R/ssrmst.R

Defines functions print.ssrmst ssrmst rmst_1

Documented in print.ssrmst ssrmst

#' @name SSRMST-package
#' @aliases  SSRMST-package
#' @docType  package
#' @title Sample Size Calculation using Restricted Mean Survival Time
#' @description
#' The difference in restricted mean survival time (RMST), a clinically interpretable model-free measure, can be one of the alternatives to the hazard ratio.
#' The package calculates the study sample size and power in designing clinical trials using the difference in RMST.
#' Two types of one-sided tests, non-inferiority and superiority tests, are prepared.
#' @author Miki Horiguchi, Hajime Uno
#' @details Please check the vignette for details: \code{browseVignettes(package = "SSRMST")}
#' @references
#' Uno H, Claggett B, Tian L, Inoue E, Gallo P, Miyata T, Schrag D, Takeuchi M, Uyama Y, Zhao L,
#' Skali H, Solomon S, Jacobus S, Hughes M, Packer M, Wei LJ. Moving beyond the hazard ratio in
#' quantifying the between-group difference in survival analysis. Journal of clinical Oncology 2014,
#' 32, 2380-2385.
#'
#' Uno H, Wittes J, Fu H, Solomon SD, Claggett B, Tian L, Cai T, Pfeffer MA, Evans SR, Wei LJ.
#' Alternatives to Hazard Ratios for Comparing the Efficacy or Safety of Therapies in non-inferiority Studies.
#' Annals of Internal Medicine 2015, 163, 127-134.
#' @seealso
#' survival
#' survRM2
#' @examples
#' #---Example data
#'
#' ac_rate   = 15
#' ac_period = 35
#' tot_time  = 510
#' tau       = 500
#' scale0    = 8500
#' scale1    = 8500
#' margin    = 18
#'
#' a = ssrmst(ac_rate=ac_rate, ac_period=ac_period, tot_time=tot_time,
#'            tau=tau, scale0=scale0, scale1=scale1, margin=margin, ntest=20)
#' print(a)
NULL


#' @name ssrmst
#' @aliases ssrmst
#' @title Sample Size Calculation using Restricted Mean Survival Time
#' @description The package calculates the study sample size and power in designing clinical trials using the difference in restricted mean survival time (RMST).
#' Two types of one-sided tests, non-inferiority and superiority tests, are prepared.
#' Under certain conditions, 2,000 sets of realizations in default are generated for calculating confidence intervals of RMST differences.
#' Then the power is calculated, i.e., the chance that the lower bound of 2,000 confidence intervals of RMST differences falls above a margin.
#'
#'
#' @usage
#' ssrmst(ac_rate=NULL, ac_period=NULL, ac_number=NULL, tot_time, tau,
#'        shape0=1, scale0, shape1=1, scale1, margin=0, allocation1=0.5,
#'        one_sided_alpha=0.025, seed=NULL, ntest=2000)
#'
#' @param ac_rate         Accrual rate: the number of patients per unit time.
#' @param ac_period       Accrual period: the time point at last accrual.
#' @param ac_number       Accrual number: the total number of accrual patients.
#' @param tot_time        Total study time: the time point at last follow-up.
#' @param tau             Truncation time point to calculate RMSTs.
#' @param shape0,shape1   Shape parameters for the Weibull distribution in both the control (arm0) and the treatment (arm1).
#' @param scale0,scale1   Scale parameters for the Weibull distribution in both the control (arm0) and the treatment (arm1). Note that when the PH assumption is assumed, the value of the scale parameter in the treatment (arm1) needs to be larger than or equal to that in the control (arm0), because the difference of the RMSTs (arm1 minus arm0) is of interest.
#' @param margin          Non-inferiority margin: a clinically acceptable difference in RMST. A value of minus \code{margin} is used to evaluate the power. When default (\code{margin = 0}), a superiority test is selected.
#' @param allocation1     Proportion of patients allocated to the treatment (arm1). Default value is 0.5.
#' @param one_sided_alpha Nominal type I error level as one-sided. When default (\code{one_sided_alpha = 0.025}), 0.95 confidence intervals of the difference in RMST are estimated to calculate the power.
#' @param seed            Random seed used for the sampling. Default is \code{NULL}.
#' @param ntest           Number of iterations. When default (\code{ntest = 2000}), 2,000 sets of realizations are generated for calculating confidence intervals of RMST differences.
#'
#' @return A list with components:
#' @return \item{result}{Total study population and expected number of events.}
#' @return \item{power1}{The power based on separate variance, i.e., the chance that the lower bound of 2,000 confidence intervals of difference in RMST falls above a value of minus margin in a non-inferiority test (or above 0 in a superiority test).}
#' @return \item{power2}{The power based on pooled variance, i.e., the chance that the lower bound of 2,000 confidence intervals of difference in RMST falls above a value of minus margin in a non-inferiority test (or above 0 in a superiority test).}
#' @return \item{ac_rate}{Accrual rate used in the analyses.}
#' @return \item{ac_period}{Accrual period used in the analyses.}
#' @return \item{ac_number}{Accrual number used in the analyses.}
#' @return \item{ac_type}{Accrual type: 1; the number of patients per unit time is automatically calculated by specifying the parameters (ac_rate and ac_period),
#' 2; the accrual rate is automatically calculated by specifying the parameters (ac_period and ac_number), 3; the accrual period is automatically calculated by specifying the parameters (ac_rate and ac_number).}
#' @return \item{tot_time}{Total study time used in the analyses.}
#' @return \item{margin}{Margin used in the analyses.}
#' @return \item{tau}{Truncation time point used in the analyses.}
#' @return \item{one_sided_alpha}{Nominal type I error level as one-sided used in the analyses.}
#' @return \item{note}{Note regarding the truncation time, tau.}
#' @details For more details, please refer to the vignette: \code{browseVignettes(package = "SSRMST")}
#' @seealso
#'  survival
#'  survRM2
#' @references
#'  Uno H, Wittes J, Fu H, Solomon SD, Claggett B, Tian L, Cai T, Pfeffer MA, Evans SR, Wei LJ.
#'  Alternatives to Hazard Ratios for Comparing the Efficacy or Safety of Therapies in non-inferiority Studies.
#'  Annals of Internal Medicine 2015, 163, 127-134.
#' @name   ssrmst
#' @aliases ssrmst
#' @import survival survRM2
#' @importFrom stats rweibull runif qnorm
#' @examples
#'  #---Example data
#'
#'  #--Non-inferiority test
#'  ac_rate   = 15
#'  ac_period = 35
#'  tot_time  = 510
#'  tau       = 500
#'  scale0    = 8500
#'  scale1    = 8500
#'  margin    = 18
#'
#'  a = ssrmst(ac_rate=ac_rate, ac_period=ac_period, tot_time=tot_time,
#'             tau=tau, scale0=scale0, scale1=scale1, margin=margin, ntest=20)
#'  print(a)
#'
#'
#'  #--Superiority test
#'  ac_rate   = 15
#'  ac_period = 35
#'  tot_time  = 510
#'  tau       = 500
#'  scale0    = 4000
#'  scale1    = 8500
#'  b = ssrmst(ac_rate=ac_rate, ac_period=ac_period, tot_time=tot_time,
#'             tau=tau, scale0=scale0, scale1=scale1, ntest=20)
#'  print(b)
NULL


###############################################
# rmst_1 (one-arm) -- from survRM2 (hidden) --
###############################################
rmst_1 <- function(time, status, tau, alpha=0.05){
  #-- time
  #-- statuts
  #-- tau -- truncation time
  #-- alpha -- gives (1-alpha) confidence interval

  ft  = survfit(Surv(time, status)~1)
  idx = ft$time<=tau

  wk.time    = sort(c(ft$time[idx],tau))
  wk.surv    = ft$surv[idx]
  wk.n.risk  = ft$n.risk[idx]
  wk.n.event = ft$n.event[idx]

  time.diff = diff(c(0, wk.time))
  areas     = time.diff * c(1, wk.surv)
  rmst      = sum(areas)

  wk.var = ifelse((wk.n.risk-wk.n.event)==0, 0,
                  wk.n.event /(wk.n.risk *(wk.n.risk - wk.n.event)))
  wk.var   = c(wk.var,0)
  rmst.var = sum( cumsum(rev(areas[-1]))^2 * rev(wk.var)[-1])
  rmst.se  = sqrt(rmst.var)

  #--- check ---
  # print(ft, rmean=tau)

  #--- output ---
  out     = matrix(0,2,4)
  out[1,] = c(rmst, rmst.se, rmst-qnorm(1-alpha/2)*rmst.se, rmst+qnorm(1-alpha/2)*rmst.se)
  out[2,] = c(tau-out[1,1], rmst.se, tau-out[1,4], tau-out[1,3])
  rownames(out) = c("RMST","RMTL")
  colnames(out) = c("Est.", "se", paste("lower .",round((1-alpha)*100, digits=0), sep=""), paste("upper .",round((1-alpha)*100, digits=0), sep=""))

  Z=list()
  Z$result   = out
  Z$rmst     = out[1,]
  Z$rmtl     = out[2,]
  Z$tau      = tau
  Z$rmst.var = rmst.var
  Z$fit      = ft
  class(Z)="rmst_1"

  return(Z)
}
NULL



#' @export
#############################
# ssrmst -- main
#############################
ssrmst <-
  function(ac_rate=NULL, ac_period=NULL, ac_number=NULL, tot_time, tau, shape0=1, scale0, shape1=1, scale1,
           margin=0, allocation1=0.5, one_sided_alpha=0.025, seed=NULL, ntest=2000){

    ###--- initial check ---
    if (tau >= tot_time){
      stop(paste("The truncation time, tau, needs to be shorter than total study time, tot_time."))
    }
    if (allocation1 <= 0 | allocation1 >= 1){
      stop(paste("The proportion of patients allocated to the treatment (arm1), allocation1, needs to be between 0 and 1."))
    }
    if (margin < 0) {
      stop(paste("The margin needs to be larger than or equal to 0."))
    }
    if (shape1 <= 0 | shape0 <= 0) {
      stop(paste("The value of the shape parameter for the Weibull distribution in both arms needs to be larger than 0."))
    }

    if (shape0 == shape1 & scale0 > scale1){
      stop(paste("The value of the scale parameter for the Weibull distribution in the treatment (arm1) needs to be larger than or equal to that in the control (arm0). This is shown only when the PH assumption is assumed."))
    }

    ###--- ac_type : Need to specify only two paramerts from the parameters for accrual information (ac_rate, ac_period, and ac_number) ---
    ##-- ac_type = 1 : the number of patients per unit time is automatically calculated by specifying the parameters (ac_rate and ac_period).
    ##-- ac_type = 2 : the accrual rate is automatically calculated by specifying the parameters (ac_period and ac_number).
    ##-- ac_type = 3 : the accrual period is automatically calculated by specifying the parameters (ac_rate and ac_number).
    if(!is.null(ac_rate) & !is.null(ac_period) & is.null(ac_number)){
      ac_type = 1
    }
    if(is.null(ac_rate) & !is.null(ac_period) & !is.null(ac_number)){
      ac_type = 2
    }
    if(!is.null(ac_rate) & is.null(ac_period) & !is.null(ac_number)){
      ac_type = 3
    }
    if(!is.null(ac_rate) & !is.null(ac_period) & !is.null(ac_number)){
      ac_type = 1
    }
    if(is.null(ac_rate) & is.null(ac_period) & is.null(ac_number)){
      stop(paste("Please specify only two paramerts from the parameters for accrual information (ac_rate, ac_period, and ac_number)"))
    }

    ###--- Setting ---
    if(ac_type==1){
      if (ac_period > tot_time){
        n0 = round(ac_rate*tot_time*(1-allocation1))
        n1 = round(ac_rate*tot_time*allocation1)
      }
      if(ac_period <= tot_time){
        n0 = round(ac_rate*ac_period*(1-allocation1))
        n1 = round(ac_rate*ac_period*allocation1)
      }
      ac_number = n0 + n1
    }
    if(ac_type==2){
      n0 = ac_number*(1-allocation1)
      n1 = ac_number*allocation1

      ac_rate = ac_number/ac_period
    }
    if(ac_type==3){
      n0 = ac_number*(1-allocation1)
      n1 = ac_number*allocation1

      ac_period = ac_number/ac_rate
    }


    ###--- test (main part) ---
    answer1    = NULL
    answer2    = NULL
    check      = NULL
    event_arm0 = NULL
    event_arm1 = NULL

    if(!is.null(seed)){
      set.seed(seed)
    }
    for(w in 1:ntest){
      ##-- data frame --
      E             = rweibull(n0, shape0, scale0)
      C             = tot_time - runif(n0, 0, ac_period)
      time          = pmin(E,C)
      status        = as.numeric(E<=C)
      arm           = rep(0,n0)
      data0         = data.frame(time, status, arm)
      ind0          = data0$status==1
      event_arm0[w] = sum(data0$time[ind0]<=tot_time)

      E             = rweibull(n1, shape1, scale1)
      C             = tot_time - runif(n1, 0, ac_period)
      time          = pmin(E,C)
      status        = as.numeric(E<=C)
      arm           = rep(1,n1)
      data1         = data.frame(time, status, arm)
      ind1          = data1$status==1
      event_arm1[w] = sum(data1$time[ind1]<=tot_time)

      data   = rbind(data0, data1)
      data   = data[data$time>0, ]

      ##-- tau check --
      idx = data$arm==0; tt = data$time[idx]; tau0max = max(tt)
      idx = data$arm==1; tt = data$time[idx]; tau1max = max(tt)

      tau_max = min(tau0max, tau1max)

      if(tau <= tau_max){
        check[w] = 1
      } else{
        check[w] = 0
      }
      if(tau > tau_max){
        stop(paste("The truncation time, tau, needs to be shorter than or equal to the minimum of the largest observed time on each of the two arms: ", round(tau_max, digits=3)))
      }

      ##-- RMST calculation 1--
      ans1 = rmst2(data$time, data$status, data$arm, tau=tau, alpha=one_sided_alpha*2)

      lower1 = ans1$unadjusted.result[1,2]
      if(lower1 > -margin){
        answer1[w] = 1
      } else{
        answer1[w] = 0
      }

      ##-- RMST calculation 2--
      junk      = rmst_1(time=data$time, status=data$status, tau=tau, alpha=one_sided_alpha*2)
      se_all    = junk$rmst[2]

      se_pooled = sqrt((se_all*sqrt(n0+n1)/sqrt(n0))^2 + (se_all*sqrt(n0+n1)/sqrt(n1))^2)
      lower2 = ans1$unadjusted.result[1,1] - qnorm(1-one_sided_alpha)*se_pooled

      if(lower2 > -margin){
        answer2[w] = 1
      } else{
        answer2[w] = 0
      }
    }


    ###--- expected number of events ---
    n0_event = round(sum(event_arm0)/ntest)
    n1_event = round(sum(event_arm1)/ntest)


    ###--- power ---
    power1 = sum(answer1)/ntest
    power2 = sum(answer2)/ntest


    ###--- final check ---
    if(sum(check) >= 1){
      NOTE = paste("The truncation time: tau =", tau, " was specified, but there are no observed events after tau =", tau, "on either or both groups. Make sure that the size of riskset at tau =", tau, "is large enough in each arm.")
    }else{
      NOTE = paste("The truncation time: tau =", tau, " was specified.")
    }


    ###--- output ---
    out = matrix(0,2,3)

    out[1,1] = n0 + n1
    out[1,2] = n0
    out[1,3] = n1
    out[2,1] = n0_event + n1_event
    out[2,2] = n0_event
    out[2,3] = n1_event

    rownames(out) = c("Sample size", "Expected number of events")
    colnames(out) = c("Total", "arm0", "arm1")

    Z = list()

    Z$result          = out
    Z$power1          = power1
    Z$power2          = power2
    Z$ac_rate         = ac_rate
    Z$ac_period       = ac_period
    Z$ac_number       = ac_number
    Z$ac_type         = ac_type
    Z$tot_time        = tot_time
    Z$margin          = margin
    Z$tau             = tau
    Z$one_sided_alpha = one_sided_alpha
    Z$ntest           = ntest
    Z$note            = NOTE

    class(Z) = "ssrmst"

    Z
  }
NULL

#' @name print.ssrmst
#' @aliases print.ssrmst
#' @title print.ssrmst
#' @description S3 method for class 'ssrmst'
#' @param x Object to be printed.
#' @param ... Further arguments ignored in this function.
#' @export
print.ssrmst <- function(x, ...){

  ###--- superiority test ---
  if (x$margin==0){
    cat ("Superiority test \n")
    cat ("\n")
    print(x$result)
    cat ("\n")
    names(x$power1) = "Power (separate)"
    names(x$power2) = "Power (pooled)"
    print(x$power1)
    print(x$power2)
  }

  ###--- non-inferiority test ---
  if (x$margin>0){
    cat ("Non-inferiority test \n")
    cat ("\n")
    print(x$result)
    cat ("\n")
    names(x$power1) = "Power (separate)"
    names(x$power2) = "Power (pooled)"
    print(x$power1)
    print(x$power2)
  }

  invisible(x)
}
NULL

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SSRMST documentation built on May 29, 2017, 8:30 p.m.