MCPModGeneral-package {MCPModGeneral}

A Supplement to the DoseFinding Package for the General Case

Description

Analyzes non-normal data via the Multiple Comparison Procedures and Modeling approach ('MCP-Mod'). Many functions rely on the 'DoseFinding' package. This package makes it so the user does not need to prespecify or calculate the \mu vector and S matrix. Instead, the user typically supplies the data in its raw form, and this package will calculate the needed objects and pass them into the ‘DoseFinding' functions. If the user wishes to primarily use the functions provided in the 'DoseFinding' package, a singular function ('prepareGen') will provide mu and S. The package currently handles power analysis and the ’MCP-Mod' procedure for negative binomial, Poisson, and binomial data. The 'MCP-Mod' procedure can also be applied to survival data, but power analysis is not available.

Details

Package:	MCPModGeneral
Type:	Package
Version:	0.1-1
Date:	2020-2-9
License:	GPL-3

The main functions are:
prepareGen: Calculates the \mu vector and S matrix to be supplied to regular MCPMod functions (e.g. MCPMod, MCTtest, planMod)
MCPModGen: Implements the full MCPMod procedure for raw negative binomial and binary data.
planModPrepare: Calculate the theoretical covariance matrix S.
powMCTGen: Calculates the power of the multiple contrast test.
sampSizeMCTGen: Calculates the sample size needed to reach the target power.

The secondary functions are:
MCPModSurv: Implements the full MCPMod procedure for basic survival data. This includes a Cox-PH model and parametric survival regression models. Power analysis is not available for the survival data.
simS: A simulation based method for estimating the theoretical covariance matrices.

Author(s)

Ian Laga

Maintainer: Ian Laga <[e-mail]>

References

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

Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997). Model selection an integral part of inference, Biometrics, 53, 603–618

Examples

  # Analyze the binary migraine data from the DoseFinding package.
  data(migraine)
  models = Mods(linear = NULL, emax = 1, quadratic = c(-0.004),
                doses = migraine$dose)

  powMCTGen(migraine$ntrt, "binomial", "logit",
            Ntype = "actual", altModels = models)
  sampSizeMCTGen("binomial", "logit", altModels = models, power = 0.8,
                 Ntype = "arm", upperN = 30, verbose = TRUE)

  # Now analyze using binomial weights
  PFrate <- migraine$painfree/migraine$ntrt
  migraine$pfrat = migraine$painfree / migraine$ntrt
  MCPModGen("binomial","logit",returnS = TRUE, w = "ntrt", dose = "dose",
     resp = "pfrat", data = migraine, models = models, selModel = "aveAIC",
     Delta = 0.2)