paradjp {multxpert}

Common parametric procedures: Adjusted p-values

Description

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

Usage

paradjp(stat,n,proc)

Arguments

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: "Single-step Dunnett", "Step-down Dunnett".

Details

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).

Value

A list with the following components:

proc

Name of procedure used.

result

A data frame with columns for the test statistics, one-sided raw p-values, and one-sided adjusted p-values for the specified procedure.

Source

http://multxpert.com/wiki/MultXpert_package

References

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.

See Also

pvaladjp

Examples

# 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"))