R: MCPMod - Multiple Comparisons and Modeling

MCPMod {DoseFinding}

R Documentation

MCPMod - Multiple Comparisons and Modeling

Description

Tests for a dose-response effect using a model-based 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).

Usage

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("full-model", "ls-means", "effect-curve"),
        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, ...)

Arguments

`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 `Mods` for details
`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 `fitMod` and Pinheiro et al. (2014).
`addCovars`	Formula specifying additive linear covariates (for ‘⁠type = "normal"⁠’)
`placAdj`	Logical, if true, it is assumed that placebo-adjusted 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 `AIC`: Selects model with smallest AIC (this is the default) `maxT`: Selects the model corresponding to the largest t-statistic. `aveAIC`: Uses a weighted average of the models corresponding to the significant contrasts. The model weights are chosen by the formula: `w_i = \exp(-0.5AIC_i)/\sum_i(\exp(-0.5AIC_i))` See Buckland et al. (1997) for details. 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 `MCTtest` and `fitMod`. Infinite degrees of (‘⁠df=Inf⁠’) correspond to the multivariate normal distribution. For type = "normal" the degrees of freedom deduced from the AN(C)OVA fit are used and this argument is ignored.
`critV`	Supply a pre-calculated critical value. If this argument is NULL, no critical value will be calculated and the test decision is based on the p-values. If ‘⁠critV = TRUE⁠’ the critical value will be calculated.
`doseType`, `Delta`, `p`	‘⁠doseType⁠’ determines the dose to estimate, ED or TD (see also `Mods`), and ‘⁠Delta⁠’ and ‘⁠p⁠’ need to be specified depending on whether TD or ED is to be estimated. See `TD` and `ED` for details.
`pVal`	Logical determining, whether p-values 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 `mvtnorm.control` for details.
`bnds`	Bounds for non-linear 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 `defBnds` function provides the default selection.
`control`	Control list for the optimization. A list with entries: "nlminbcontrol", "optimizetol" and "gridSize". 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 ls-means (SAS type) or the effect curve (difference to placebo). newdata gives the covariates to use in producing the predictions (for ‘⁠predType = "full-model"⁠’), if missing the covariates used for fitting are used. doseSeq dose-sequence on where to produce predictions (for ‘⁠predType = "effect-curve"⁠’ and ‘⁠predType = "ls-means"⁠’). 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 ls-means are displayed, when ‘⁠type = "general"⁠’ the option "raw" is not available. ‘⁠colMn⁠’ and ‘⁠colFit⁠’ determine the colors of fitted model and the raw means.

Value

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).

Author(s)

Bjoern Bornkamp

References

Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple comparisons and modeling techniques in dose-response 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 dose-response 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) Model-based 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 MCP-Mod 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 dose-finding 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.

Examples

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))
plot(models)
## 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 dose-response models
predict(MM, se.fit=TRUE, doseSeq=c(0,0.2,0.4, 0.9, 1),
        predType="ls-means")
## display all model functions 
plot(MM, plotData="meansCI", CI=TRUE)

## now perform model-averaging
MM2 <- MCPMod(dose, resp, biom, models, Delta=0.5, selModel = "aveAIC")
sq <- seq(0,1,length=11)
pred <- predict(MM, doseSeq=sq, predType="ls-means")
modWeights <- MM2$selMod
## model averaged predictions
pred <- do.call("cbind", pred)%*%modWeights
## model averaged dose-estimate
TDEst <- MM2$doseEst%*%modWeights

## now an example using a general fit and fitting based on placebo
## adjusted first-stage 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))
plot(models)
## hand over placebo-adjusted 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)

[Package DoseFinding version 1.1-1 Index]