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 μ 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.
The main functions are:
prepareGen: Calculates the μ vector and S matrix to be supplied to regular
MCPMod functions (e.g.
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.
Maintainer: Ian Laga <email@example.com>
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
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# 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)
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