MCPMod-package: Design and Analysis of Dose-Finding Studies

In MCPMod: Design and Analysis of Dose-Finding Studies

Description

This package implements a methodology for dose-response analysis that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, (2005)). The package provides tools for the analysis of dose finding trials as well as a variety of tools necessary to plan a trial to be conducted with the MCPMod methodology. **Note: The MCPMod package will not be further developed, all future development of the MCP-Mod methodology will be done in the DoseFinding R-package, which already contains an extended version of MCP-Mod, and additional functions useful for planning and analysing dose-finding trials.**

Details

Package:	MCPMod
Type:	Package
Version:	1.0-9
Date:	2016-11-24
License:	GPL-3

Author(s)

Bjoern Bornkamp, Jose Pinheiro and Frank Bretz

Maintainer: Bjoern Bornkamp <[e-mail]>

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

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

Examples

# detailed information regarding MCP-Mod methodology
# and R-package available via vignette("MCPMod")
## Not run: 
# planning a trial for MCPMod
doses <- c(0,10,25,50,100,150)                                             
models <- list(linear = NULL, emax = c(25),                                
               logistic = c(50, 10.88111), exponential = c(85),            
               betaMod = matrix(c(0.33, 2.31, 1.39, 1.39), byrow=TRUE,nrow=2))
plotModels(models, doses, base = 0, maxEff = 0.4, scal = 200) 
sSize <- sampSize(models, doses, base = 0, maxEff = 0.4, sigma = 1,             
           upperN = 80, scal = 200, alpha = 0.05)
sSize
plM <- planMM(models, doses, n = rep(sSize$samp.size,6), scal=200, alpha = 0.05)
plM
plot(plM)

# analysing a trial
data(biom)
models <- list(linear = NULL, linlog = NULL, emax = 0.2,
            exponential = c(0.279,0.15), quadratic = c(-0.854,-1))

dfe <- MCPMod(biom, models, alpha = 0.05, dePar = 0.05, pVal = TRUE,
           selModel = "maxT", doseEst = "MED2", clinRel = 0.4, off = 1)
# detailed information is available via summary
summary(dfe)
# plots data with selected model function
plot(dfe, complData = TRUE, cR = TRUE)

## End(Not run)

Example output

Loading required package: mvtnorm
Loading required package: lattice
MCPMod sampSize

Input parameters:
 Summary Function: mean 
 Desired combined power value: 0.8 
 Level of significance: 0.05 (one-sided) 
 Allocations: balanced 

Sample size per group: 62 

Associated mean power: 0.8067 
Power under models: 
     linear        emax    logistic exponential    betaMod1    betaMod2 
     0.7910      0.7736      0.9163      0.7639      0.8266      0.7689 

MCPMod planMM

Optimal Contrasts:
    linear   emax logistic exponential betaMod1 betaMod2
0   -0.428 -0.706   -0.406      -0.332   -0.566   -0.533
10  -0.351 -0.317   -0.392      -0.302    0.352   -0.418
25  -0.236 -0.025   -0.329      -0.250    0.461   -0.166
50  -0.045  0.202    0.061      -0.141    0.338    0.245
100  0.339  0.384    0.529       0.203   -0.121    0.627
150  0.722  0.461    0.538       0.822   -0.463    0.245

Critical Value (alpha = 0.05, one-sided): 2.153 

Contrast Correlation Matrix:
            linear  emax logistic exponential betaMod1 betaMod2
linear       1.000 0.873    0.954       0.975   -0.381    0.792
emax         0.873 1.000    0.883       0.764    0.085    0.916
logistic     0.954 0.883    1.000       0.876   -0.352    0.914
exponential  0.975 0.764    0.876       1.000   -0.487    0.638
betaMod1    -0.381 0.085   -0.352      -0.487    1.000   -0.028
betaMod2     0.792 0.916    0.914       0.638   -0.028    1.000

MCPMod

Input parameters: 
 alpha = 0.05 (one-sided) 
 model selection: maxT 
 clinical relevance = 0.4 
 dose estimator: MED2 (gamma = 0.05) 

Optimal Contrasts:
     linear linlog   emax exponential1 exponential2 quadratic1 quadratic2
0    -0.437 -0.473 -0.643       -0.292       -0.244     -0.574     -0.420
0.05 -0.378 -0.390 -0.361       -0.286       -0.243     -0.364     -0.197
0.2  -0.201 -0.164  0.061       -0.257       -0.240      0.156      0.331
0.6   0.271  0.324  0.413       -0.039       -0.166      0.714      0.706
1     0.743  0.702  0.530        0.875        0.892      0.068     -0.420

Contrast Correlation:
             linear linlog  emax exponential1 exponential2 quadratic1
linear        1.000  0.996 0.912        0.927        0.865      0.601
linlog        0.996  1.000 0.941        0.893        0.822      0.667
emax          0.912  0.941 1.000        0.723        0.635      0.841
exponential1  0.927  0.893 0.723        1.000        0.990      0.263
exponential2  0.865  0.822 0.635        0.990        1.000      0.134
quadratic1    0.601  0.667 0.841        0.263        0.134      1.000
quadratic2    0.071  0.155 0.431       -0.301       -0.421      0.840
             quadratic2
linear            0.071
linlog            0.155
emax              0.431
exponential1     -0.301
exponential2     -0.421
quadratic1        0.840
quadratic2        1.000

Multiple Contrast Test:
             Tvalue pValue
emax          3.464  0.001
linlog        3.109  0.004
quadratic1    3.100  0.004
linear        2.972  0.006
exponential1  2.217  0.043
exponential2  1.898  0.085
quadratic2    1.850  0.094

Critical value: 2.159

Selected for dose estimation:
  emax 

Parameter estimates: 
emax model:
   e0  eMax  ed50 
0.322 0.746 0.142 

Dose estimate 
MED2,90% 
    0.17 

MCPMod documentation built on March 26, 2020, 7:28 p.m.