# AnalyseCTP: Analysing a Closed Testing Procedure In CTP: Closed Testing Procedure (CTP)

## Description

Calculation of p-values of a closed testing procedure (CTP). The function returns an object of `oldClass "ctp"; summary()` and `Display()` can be applied.

## Usage

 `1` ```AnalyseCTP(ctp.struc, model, data, factor.name = NULL, test.name = "F", ...) ```

## Arguments

 `ctp.struc` Object generated by the function `IntersectHypotheses` (structure of CTP) `model` model of the form response~treatment. If `testname="F"`, the model can be extended by covariates and other factors. In the case of a Logrank test the response must be imputed as `Surv(time,status)`. `data` Dataframe, missing values in the response or treatment variable are not allowed! `factor.name` Character string naming the factor whose levels are compared (treatment factor). By default the first variable of the right-hand side of the model formula is used. `test.name` One of the following strings `"F"` - F-Test (ANOVA from linear model, default) `"glm"` - generalised linear model `"kruskal"` -Kruskal-Wallis-Test `"chisq"` - Chi square test `"prob"` - Fisher's exact test for total number of observations <200 else Chi square test `"lgrank"` - Logrank-test `"jonckheere"` - Jonckheere-Terpstra test of ordered alternatives `"glm"` - generalised linear model, using function `glm` from `stats`. `...` Additional arguments for the chosen test.

## Details

The hypothesis tree of the closed testing procedure must be created using `IntersectHypotheses`. For more details on the theory and the implementation as well for many examples, see the vignettes.

## Value

An object of old class(`ctp`), consisting of a list with:

• `CTPparms`: List with objects describing the CTP setup.

• `pvalues`: Dataframe with all tested hypotheses, raw and adjusted p-values.

## Note

This procedure is constructed for testing differences and two-sided hypotheses, but not for equivalence tests. It is further based on independent samples from the population involved (i.e. on parallel group designs, but not on cross-over designs).

`IntersectHypotheses`, `Display`, `summary.ctp.str`, `summary.ctp`, `Adjust_raw`
 ```1 2 3 4 5 6``` ``` data(pasi) three.to.first <- IntersectHypotheses(list(1:2,c(1,3),c(1,4))) Display(three.to.first) pasi.ctp.F1 <- AnalyseCTP(three.to.first,pasi.ch~dose,pasi) summary(pasi.ctp.F1) Display(pasi.ctp.F1) ```