pargateadjp {multxpert}

Multistage parallel gatekeeping procedures: Adjusted p-values

Description

Computation of adjusted p-values for multistage parallel gatekeeping procedures.

Usage

 pargateadjp(gateproc, independence, alpha, printDecisionRules)

Arguments

gateproc

List of gatekeeping procedure parameters in each family of null hypotheses, including the family label, vector of raw p-values, procedure name and procedure parameter (pargateadjp function supports truncated and regular versions of the Bonferroni, Holm, Hommel, Hochberg and fallback procedures).

independence

Boolean indicator (TRUE, Independence condition is imposed (i.e., inferences in earlier families are independent of inferences in later families); FALSE, Independence condition is not imposed).

alpha

Global family-wise error rate (default is 0.05). Note that this argument is not needed if the function is called to compute adjusted p-values, i.e., if printDecisionRules=FALSE.

printDecisionRules

Boolean indicator for printing the decision rules for the gatekeeping procedure (default is FALSE).

Details

This function computes adjusted p-values and generates decision rules for multistage parallel gatekeeping procedures in hypothesis testing problems with multiple families of null hypotheses (null hypotheses are assumed to be equally weighted within each family) based on the methodology presented in Dmitrienko, Tamhane and Wiens (2008) and Dmitrienko, Kordzakhia and Tamhane (2011). For more information on parallel gatekeeping procedures (computation of adjusted p-values, independence condition, etc), see Dmitrienko and Tamhane (2009, Section 5.4).

Value

A data frame result with columns for the family labels, procedures, procedure parameters (truncation parameters), raw p-values, and adjusted p-values.

Source

http://multxpert.com/wiki/MultXpert_package

References

Dmitrienko, A., Tamhane, A., Wiens, B. (2008). General multistage gatekeeping procedures. Biometrical Journal. 50, 667–677.

Dmitrienko, A., Tamhane, A.C. (2009). Gatekeeping procedures in clinical trials. Multiple Testing Problems in Pharmaceutical Statistics. Dmitrienko, A., Tamhane, A.C., Bretz, F. (editors). Chapman and Hall/CRC Press, New York.

Dmitrienko, A., Kordzakhia, G., Tamhane, A.C. (2011). Multistage and mixture parallel gatekeeping procedures in clinical trials. Journal of Biopharmaceutical Statistics. To appear.

Examples

# Consider a clinical trial with two families of null hypotheses

# Family 1: Primary null hypotheses (one-sided p-values)
# H1 (Endpoint 1), p1=0.0082
# H2 (Endpoint 2), p2=0.0174

# Family 2: Secondary null hypotheses (one-sided p-values)
# H3 (Endpoint 3), p3=0.0042
# H4 (Endpoint 4), p4=0.0180

# Define family label and raw p-values in Family 1
label1<-"Primary endpoints"
rawp1<-c(0.0082,0.0174)

# Define family label and raw p-values in Family 2
label2<-"Secondary endpoints"
rawp2<-c(0.0042,0.0180)

# Independence condition is imposed (Families 1 and 2 are tested
# sequentually from first to last and thus adjusted p-values 
# in Family 1 do not depend on inferences in Family 2)
independence<-TRUE

# Define a two-stage parallel gatekeeping procedure which
# utilizes the truncated Holm procedure in Family 1 (truncation
# parameter=0.5) and regular Holm procedure in Family 2 (truncation
# parameter=1)

# Create a list of gatekeeping procedure parameters
family1<-list(label=label1, rawp=rawp1, proc="Holm", procpar=0.5)
family2<-list(label=label2, rawp=rawp2, proc="Holm", procpar=1)
gateproc<-list(family1,family2)

# Compute adjusted p-values
pargateadjp(gateproc, independence)

# Generate decision rules using a one-sided alpha=0.025
pargateadjp(gateproc, independence, alpha=0.025, printDecisionRules=TRUE)