R/AdjustPvalues.R
In gpaux/Mediana: Clinical Trial Simulations
Defines functions
AdjustPvalues
Documented in
AdjustPvalues
#' AdjustPvalues function
#'
#' Computation of 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.
#'
#' This function can be used to adjust p-values according to a multiple testing
#' procedure defines in the \code{proc} argument.
#'
#' This function computes adjusted p-values and generates decision rules for
#' the Bonferroni, Holm (Holm, 1979), Hommel (Hommel, 1988), Hochberg
#' (Hochberg, 1988), fixed-sequence (Westfall and Krishen, 2001) and Fallback
#' (Wiens, 2003; Wiens and Dmitrienko, 2005) procedures. The adjusted p-values
#' are computed using the closure principle (Marcus, Peritz and Gabriel, 1976)
#' in general hypothesis testing problems (equally or unequally weighted null
#' hypotheses). For more information on the algorithms used in the function,
#' see Dmitrienko et al. (2009, Section 2.6).
#'
#' 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 the
#' Dunnett procedures, it is assumed that the test statistics follow a t
#' distribution. For more information on the algorithms used in the function,
#' see Dmitrienko et al. (2009, Section 2.7).
#'
#' 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), Dmitrienko, Kordzakhia and Tamhane
#' (2011) and Dmitrienko, Kordzakhia and Brechenmacher (2016). For more
#' information on parallel gatekeeping procedures (computation of adjusted
#' p-values, independence condition, etc), see Dmitrienko and Tamhane (2009,
#' Section 5.4).
#'
#' @param pval defines the raw p-values.
#' @param 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 \code{par} argument):
#' \itemize{ \item \code{BonferroniAdj}: Bonferroni procedure. Optional
#' parameter: \code{weight}. \item \code{HolmAdj}: Holm procedure. Optional
#' parameter: \code{weight}. \item \code{HochbergAdj}: Hochberg procedure.
#' Optional parameter: \code{weight}. \item \code{HommelAdj}: Hommel procedure.
#' Optional parameter: \code{weight}. \item \code{FixedSeqAdj}: Fixed-sequence
#' procedure. \item \code{DunnettAdj}: Single-step Dunnett procedure. Required
#' parameters:\code{n}. \item \code{StepDownDunnettAdj}: Step-down Dunnett
#' procedure. Required parameters:\code{n}. \item \code{ChainAdj}: Family of
#' chain procedures. Required parameters: \code{weight} and \code{transition}.
#' \item \code{FallbackAdj}: Fallback procedure. Required parameters:
#' \code{weight}. \item \code{NormalParamAdj}: Parametric multiple testing
#' procedure derived from a multivariate normal distribution. Required
#' parameter: \code{corr}. Optional parameter: \code{weight}. \item
#' \code{ParallelGatekeepingAdj}: Family of parallel gatekeeping procedures.
#' Required parameters: \code{family}, \code{proc}, \code{gamma}. \item
#' \code{MultipleSequenceGatekeepingAdj}: Family of multiple-sequence
#' gatekeeping procedures. Required parameters: \code{family}, \code{proc},
#' \code{gamma}. \item \code{MixtureGatekeepingAdj}: Family of mixture-based
#' gatekeeping procedures. Required parameters: \code{family}, \code{proc},
#' \code{gamma}, \code{serial}, \code{parallel}. }
#' @param par defines the parameters associated to the multiple testing
#' procedure
#' @return Return a vector of adjusted p-values.
#' @seealso See Also \code{\link{MultAdjProc}} and \code{\link{AdjustCIs}}.
#' @references http://gpaux.github.io/Mediana/
#'
#' Dmitrienko, A., Bretz, F., Westfall, P.H., Troendle, J., Wiens, B.L.,
#' Tamhane, A.C., Hsu, J.C. (2009). Multiple testing methodology.
#' \emph{Multiple Testing Problems in Pharmaceutical Statistics}. Dmitrienko,
#' A., Tamhane, A.C., Bretz, F. (editors). Chapman and Hall/CRC Press, New
#' York. \cr
#'
#' Dmitrienko, A., Kordzakhia, G., Tamhane, A.C. (2011). Multistage and mixture
#' parallel gatekeeping procedures in clinical trials. \emph{Journal of
#' Biopharmaceutical Statistics}. 21, 726--747.
#'
#' Dmitrienko, A., Tamhane, A., Wiens, B. (2008). General multistage
#' gatekeeping procedures. \emph{Biometrical Journal}. 50, 667--677. \cr
#'
#' Dmitrienko, A., Tamhane, A.C. (2009). Gatekeeping procedures in clinical
#' trials. \emph{Multiple Testing Problems in Pharmaceutical Statistics}.
#' Dmitrienko, A., Tamhane, A.C., Bretz, F. (editors). Chapman and Hall/CRC
#' Press, New York. \cr
#'
#' Dmitrienko, A., Kordzakhia, G., Brechenmacher, T. (2016). Mixture-based
#' gatekeeping procedures for multiplicity problems with multiple sequences of
#' hypotheses. \emph{Journal of Biopharmaceutical Statistics}. 26, 758--780.
#'
#' Dunnett, C.W. (1955). A multiple comparison procedure for comparing several
#' treatments with a control. \emph{Journal of the American Statistical
#' Association}. 50, 1096--1121. \cr
#'
#' Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple
#' significance testing. \emph{Biometrika}. 75, 800--802. \cr
#'
#' Holm, S. (1979). A simple sequentially rejective multiple test procedure.
#' \emph{Scandinavian Journal of Statistics}. 6, 65--70. \cr
#'
#' Hommel, G. (1988). A stagewise rejective multiple test procedure based on a
#' modified Bonferroni test. \emph{Biometrika}. 75, 383--386. \cr
#'
#' Marcus, R. Peritz, E., Gabriel, K.R. (1976). On closed testing procedures
#' with special reference to ordered analysis of variance. \emph{Biometrika}.
#' 63, 655--660. \cr
#'
#' Naik, U.D. (1975). Some selection rules for comparing \eqn{p} processes with
#' a standard. \emph{Communications in Statistics. Series A}. 4, 519--535.
#'
#' Westfall, P. H., Krishen, A. (2001). Optimally weighted, fixed sequence, and
#' gatekeeping multiple testing procedures. \emph{Journal of Statistical
#' Planning and Inference}. 99, 25--40. \cr
#'
#' Wiens, B. (2003). A fixed-sequence Bonferroni procedure for testing multiple
#' endpoints. \emph{Pharmaceutical Statistics}. 2, 211--215. \cr
#'
#' Wiens, B., Dmitrienko, A. (2005). The fallback procedure for evaluating a
#' single family of hypotheses. \emph{Journal of Biopharmaceutical Statistics}.
#' 15, 929--942. \cr
#' @examples
#'
#' # Bonferroni, Holm, Hochberg, Hommel and Fixed-sequence procedure
#' proc = c("BonferroniAdj", "HolmAdj", "HochbergAdj", "HommelAdj", "FixedSeqAdj", "FallbackAdj")
#' rawp = c(0.012, 0.009, 0.023)
#'
#' # Equally weighted
#' sapply(proc, function(x) {AdjustPvalues(rawp,
#' proc = x)})
#'
#' # 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)))})
#'
#' # Dunnett procedures
#' # Compute one-sided adjusted p-values for the single-step Dunnett procedure
#' # 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
#'
#' # One-sided pvalue
#' rawp = 1-pt(stat,2*(n-1))
#'
#' # 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))
#'
#' # Parallel gatekeeping
#' # 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 raw p-values
#' rawp<-c(0.0082,0.0174, 0.0042,0.0180)
#'
#' # Define hHypothesis included in each family
#' 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)
#'
#' adjustp = AdjustPvalues(rawp,
#' proc = "ParallelGatekeepingAdj",
#' par = parameters(family = family,
#' proc = component.procedure,
#' gamma = gamma))
#'
#'
#' @export AdjustPvalues
AdjustPvalues = function(pval, proc, par=NA){
# Check if the multiplicity adjustment procedure is specified, check if it exists
if (!exists(proc)) {
stop(paste0("AdjustPvalues: Multiplicity adjustment procedure function '", proc, "' does not exist."))
} else if (!is.function(get(as.character(proc), mode = "any"))) {
stop(paste0("AdjustPvalues: Multiplicity adjustment procedure function '", proc, "' does not exist."))
}
adjustpval = do.call(proc, list(pval, list("Analysis", par)))
return(adjustpval)
}
gpaux/Mediana documentation built on May 31, 2021, 1:22 a.m.
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Defines functions AdjustPvalues
Documented in AdjustPvalues
#' AdjustPvalues function
#'
#' Computation of 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.
#'
#' This function can be used to adjust p-values according to a multiple testing
#' procedure defines in the \code{proc} argument.
#'
#' This function computes adjusted p-values and generates decision rules for
#' the Bonferroni, Holm (Holm, 1979), Hommel (Hommel, 1988), Hochberg
#' (Hochberg, 1988), fixed-sequence (Westfall and Krishen, 2001) and Fallback
#' (Wiens, 2003; Wiens and Dmitrienko, 2005) procedures. The adjusted p-values
#' are computed using the closure principle (Marcus, Peritz and Gabriel, 1976)
#' in general hypothesis testing problems (equally or unequally weighted null
#' hypotheses). For more information on the algorithms used in the function,
#' see Dmitrienko et al. (2009, Section 2.6).
#'
#' 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 the
#' Dunnett procedures, it is assumed that the test statistics follow a t
#' distribution. For more information on the algorithms used in the function,
#' see Dmitrienko et al. (2009, Section 2.7).
#'
#' 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), Dmitrienko, Kordzakhia and Tamhane
#' (2011) and Dmitrienko, Kordzakhia and Brechenmacher (2016). For more
#' information on parallel gatekeeping procedures (computation of adjusted
#' p-values, independence condition, etc), see Dmitrienko and Tamhane (2009,
#' Section 5.4).
#'
#' @param pval defines the raw p-values.
#' @param 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 \code{par} argument):
#' \itemize{ \item \code{BonferroniAdj}: Bonferroni procedure. Optional
#' parameter: \code{weight}. \item \code{HolmAdj}: Holm procedure. Optional
#' parameter: \code{weight}. \item \code{HochbergAdj}: Hochberg procedure.
#' Optional parameter: \code{weight}. \item \code{HommelAdj}: Hommel procedure.
#' Optional parameter: \code{weight}. \item \code{FixedSeqAdj}: Fixed-sequence
#' procedure. \item \code{DunnettAdj}: Single-step Dunnett procedure. Required
#' parameters:\code{n}. \item \code{StepDownDunnettAdj}: Step-down Dunnett
#' procedure. Required parameters:\code{n}. \item \code{ChainAdj}: Family of
#' chain procedures. Required parameters: \code{weight} and \code{transition}.
#' \item \code{FallbackAdj}: Fallback procedure. Required parameters:
#' \code{weight}. \item \code{NormalParamAdj}: Parametric multiple testing
#' procedure derived from a multivariate normal distribution. Required
#' parameter: \code{corr}. Optional parameter: \code{weight}. \item
#' \code{ParallelGatekeepingAdj}: Family of parallel gatekeeping procedures.
#' Required parameters: \code{family}, \code{proc}, \code{gamma}. \item
#' \code{MultipleSequenceGatekeepingAdj}: Family of multiple-sequence
#' gatekeeping procedures. Required parameters: \code{family}, \code{proc},
#' \code{gamma}. \item \code{MixtureGatekeepingAdj}: Family of mixture-based
#' gatekeeping procedures. Required parameters: \code{family}, \code{proc},
#' \code{gamma}, \code{serial}, \code{parallel}. }
#' @param par defines the parameters associated to the multiple testing
#' procedure
#' @return Return a vector of adjusted p-values.
#' @seealso See Also \code{\link{MultAdjProc}} and \code{\link{AdjustCIs}}.
#' @references http://gpaux.github.io/Mediana/
#'
#' Dmitrienko, A., Bretz, F., Westfall, P.H., Troendle, J., Wiens, B.L.,
#' Tamhane, A.C., Hsu, J.C. (2009). Multiple testing methodology.
#' \emph{Multiple Testing Problems in Pharmaceutical Statistics}. Dmitrienko,
#' A., Tamhane, A.C., Bretz, F. (editors). Chapman and Hall/CRC Press, New
#' York. \cr
#'
#' Dmitrienko, A., Kordzakhia, G., Tamhane, A.C. (2011). Multistage and mixture
#' parallel gatekeeping procedures in clinical trials. \emph{Journal of
#' Biopharmaceutical Statistics}. 21, 726--747.
#'
#' Dmitrienko, A., Tamhane, A., Wiens, B. (2008). General multistage
#' gatekeeping procedures. \emph{Biometrical Journal}. 50, 667--677. \cr
#'
#' Dmitrienko, A., Tamhane, A.C. (2009). Gatekeeping procedures in clinical
#' trials. \emph{Multiple Testing Problems in Pharmaceutical Statistics}.
#' Dmitrienko, A., Tamhane, A.C., Bretz, F. (editors). Chapman and Hall/CRC
#' Press, New York. \cr
#'
#' Dmitrienko, A., Kordzakhia, G., Brechenmacher, T. (2016). Mixture-based
#' gatekeeping procedures for multiplicity problems with multiple sequences of
#' hypotheses. \emph{Journal of Biopharmaceutical Statistics}. 26, 758--780.
#'
#' Dunnett, C.W. (1955). A multiple comparison procedure for comparing several
#' treatments with a control. \emph{Journal of the American Statistical
#' Association}. 50, 1096--1121. \cr
#'
#' Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple
#' significance testing. \emph{Biometrika}. 75, 800--802. \cr
#'
#' Holm, S. (1979). A simple sequentially rejective multiple test procedure.
#' \emph{Scandinavian Journal of Statistics}. 6, 65--70. \cr
#'
#' Hommel, G. (1988). A stagewise rejective multiple test procedure based on a
#' modified Bonferroni test. \emph{Biometrika}. 75, 383--386. \cr
#'
#' Marcus, R. Peritz, E., Gabriel, K.R. (1976). On closed testing procedures
#' with special reference to ordered analysis of variance. \emph{Biometrika}.
#' 63, 655--660. \cr
#'
#' Naik, U.D. (1975). Some selection rules for comparing \eqn{p} processes with
#' a standard. \emph{Communications in Statistics. Series A}. 4, 519--535.
#'
#' Westfall, P. H., Krishen, A. (2001). Optimally weighted, fixed sequence, and
#' gatekeeping multiple testing procedures. \emph{Journal of Statistical
#' Planning and Inference}. 99, 25--40. \cr
#'
#' Wiens, B. (2003). A fixed-sequence Bonferroni procedure for testing multiple
#' endpoints. \emph{Pharmaceutical Statistics}. 2, 211--215. \cr
#'
#' Wiens, B., Dmitrienko, A. (2005). The fallback procedure for evaluating a
#' single family of hypotheses. \emph{Journal of Biopharmaceutical Statistics}.
#' 15, 929--942. \cr
#' @examples
#'
#' # Bonferroni, Holm, Hochberg, Hommel and Fixed-sequence procedure
#' proc = c("BonferroniAdj", "HolmAdj", "HochbergAdj", "HommelAdj", "FixedSeqAdj", "FallbackAdj")
#' rawp = c(0.012, 0.009, 0.023)
#'
#' # Equally weighted
#' sapply(proc, function(x) {AdjustPvalues(rawp,
#' proc = x)})
#'
#' # 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)))})
#'
#' # Dunnett procedures
#' # Compute one-sided adjusted p-values for the single-step Dunnett procedure
#' # 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
#'
#' # One-sided pvalue
#' rawp = 1-pt(stat,2*(n-1))
#'
#' # 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))
#'
#' # Parallel gatekeeping
#' # 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 raw p-values
#' rawp<-c(0.0082,0.0174, 0.0042,0.0180)
#'
#' # Define hHypothesis included in each family
#' 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)
#'
#' adjustp = AdjustPvalues(rawp,
#' proc = "ParallelGatekeepingAdj",
#' par = parameters(family = family,
#' proc = component.procedure,
#' gamma = gamma))
#'
#'
#' @export AdjustPvalues
AdjustPvalues = function(pval, proc, par=NA){
# Check if the multiplicity adjustment procedure is specified, check if it exists
if (!exists(proc)) {
stop(paste0("AdjustPvalues: Multiplicity adjustment procedure function '", proc, "' does not exist."))
} else if (!is.function(get(as.character(proc), mode = "any"))) {
stop(paste0("AdjustPvalues: Multiplicity adjustment procedure function '", proc, "' does not exist."))
}
adjustpval = do.call(proc, list(pval, list("Analysis", par)))
return(adjustpval)
}
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