intervals should be plotted. ‘level’ determines the level of the confidence intervals. ‘plotData’ determines how the data are plotted: Either as means or as means with CI, raw data or none. In case of ‘type = "normal"’ and covariates the lsmeans are displayed, when ‘type = "general"’ the option "raw" is not available. ‘colMn’ and ‘colFit’ determine the colors of fitted model and the raw means. 
An object of class ‘MCPMod’, which contains the fitted ‘MCTtest’ object as well as the ‘DRMod’ objects and additional information (model selection criteria, dose estimates, selected models).
Bjoern Bornkamp
Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple comparisons and modeling techniques in doseresponse studies, Biometrics, 61, 738–748
Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statistics, 16, 639–656
Pinheiro, J. C., Bretz, F., and Branson, M. (2006). Analysis of doseresponse studies  modeling approaches, in N. Ting (ed.). Dose Finding in Drug Development, Springer, New York, pp. 146–171
Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Modelbased dose finding under model uncertainty using general parametric models, Statistics in Medicine, 33, 1646–1661
Schorning, K., Bornkamp, B., Bretz, F., & Dette, H. (2016). Model selection versus model averaging in dose finding studies. Statistics in Medicine, 35, 4021–4040
Xun, X. and Bretz, F. (2017) The MCPMod methodology: Practical Considerations and The DoseFinding R package, in O'Quigley, J., Iasonos, A. and Bornkamp, B. (eds) Handbook of methods for designing, monitoring, and analyzing dosefinding trials, CRC press
Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997). Model selection an integral part of inference, Biometrics, 53, 603–618
Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley.
data(biom)
## first define candidate model set (only need "standardized" models)
models < Mods(linear = NULL, emax=c(0.05,0.2), linInt=c(1, 1, 1, 1),
doses=c(0,0.05,0.2,0.6,1))
## perform MCPMod procedure
MM < MCPMod(dose, resp, biom, models, Delta=0.5)
## a number of things can be done with an MCPMod object
MM # print method provides basic information
summary(MM) # more information
## predict all significant doseresponse models
predict(MM, se.fit=TRUE, doseSeq=c(0,0.2,0.4, 0.9, 1),
predType="lsmeans")
## display all model functions
plot(MM, plotData="meansCI", CI=TRUE)
## now perform modelaveraging
MM2 < MCPMod(dose, resp, biom, models, Delta=0.5, selModel = "aveAIC")
sq < seq(0,1,length=11)
pred < predict(MM, doseSeq=sq, predType="lsmeans")
modWeights < MM2$selMod
## model averaged predictions
pred < do.call("cbind", pred)%*%modWeights
## model averaged doseestimate
TDEst < MM2$doseEst%*%modWeights
## now an example using a general fit and fitting based on placebo
## adjusted firststage estimates
data(IBScovars)
## ANCOVA fit model including covariates
anovaMod < lm(resp~factor(dose)+gender, data=IBScovars)
drFit < coef(anovaMod)[2:5] # placebo adjusted estimates at doses
vCov < vcov(anovaMod)[2:5,2:5]
dose < sort(unique(IBScovars$dose))[1] # no estimate for placebo
## candidate models
models < Mods(emax = c(0.5, 1), betaMod=c(1,1), doses=c(0,4))
## hand over placeboadjusted estimates drFit to MCPMod
MM3 < MCPMod(dose, drFit, S=vCov, models = models, type = "general",
placAdj = TRUE, Delta=0.2)
plot(MM3, plotData="meansCI")
## The first example, but with critical value handed over
## this is useful, e.g. in simulation studies
MM4 < MCPMod(dose, resp, biom, models, Delta=0.5, critV = 2.31)