Description Usage Arguments Details Value Source References See Also Examples

Computation of adjusted *p*-values for commonly used parametric
multiple testing procedures (single-step and step-down Dunnett procedures).

1 | ```
paradjp(stat,n,proc)
``` |

`stat` |
Vector of test statistics. |

`n` |
Common sample size in each treatment group. |

`proc` |
Vector of character strings containing the procedure name.
This vector should include any of the following: |

This function computes adjusted *p*-values for the single-step Dunnett procedure
(Dunnett, 1955) and step-down Dunnett procedure (Naik, 1975; Marcus, Peritz
and Gabriel, 1976) in one-sided hypothesis testing problems with a balanced
one-way layout and equally weighted null hypotheses. For more information on the
algorithms used in the function, see Dmitrienko et al. (2009, Section 2.7).

A list with the following components:

`proc` |
Name of procedure used. |

`result` |
A data frame with columns for the test statistics,
one-sided raw |

http://multxpert.com/wiki/MultXpert_package

Dmitrienko, A., Bretz, F., Westfall, P.H., Troendle, J., Wiens, B.L.,
Tamhane, A.C., Hsu, J.C. (2009). Multiple testing methodology.
*Multiple Testing Problems in Pharmaceutical Statistics*.
Dmitrienko, A., Tamhane, A.C., Bretz, F. (editors). Chapman and
Hall/CRC Press, New York.

Dunnett, C.W. (1955). A multiple comparison procedure for
comparing several treatments with a control. *Journal of the American
Statistical Association*. 50, 1096–1121.

Marcus, R. Peritz, E., Gabriel, K.R. (1976). On closed testing
procedures with special reference to ordered analysis of variance.
*Biometrika*. 63, 655–660.

Naik, U.D. (1975). Some selection rules for comparing *p* processes
with a standard. *Communications in Statistics. Series A*.
4, 519–535.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
# Consider a clinical trial conducted to evaluate the effect of three
# doses of a treatment compared to a placebo with respect to a normally
# distributed endpoint
# Three null hypotheses of no effect are tested in the trial:
# Null hypothesis H1: No difference between Dose 1 and Placebo
# Null hypothesis H2: No difference between Dose 2 and Placebo
# Null hypothesis H3: No difference between Dose 3 and Placebo
# Treatment effect estimates (mean dose-placebo differences)
est<-c(2.3,2.5,1.9)
# Pooled standard deviation
sd<-9.5
# Study design is balanced with 180 patients per treatment arm
n<-180
# Standard errors
stderror<-rep(sd*sqrt(2/n),3)
# T-statistics associated with the three dose-placebo tests
stat<-est/stderror
# Compute one-sided adjusted p-values for the single-step Dunnett procedure
paradjp(stat, n, proc="Single-step Dunnett")
# Compute one-sided adjusted p-values for the single-step and
# step-down Dunnett procedures
paradjp(stat, n, proc=c("Single-step Dunnett", "Step-down Dunnett"))
``` |

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