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)