Introduction

Along with the clinical trial simulations feature, the Mediana R package can be used to obtain adjusted p-values and one-sided simultaneous confidence limits.

`AdjustPvalues` function

The AdjustPvalues function can be used to get adjusted p-values for commonly used multiple testing procedures based on univariate p-values (Bonferroni, Holm, Hommel, Hochberg, fixed-sequence and Fallback procedures), commonly used parametric multiple testing procedures (single-step and step-down Dunnett procedures) and multistage gatepeeking procedure.

Description

Inputs

The AdjustPvalues function requires the input of two pre-specified objects defined in the following two arguments:

pval defines the raw p-values.
proc defines the multiple testing procedure. Several procedures are already implemented in the Mediana package (listed below, along with the required or optional parameters to specify in the par argument):
- BonferroniAdj: Bonferroni procedure. Optional parameter: weight.
- HolmAdj: Holm procedure. Optional parameter: weight.
- HochbergAdj: Hochberg procedure. Optional parameter: weight.
- HommelAdj: Hommel procedure. Optional parameter: weight.
- FixedSeqAdj: Fixed-sequence procedure.
- FallbackAdj: Fallback procedure. Required parameters: weight.
- DunnettAdj: Single-step Dunnett procedure. Required parameters: n.
- StepDownDunnettAdj: Step-down Dunnett procedure. Required parameters: n.
- ChainAdj: Family of chain procedures. Required parameters: weight and transition.
- NormalParamAdj: Parametric multiple testing procedure derived from a multivariate normal distribution. Required parameter: corr. Optional parameter: weight.
- ParallelGatekeepingAdj: Family of parallel gatekeeping procedures. Required parameters: family, proc, gamma.
- MultipleSequenceGatekeepingAdj: Family of multiple-sequence gatekeeping procedures. Required parameters: family, proc, gamma.
- MixtureGatekeepingAdj: Family of mixture-based gatekeeping procedures. Required parameters: family, proc, gamma, serial, parallel.
par defines the parameters associated to the multiple testing procedure.

Outputs

The AdjustPvalues function returns a vector of adjusted p-values.

Example

The following example illustrates the use of the AdjustedPvalues function to get adjusted p-values for traditional nonparametric, semi-parametric and parametric procedures, as well as more complex multiple testing procedures.

Traditional nonparametric and semiparametric procedures

For the illustration of adjustedment of raw p-values with the traditional nonparametric and semiparametric procedures, we will consider the following three raw p-values:

rawp = c(0.012, 0.009, 0.023)

These p-values will be adjusted with several multiple testing procedures as specified below:

# Bonferroni, Holm, Hochberg, Hommel and Fixed-sequence procedure
proc = c("BonferroniAdj", "HolmAdj", "HochbergAdj", "HommelAdj", "FixedSeqAdj", "FallbackAdj")

In order to obtain the adjusted p-values for all these procedures, the sapply function can be used as follows. Note that as no weight parameter is defined, the equally weighted procedures are used to adjust the p-values. Finally, for the fixed-sequence procedure (FixedSeqAdj), the order of the testing sequence is based on the order of the p-values in the vector.

# Equally weighted
sapply(proc, function(x) {AdjustPvalues(rawp,
                                        proc = x)})

The output is as follows:

     BonferroniAdj HolmAdj HochbergAdj HommelAdj FixedSeqAdj FallbackAdj
[1,]         0.036   0.027       0.023     0.023       0.012      0.0360
[2,]         0.027   0.027       0.023     0.018       0.012      0.0270
[3,]         0.069   0.027       0.023     0.023       0.023      0.0345

In order to specify unequal weights for the three raw p-values, the weight parameter can be defined as follows. Note that this parameter has no effect on the adjustment with the fixed-sequence procedure.

# Unequally weighted (no effect on the fixed-sequence procedure)
sapply(proc, function(x) {AdjustPvalues(rawp,
                                        proc = x,
                                        par = parameters(weight = c(1/2, 1/4, 1/4)))})

The output is as follows:

     BonferroniAdj HolmAdj HochbergAdj HommelAdj FixedSeqAdj FallbackAdj
[1,]         0.024   0.024       0.018     0.018       0.012       0.024
[2,]         0.036   0.024       0.018     0.018       0.012       0.024
[3,]         0.092   0.024       0.023     0.023       0.023       0.024

Traditional parametric procedures

Consider a clinical trials comparing three doses with a Placebo based on a normally distributed endpoints. Let H1, H2 and H3 be the three null hypotheses of no effect tested in the trial:

H1: No difference between Dose 1 and Placebo
H2: No difference between Dose 2 and Placebo
H3: No difference between Dose 3 and Placebo

The treatment effect estimates, corresponding to the mean dose-placebo difference are specified below, as well as the pooled standard deviation, the sample size, the standard errors and the T-statistics associated with the three dose-placebo tests

# 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

Based on the T-statistics, the raw p-values can be easily obtained:

# One-sided pvalue
rawp = 1-pt(stat,2*(n-1))

The adjusted p-values based on the single step Dunnett and step-down Dunnett procedures are obtained as follows.

# Adjusted p-values based on the Dunnett procedures
# (assuming that each test statistic follows a t distribution)
AdjustPvalues(rawp,proc = "DunnettAdj", par = parameters(n = n))
AdjustPvalues(rawp,proc = "StepDownDunnettAdj", par = parameters(n = n))

The outputs are presented below.

> AdjustPvalues(rawp,proc = "DunnettAdj",par = parameters(n = n))
[1] 0.02887019 0.01722656 0.07213393
> AdjustPvalues(rawp,proc = "StepDownDunnettAdj",par = parameters(n = n))
[1] 0.02043820 0.01722544 0.02909082

Gatekeeping procedures

For illustration, we will consider a clinical trial with two families of null hypotheses. The first family contains the null hypotheses associated with the Endpoints 1 and 2, that are considered as primary endpoints, and the second family the null hypotheses associated with the Endpoints 3 and 4 (key secondary endpoints). The null hypotheses of the secondary family will be tested if and only if at least one null hypothesis from the first family is rejected. Let H1, H2, H3 and H4 be the four null hypotheses of no effect on Endpoint 1, 2, 3 and 4 respectively tested in the trial:

H1: No difference between Drug and Placebo on Endpoint 1 (Family 1)
H2: No difference between Drug and Placebo on Endpoint 2 (Family 1)
H3: No difference between Drug and Placebo on Endpoint 3 (Family 2)
H4: No difference between Drug and Placebo on Endpoint 4 (Family 2)

The raw p-values are specified below:

# One-sided raw p-values (associated respectively with H1, H2, H3 and H4)
rawp<-c(0.0082, 0.0174, 0.0042, 0.0180)

The parameters of the parallel gatekeeping procedure are specified using the three arguments family which specifies the hypotheses included in each family, proc which specifies the component procedure associated with each family and gamma which specifies the truncation parameter of each family.

# Define hypothesis included in each family (index of the raw p-value vector)
family = families(family1 = c(1, 2),
                  family2 = c(3, 4))

# Define component procedure of each family
component.procedure = families(family1 ="HolmAdj",
                               family2 = "HolmAdj")

# Truncation parameter of each family
gamma = families(family1 = 0.5,
                 family2 = 1)

The adjusted p-values are obtained using the AdjustedPvalues function as specified below:

AdjustPvalues(rawp,
                        proc = "ParallelGatekeepingAdj",
                        par = parameters(family = family,
                                         proc = component.procedure,
                                         gamma = gamma))

[1] 0.0164 0.0232 0.0232 0.0232

`AdjustCIs` function

The AdjustCIs function can be used to get simultaneous confidence intervals for selected multiple testing procedures based on univariate p-values (Bonferroni, Holm and fixed-sequence procedures) and commonly used parametric multiple testing procedures (single-step and step-down Dunnett procedures).

Description

Inputs

The AdjustPvalues function requires the input of two pre-specified objects defined in the following two arguments:

est defines the point estimates.
proc defines the multiple testing procedure. Several procedures are already implemented in the Mediana package (listed below, along with the required or optional parameters to specify in the par argument):
- BonferroniAdj: Bonferroni procedure. Required parameters: n, sd and covprob. Optional parameter: weight.
- HolmAdj: Holm procedure. Required parameters: n, sd and covprob. Optional parameter: weight.
- FixedSeqAdj: Fixed-sequence procedure. Required parameters: n, sd and covprob.
- DunnettAdj: Single-step Dunnett procedure. Required parameters: n, sd and covprob.
- StepDownDunnettAdj: Step-down Dunnett procedure. Required parameters: n, sd and covprob.
par defines the parameters associated to the multiple testing procedure.

Outputs

The AdjustCIs function returns a vector lower simultaneous confidence limits.

Example

Consider a clinical trials comparing three doses with a Placebo based on a normally distributed endpoints. Let H1, H2 and H3 be the three null hypotheses of no effect tested in the trial:

H1: No difference between Dose 1 and Placebo
H2: No difference between Dose 2 and Placebo
H3: No difference between Dose 3 and Placebo

The treatment effect estimates, corresponding to the mean dose-placebo difference are specified below, as well as the pooled standard deviation, the sample size.

# Null hypotheses of no treatment effect are equally weighted
weight<-c(1/3,1/3,1/3)

# 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

The one-sided simultaneous confidence limits for several multiple testing procedures are obtained using the AdjustCIs function wrapped in a sapply function.

# Bonferroni, Holm, Hochberg, Hommel and Fixed-sequence procedure
proc = c("BonferroniAdj", "HolmAdj", "FixedSeqAdj", "DunnettAdj", "StepDownDunnettAdj")

# Equally weighted
sapply(proc, function(x) {AdjustCIs(est,
                                    proc = x,
                                    par = parameters(sd = sd,
                                                     n = n,
                                                     covprob = 0.975,
                                                     weight = weight))})

The output obtained is presented below:

     BonferroniAdj     HolmAdj FixedSeqAdj  DunnettAdj StepDownDunnettAdj
[1,]   -0.09730247  0.00000000  0.00000000 -0.05714354         0.00000000
[2,]    0.10269753  0.00000000  0.00000000  0.14285646         0.00000000
[3,]   -0.49730247 -0.06268427 -0.06268427 -0.45714354        -0.06934203

Adjusted p-values and one-sided simultaneous confidence limits

Introduction

AdjustPvalues function

Description

Inputs

Outputs

Example

Traditional nonparametric and semiparametric procedures

Traditional parametric procedures

Gatekeeping procedures

AdjustCIs function

Description

Inputs

Outputs

Example

`AdjustPvalues` function

`AdjustCIs` function