#' ---
#' title: "Designing group-sequential trials with two groups and a survival endpoint with rpact"
#' author: "Marcel Wolbers, Gernot Wassmer, and Friedrich Pahlke"
#' date: "Last change: `r format(Sys.time(), '%d %B, %Y')`"
#' number: 4
#' header-includes:
#' - \usepackage{fancyhdr}
#' - \pagestyle{fancy}
#' - \setlength{\headheight}{23pt}
#' - \fancyfoot[C]{www.rpact.com}
#' - \fancyfoot[L]{\thepage}
#' output:
#' rmarkdown::html_document:
#' highlight: pygments
#' number_sections: yes
#' self_contained: yes
#' toc: yes
#' toc_depth: 3
#' toc_float: yes
#' css: style.css
#' includes:
#' before_body: header.html
#' after_body: footer2.html
#' ---
#'
#'
#'
#'
#'
#' This R markdown file provides examples for designing trials with survival endpoints with rpact. These examples are not intended to replace the official rpact documentation and help pages but rather to supplement them. They also only cover a selection of all rpact features.
#'
#' General convention: In rpact, arguments containing the **index "2"** always refer to the **control group**, **"1"** refer to the **intervention group**, and **treatment effects compare treatment versus control**.
#'
#' # Load rpact package
## ---- include=TRUE, echo=TRUE--------------------------------------------
# Load rpact
library(rpact)
packageVersion("rpact") # version should be version 2.0.1 or later
#'
#' # Overview of relevant rpact functions
#'
#' Sample size calculation for a group-sequential trial with a survival endpoints is performed in **two steps**:
#'
#' 1. **Define the (abstract) group-sequential design** using the function `getDesignGroupSequential`. For details regarding this step, see the R markdown file "Defining group-sequential boundaries with rpact". This step can be omitted if the trial has no interim analyses.
#'
#' 2. **Calculate sample size** for the survival endpoint by feeding the abstract design into function `getSampleSizeSurvival`. This step is described in detail below.
#'
#' Other relevant rpact functions for survival are:
#'
#' - `getPowerSurvival`: This function is the analogue to `getSampleSizeSurvival` for the calculation of power rather than sample size.
#' - `getEventProbabilities`: Calculates the probability of an event depending on follow-up time. This is useful for aligning interim analyses for different time-to-event endpoints.
#' - `getSimulationSurvival`: This function simulates group-sequential trials. For example, it allows to assess the power of trials with delayed treatment effects or to assess the data-dependent variability of the timing of interim analyses even if the protocol assumptions are perfectly fulfilled. It also allows to simulate hypothetical datasets for trials stopped early.
#'
#' This document describes all functions mentioned above except for trial simulation (`getSimulationSurvival`) which is described in the document "Simulation-based design of group-sequential trials with a survival endpoint with rpact".
#'
#' However, before describing the functions themselves, the document describes how survival functions, drop-out, and accrual can be specified in rpact which is required for all of these functions.
#'
#' # Specifying survival distributions in rpact
#'
#' rpact allows to specify survival distributions with exponential, piecewise exponential, and Weibull distributions. Exponential and piecewise exponential distributions are described below.
#'
#' Weibull distributions are specified in the same way as exponential distributions except that an additional scale parameter `kappa` needs to be provided which is 1 for the exponential distribution. Note that the parameters `shape` and `scale` in the standard R functions for the Weibull distribution in the stats-library (such as `dweibull`) are equivalent to `kappa` and 1/`lambda`, respectively, in rpact.
#'
#' ## Exponential survival distributions
#'
#' ### Event probability at a specific time point known
#'
#' - The time point is given by argument `eventTime`.
#' - The probability of an event (i.e. 1 minus survival function) in the control group is given by argument `pi2`.
#' - The probability of an event in the intervention arm can either be provided explicitly through argument `pi1` or, alternatively, implicitly by specifying the target hazard ratio `hazardRatio`.
#'
#' **Example:** If the intervention is expected to improve survival at 24 months from 70% to 80%, this would be expressed through arguments `eventTime=24, pi2=0.3, pi1=0.2`.
#'
#' ### Exponential parameter $\lambda$ known
#'
#' - The constant hazard function in the control arm can be provided as argument `lambda2`.
#' - The hazard in the intervention arm can either be provided explicitly through the argument `lambda1` or, alternatively, implicitly by specifying the target hazard ratio `hazardRatio`.
#'
#' ### Median survival known
#'
#' Medians cannot be specified directly. However, one can exploit that the hazard rate $\lambda$ of the exponential distribution is equal to $\lambda=\log(2)/\text{median}$.
#'
#' **Example:** If the intervention is expected to improve the median survival from 60 to 75 months, this would be expressed through the arguments `lambda2=log(2)/60, lambda1=log(2)/75`. Alternatively, it could be specified via `lambda2=log(2)/60, hazardRatio=0.8` (as the hazard ratio is 60/75=0.8).
#'
#' ## Weibull
#'
#' Weibull distributions are specified in the same way as exponential distributions except that an additional scale parameter `kappa` needs to be provided which is 1 for the exponential distribution.
#'
#'
#' ## Piecewise exponential survival
#'
#' ### Piecewise constant hazard rate $\lambda$ in each interval known
#'
#' **Example:** If the survival function in the control arm is assumed to be 0.03 (events/time-unit of follow-up) for the first 6 months, 0.06 for months 6-12, and 0.02 from month 12 onwards, this can be specified using the argument `piecewiseSurvivalTime` as follows:
#'
#' ```
#' piecewiseSurvivalTime=list("0 - <6" =0.03,
#' "6 - <12"=0.06,
#' ">=12" =0.02)
#' ```
#' Alternatively, the same distribution can be specified by giving the start times of each interval as argument `piecewiseSurvivalTime` and the actual hazard rate in that interval as argument `lambda2`. I.e., the relevant arguments for this example would be:
#'
#' ```
#' piecewiseSurvivalTime=c(0,6,12), lambda2=c(0.03,0.06,0.02)
#' ```
#'
#' For the intervention arm, one could either explicitly specify the hazard rate in the same time intervals through the argument `lambda1` or, more conveniently, specify the survival function in the intervention arm indirectly via the target hazard ratio (argument `hazardRatio`).
#'
#' Note: The sample size calculation functions discussed in this document **assume that the proportional hazards assumption holds**, i.e., that `lambda1` and `lambda2` are proportional if both are provided. Otherwise, the function call to `getSampleSizeSurvival` gives an error. For situations with non-proportional hazards, please see the separate document which discusses the simulation tool using the function `getSimulationSurvival`.
#'
#' ### Survival function at different time points known
#'
#' Piecewise exponential distribution are useful to **approximate survival functions**.
#' In principle, it is possible to approximate any distribution function well with a piecewise exponential distribution if suitably narrow intervals are chosen.
#'
#' Assume that the survival function is $S(t_1)$ at time $t_1$ and $S(t_2)$ at time $t_2$ with $t_2>t_1$ and that the hazard rate in the interval $[t_1,t_2]$ is constant. Then, the hazard rate in the interval can be derived as $(\log(S(t_1))-\log(S(t_2)))/(t_2-t_1)$.
#'
#' **Example:** Assume that it is known that the survival function is 1, 0.9, 0.7, and
#' 0.5 at months 0, 12, 24, 48 in the control arm. Then, an interpolating piecewise exponential
#' distribution can be derived as follows:
## ---- include=TRUE, echo=TRUE--------------------------------------------
t <- c(0,12,24,48) # time points at which survival function is known (must include 0)
S <- c(1,0.9,0.7,0.5) # Survival function at timepoints t
# derive hazard in each intervals per the formula above
lambda <- -diff(log(S))/diff(t)
# Define parameters for piecewise exponential distribution in the control arm
# interpolating the targeted survival values
# (code for lambda1 below assumes that the hazard afer month 48 is
# identical to the hazard in interval [24,48])
piecewiseSurvivalTime <- t
lambda2 <- c(lambda,tail(lambda,n=1))
lambda2 # print hazard rates
#'
#' ### Appendix: Random numbers, cumulative distribution, and quantiles for the piecewise exponential distribution {.unnumbered}
#'
#' rpact also provides general functionality for the piecewise exponential distribution,
#' see `?getPiecewiseExponentialDistribution` for details. Below is some example code which
#' shows that the derivation of the piecewise exponential distribution in the previous example is correct.
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
# plot the piecewise exponential survival distribution from the example above
tp <- seq(0,72,by=0.01)
plot(tp,
1-getPiecewiseExponentialDistribution(time=tp,
piecewiseSurvivalTime = piecewiseSurvivalTime,
piecewiseLambda = lambda2),
type="l",xlab="Time (months)",ylab="Survival function S(t)",lwd=2,ylim=c(0,1),axes=F,
main="Piecewise exponential distribution for the example")
axis(1,at=seq(0,72,by=12)); axis(2,at=seq(0,1,by=0.1))
abline(v=seq(0,72,by=12),h=seq(0,1,by=0.1),col=gray(0.9))
# Calculate median survival for the example (which should give 48 months here)
getPiecewiseExponentialQuantile(0.5,piecewiseSurvivalTime = piecewiseSurvivalTime,
piecewiseLambda = lambda2)
#'
#' # Specifying dropout in rpact
#'
#' rpact models dropout with an independent exponentially distributed variable. Dropout is specified by giving the probability of dropout at a specific time point. For example, an annual (12-monthly) dropout probability of 5% in both treatment arms can be specified through the following arguments:
#' ```
#' dropoutRate1 = 0.05, dropoutRate2 = 0.05, dropoutTime = 12
#' ```
#'
#' # Specifying accrual in rpact
#'
#' rpact allows to specify arbitrarily complex recruitment scenarios. Some examples are provided below.
#'
#' ## Absolute accrual intensity and accrual duration known {#accrualDurationKnown}
#'
#' **Example 1:** A **constant accrual intensity** of 24 subjects/months over 30 months (i.e., a maximum sample size of 24*30=720 subjects) can be specified through the arguments below:
#' ```
#' accrualTime = c(0,30), accrualIntensity=24
#' ```
#'
#' **Example 2:** A **flexible accrual intensity** of 20 subjects/months in the first 6 months, 25 subjects/months for months 6-12, and 30 subjects per month for months 12-24 can be specified through either of the two equivalent options below:
#'
#'
#' Option 1: List-based definition:
#' ```
#' accrualTime = list("0 - <6" = 20,
#' "6 - <12" = 25,
#' "12- <24" = 30)
#' ```
#'
#' Option 2: Vector-based definition (note that the length of the `accrualTime` vector is 1 larger than the length of the `accrualIntensity` as the end time of the accrual period is also included):
#' ```
#' accrualTime = c(0,6,12,24), accrualIntensity=c(20,25,30)
#' ```
#'
#' ## Absolute accrual intensity known, accrual duration unknown
#'
#' **Example 1:** A **constant accrual intensity** of 24 subjects/months with unspecified end of recruitment is specified through the arguments below:
#' ```
#' accrualTime = 0, accrualIntensity=24
#' # Note: accrualTime is the start time of accrual here which must be explicitly set to 0
#' ```
#'
#' **Example 2:** A **flexible accrual intensity** of 20 subjects/months in the first 6 months, 25 subjects/months from month 6-12, and 30 subjects thereafter can be specified through either of the two equivalent options below:
#'
#'
#' Option 1: List-based definition:
#' ```
#' accrualTime = list("0 - <6" = 20,
#' "6 - <12" = 25,
#' ">=12" = 30)
#' ```
#'
#' Option 2: Vector-based definition (note that the length of the `accrualTime` vector is equal to the length of the accrualIntensity vector as only the start time of the last accrual intensity is provided):
#' ```
#' accrualTime = c(0,6,12), accrualIntensity=c(20,25,30)
#' ```
#'
#' # Sample size calculation for superiority trials without interim analyses
#'
#' Given the specification of the survival distributions, drop-out and accrual as described above, sample size calculations can be readily performed in rpact using the function `getSampleSizeSurvival`. Importantly, in survival trials, the **number of events (and not the sample size) determines the power** of the trial. Hence, the **choice of the sample size** to reach this target number of events **is based on study-specific trade-offs** between the costs per recruited patient, study duration, and desired minimal follow-up duration.
#'
#' In practice, **a range of sample sizes need to be explored manually or via suitable graphs to find an optimal trade off**. Given a plausible absolute accrual intensity (usually provided by operations), a simple approach in rpact is to use one or both of the two options below:
#'
#' 1. Perform the calculation by trying multiple maximum sample sizes (argument `maxNumberOfSubjects`).
#' 2. It often makes sense to require a minimal follow-up time (argument `followUpTime`) for all patients in the trial at the time of the analysis. In this case, rpact automatically determines the maximum sample size.
#'
#' ## Example - exponential survival, flexible accrual intensity, no interim analyses {#sampleSizeGroupSeq}
#'
#' - Exponential PFS with a median PFS of 60 months in control (`lambda2=log(2)/60`) and a target hazard ratio of 0.74 (`hazardRatio=0.74`).
#' - Log-rank test at the two-sided 5%-significance level (`sided=2, alpha=0.05`), power 80% (`beta=0.2`).
#' - Annual drop-out of 2.5% in both arms (`dropoutRate1 = 0.025, dropoutRate2 = 0.025, dropoutTime = 12`).
#' - Recruitment is 42 patients/month from month 6 onwards after linear ramp up. (`accrualTime = c(0,1,2,3,4,5,6), accrualIntensity=c(6,12,18,24,30,36,42)`)
#' - Randomization ratio 1:1 (`allocationRatioPlanned=1`). This is the default and is thus not explicitly set in the function call below.
#' - Two sample size choices will be initially explored:
#' + A fixed total sample size of 1200 (`maxNumberOfSubjects = 1200`).
#' + Alternatively, the total sample size will be implicitly determined by specifying that every subject must have a minimal follow-up duration of at 12 months at the time of the analysis (`followUpTime=12`).
#'
#' Based on this, the required number of events and timing of interim analyses for the **fixed total sample size** of 1200 can be determined as follows:
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
sampleSize1 <- getSampleSizeSurvival(sided=2,alpha=0.05,beta=0.2,
lambda2=log(2)/60,hazardRatio=0.74,
dropoutRate1 = 0.025, dropoutRate2 = 0.025, dropoutTime = 12,
accrualTime = c(0,1,2,3,4,5,6),
accrualIntensity=c(6,12,18,24,30,36,42),
maxNumberOfSubjects = 1200)
sampleSize1
#' Thus, the required number of events is `r ceiling(sampleSize1$maxNumberOfEvents)` and the MDD corresponds to an observed HR of `r formatC(sampleSize1$criticalValuesEffectScale,digits=2,format="f")`.The `r ceiling(sampleSize1$maxNumberOfSubjects)` subjects will be recruited over `r formatC(sampleSize1$totalAccrualTime,digits=2,format="f")` months and the total study duration is `r formatC(sampleSize1$studyDuration,digits=2,format="f")` months. The user could now vary `maxNumberSubject` to further optimize the trade-off between sample size and study duration.
#'
#' Alternatively, **specifying a minimum follow-up duration** of 12 months leads to the following result:
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
sampleSize2 <- getSampleSizeSurvival(sided=2,alpha=0.05,beta=0.2,
lambda2=log(2)/60,hazardRatio=0.74,
dropoutRate1 = 0.025, dropoutRate2 = 0.025,dropoutTime = 12,
accrualTime = c(0,1,2,3,4,5,6),
accrualIntensity=c(6,12,18,24,30,36,42),
followUpTime=12)
sampleSize2
#' This specification leads to a higher sample size of `r ceiling(sampleSize2$maxNumberOfSubjects)` subjects which will be recruited over `r formatC(sampleSize2$totalAccrualTime,digits=2,format="f")` months and the total study duration is `r formatC(sampleSize2$studyDuration,digits=2,format="f")` months.
#'
#' To further explore the possible trade-offs, one could visualize recruitment and study durations for a range of sample sizes as illustrated in the code below:
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
# set up data frame which contains sample sizes and corresponding durations
sampleSizeDuration <- data.frame(
maxNumberSubjects=seq(600,1800,by=50),
accrualTime=NA,
studyDuration=NA)
# calculate recruitment and study duration for each sample size
for (i in 1:nrow(sampleSizeDuration)){
sampleSizeResult <- getSampleSizeSurvival(
sided = 2,alpha = 0.05,beta = 0.2,
lambda2 = log(2)/60,hazardRatio = 0.74,
dropoutRate1 = 0.025, dropoutRate2 = 0.025,dropoutTime = 12,
accrualTime = c(0,1,2,3,4,5,6),
accrualIntensity = c(6,12,18,24,30,36,42),
maxNumberOfSubjects = sampleSizeDuration$maxNumberSubjects[i])
sampleSizeDuration$accrualTime[i] <- sampleSizeResult$totalAccrualTime
sampleSizeDuration$studyDuration[i] <- sampleSizeResult$maxStudyDuration
}
# plot result
plot(sampleSizeDuration$maxNumberSubjects,
sampleSizeDuration$studyDuration,type="l",
xlab="Total sample size",
ylab="Duration (months)",
main="Recruitment and study duration vs sample size",
ylim=c(0,max(sampleSizeDuration$studyDuration)),
col="blue",lwd=1.5)
lines(sampleSizeDuration$maxNumberSubjects,
sampleSizeDuration$accrualTime,col="red",lwd=1.5)
legend(x=1000,y=100,
legend=c("Study duration under H1",
"Recruitment duration"),
col=c("blue","red"),lty=1,lwd=1.5)
#'
#' # Sample size calculation for non-inferiority trials without interim analyses
#'
#' Sample size calculation proceeds in the same fashion as for superiority trials except that the role of the null and the alternative hypothesis are reversed. I.e. in this case, the non-inferiority margin $\Delta$ corresponds to the treatment effect under the null hypothesis (`thetaH0`). Testing for non-inferiority trials is always one-sided.
#'
#' In the example code below, we assume that the non-inferiority margin corresponds to a 20% increase of the hazard function and that "in reality", i.e. under the alternative hypothesis, the survival functions in the two groups are equal. In this case, the "null hypothesis" is `thetaH0=1.2` versus the "alternative" `hazardRatio=1`. For illustration, all other assumptions were chosen as per the sample size calculations for superiority trials above.
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
sampleSizeNonInf <- getSampleSizeSurvival(sided = 1,alpha = 0.025,beta = 0.2,
lambda2 = log(2)/60,
thetaH0=1.2, hazardRatio = 1,
dropoutRate1 = 0.025, dropoutRate2 = 0.025,dropoutTime = 12,
accrualTime = c(0,1,2,3,4,5,6),
accrualIntensity = c(6,12,18,24,30,36,42),
followUpTime = 12)
sampleSizeNonInf
#' In this example, the required number of events is `r ceiling(sampleSizeNonInf$maxNumberOfEvents)`. The specification above leads to a sample size of `r ceiling(sampleSizeNonInf$maxNumberOfSubjects)` subjects recruited over `r formatC(sampleSizeNonInf$totalAccrualTime,digits=2,format="f")` months and an expected total study duration of `r formatC(sampleSizeNonInf$studyDuration,digits=2,format="f")` months.
#'
#' # Sample size calculation for trials with interim analyses
#'
#' Calculations can be performed as for trials without interim analyses except that the group-sequential design needs to be specified using `getDesignGroupSequential` before calling `getSampleSizeSurvival`. For details regarding the specification of the group-sequential design, see the R markdown file "Defining group-sequential boundaries with rpact".
#'
#' In general, rpact supports both one-sided and two-sided group-sequential designs. However, if futility boundaries are specified, only one-sided tests are permitted. **For simplicity, it is often preferred to use one-sided tests for group-sequential designs** (typically with $\alpha=0.025$).
#'
#' One additional consideration for phase III trials with **efficacy interim analyses** is that they **should only be performed at time-points when the data is sufficiently mature** to allow for a potential filing. For example, it is often sensible to perform efficacy interim analyses only after all subjects have been recruited. This aspect can be further explored by looking at simulated data for (hypothetical) trials that would have stopped at the interim analysis. This can easily be implemented using the **simulation tool** in rpact and we refer to the R markdown document "Simulation-based design of group-sequential trials with a survival endpoint with rpact" for further details.
#'
#' ## Example - piece-wise exponential survival, constant accrual intensity, group-sequential design
#'
#' - 1:1 randomized.
#' - 80% power at the one-sided 2.5% significance level.
#' - Efficacy interim analyses at 50% and 75% of total information using an O'Brien&Fleming type $\alpha$-spending function, no futility interim analyses.
#' - Target HR for primary endpoint (PFS) is 0.75.
#' - PFS in the control arm follows a piece-wise exponential distribution, with the hazard rate h(t) estimated using historical controls as follows:
#' + h(t) = 0.025 for t between 0 and 6 months,
#' + h(t) = 0.04 for t between 6 and 9 months,
#' + h(t) = 0.015 for t between 9 and 15 months,
#' + h(t) = 0.01 for t between 15 and 21 months,
#' + h(t) = 0.007 for t beyond 21 months.
#' - An annual dropout probability of 5%.
#' - Recruitment of 42 patients/month up to a maximum of 1000 subjects.
#'
#' ### Sample size calculation
#' The **group-sequential design** (including type I and II error) can be specified as follows:
## ---- include=TRUE, echo=TRUE--------------------------------------------
design <- getDesignGroupSequential(sided = 1, alpha = 0.025, beta = 0.2,
informationRates = c(0.5,0.75,1),
typeOfDesign="asOF")
#'
#' The **piecewise exponential survival** is defined as described above:
## ---- include=TRUE, echo=TRUE--------------------------------------------
piecewiseSurvivalTime <- list(
"0 - <6" = 0.025,
"6 - <9" = 0.04,
"9 - <15" = 0.015,
"15 - <21" = 0.01,
">=21" = 0.007)
#'
#' Now, the **sample size** calculation can be performed:
## ---- include=TRUE, echo=TRUE--------------------------------------------
sampleSizeResult <- getSampleSizeSurvival(
design = design,
piecewiseSurvivalTime = piecewiseSurvivalTime,hazardRatio = 0.75,
dropoutRate1 = 0.05, dropoutRate2 = 0.05, dropoutTime = 12,
accrualTime = 0, accrualIntensity=42, maxNumberOfSubjects = 1000)
#' The sample size result can be summarized by displaying the **default rpact output**:
## ---- include=TRUE, echo=TRUE--------------------------------------------
sampleSizeResult
#' In addition, the **`printDesignPlanTable` function** summarizes the results per stage in a
#' different format and also adds results which are stored in the underlying
#' group-sequential `design` such as exit probabilities:
## ---- include=TRUE, echo=TRUE--------------------------------------------
# Summarize design
simpleBoundarySummary <- function(result) {
parameters <- list("Stage" = c(1:result$.design$kMax),
"Information rate" = round(t(result$.design$informationRates), 2),
"Number of events" = round(t(result$eventsPerStage[, 1]), 1),
"Analysis time under H1" = round(t(result$analysisTime[, 1]), 1),
"Cumulative alpha spent" = round(result$.design$alphaSpent, 4),
"Stage levels" = round(result$.design$stageLevels, 4),
"Efficacy boundary (Z-value scale)" = round(result$.design$criticalValues, 3))
if (ncol(result$criticalValuesEffectScale) > 0) {
parameters$"Efficacy boundary (treatment effect scale)" <-
round(t(result$criticalValuesEffectScale[, 1]), 3)
}
names(parameters) <- paste0(format(names(parameters)), " ")
for (paramName in names(parameters)) {
values <- parameters[[paramName]]
values <- format(c(" ", values))[2:(length(values) + 1)]
cat(paramName, values, "\n")
}
}
simpleBoundarySummary(sampleSizeResult)
#'
#' ### Sample size plots
#'
#' rpact provides a large number of plots, see the R markdown file "Example:
#' design, sample size and power plots" (provided by rpact) for examples of all plots.
#' Below, we just show two selected plots.
## ---- include=TRUE, echo=TRUE--------------------------------------------
# Boundary on treatment effect scale
plot(sampleSizeResult, type = 2, showSource = TRUE)
# Survival function and piecewise constant hazard
plot(sampleSizeResult, type = 14)
#'
#' # Power calculation
#'
#' The function `getPowerSurvival` is the analogue of `getSampleSizeSurvival` for power calculations and has a very similar syntax. We illustrate its usage for the above example below.
#'
#' ## Example - piece-wise exponential survival, constant accrual intensity, group-sequential design (continued)
#'
#' What is the power of the trial from the example above if the true hazard ratio is 0.7 rather than the targeted hazard ratio of 0.75 above?
#'
#' The corresponding power calculation is given below. The function call uses the same arguments as the call to function `getSampleSizeSurvival` above except that the maximum sample size of 1000 and the maximum number of events of 387 from above are provided as arguments and the hazard ratio is changed to 0.7.
#' In addition, the direction of the test (`directionUpper`) needs to be specified.
## ---- include=TRUE, echo=TRUE--------------------------------------------
powerResult <- getPowerSurvival(
design = design,
piecewiseSurvivalTime = piecewiseSurvivalTime,hazardRatio = 0.7,
dropoutRate1 = 0.05, dropoutRate2 = 0.05, dropoutTime = 12,
accrualTime = 0, accrualIntensity=42,
maxNumberOfSubjects = 1000,maxNumberOfEvents=387,directionUpper = FALSE)
powerResult
#' This gives an overall power of `r formatC(powerResult$overallReject,digits=2,format="f")`. Note also that the timing of the interim analyses is slightly deferred now (as the events now occur more slowly in the intervention arm) but the expected study duration is decreased from `r formatC(sampleSizeResult$studyDurationH1,digits=2,format="f")` to `r formatC(powerResult$studyDuration,digits=2,format="f")` months because chances to stop the trial early have increased.
#'
#' The impact of the true hazard ratio on power and study duration can also be calculated by entering the `hazardRatio` argument as a vector. Below, the hazard ratio is varied from 0.6 to 1 (in increments of 0.02) and the results are displayed graphically.
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
powerResult2 <- getPowerSurvival(
design = design,
piecewiseSurvivalTime = piecewiseSurvivalTime,hazardRatio = seq(0.6,1,by=0.02),
dropoutRate1 = 0.05, dropoutRate2 = 0.05, dropoutTime = 12,
accrualTime = 0, accrualIntensity=42,
maxNumberOfSubjects = 1000,maxNumberOfEvents=387,directionUpper = FALSE)
# Plot true hazard ratio vs overall power, probability of an early rejection,
# and expected number of events
plot(powerResult2, type = 6, legendPosition = 3) # legend in left bottom
# Plot hazard ratio vs analysis time
plot(powerResult2, type = 12, legendPosition = 4) # right top
#'
#' For examples of all available plots in rpact, see the R vignette "How to create admirable plots with rpact" (written by rpact, [link](https://vignettes.rpact.org/html/rpact_plot_examples.html)).
#'
#' # Prediction of number of events over time
#'
#' The function `getEventProbabilities` calculates the probability that a randomly chosen subject has an observed event up to a specied `time` after the start of the study. By multiplying this probability with the maximal sample size, we obtain the **expected number of events** up to that time point.
#'
#' The arguments for the function `getEventProbabilities` are similar to those for the function `getSampleSizeSurvival` discussed above. One difference is that the maximal sample size must always be provided in function `getEventProbabilities`. This can either be done explicitly by setting argument `maxNumberOfSubjects` or implicitly by specifying the accrual duration as described in Section \@ref(accrualDurationKnown).
#'
#' For illustration, we revisit the example from Section \@ref(sampleSizeGroupSeq). In that example, the sample size was `r sampleSizeResult$maxNumberOfSubjects` and the expected timing of the final analysis after observing `r ceiling(sampleSizeResult$maxNumberOfEvents)` events was after `r formatC(sampleSizeResult$maxStudyDuration,digits=2,format="f")` months. The code below re-calculates the expected number of events until that time point.
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
# Sample size per arm and overall for the example
maxNumberOfSubjects1 <- 500
maxNumberOfSubjects2 <- 500
maxNumberOfSubjects <- maxNumberOfSubjects1+maxNumberOfSubjects2
# Probability that a randomly chosen subject has an observed event
# until 60 months after study start
probEvent <- getEventProbabilities(time = 60,
piecewiseSurvivalTime = piecewiseSurvivalTime,hazardRatio = 0.75,
dropoutRate1 = 0.05, dropoutRate2 = 0.05, dropoutTime = 12,
accrualTime = 0, accrualIntensity = 42,maxNumberOfSubjects = maxNumberOfSubjects)
probEvent
# Total expected number of events until 60 months
probEvent$overallEventProbabilities*maxNumberOfSubjects
# Expected number of events per group until 60 months
probEvent$eventProbabilities1*maxNumberOfSubjects1 # intervention
probEvent$eventProbabilities2*maxNumberOfSubjects2 # control
#'
#' The function `getEventProbabilities` also allows a vector argument for `time` and the code below illustrates how to calculate (and plot) expected event numbers over time (overall and by group). In addition, the number of recruited subjects is added to the plot which can be obtained with function `getNumberOfSubjects`.
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
time <- 1:60
# Expected number of events overall and per group at 1-60 months after study start
probEvent <- getEventProbabilities(time = time,
piecewiseSurvivalTime = piecewiseSurvivalTime,hazardRatio = 0.75,
dropoutRate1 = 0.05, dropoutRate2 = 0.05, dropoutTime = 12,
accrualTime = 0, accrualIntensity = 42,maxNumberOfSubjects = maxNumberOfSubjects)
expEvent <- probEvent$overallEventProbabilities*maxNumberOfSubjects # overall
expEventInterv <- probEvent$eventProbabilities1*maxNumberOfSubjects1 # intervention
expEventCtrl <- probEvent$eventProbabilities2*maxNumberOfSubjects2 # control
# Cumulative number of recruited patients over time
nSubjects <- getNumberOfSubjects(time=time, accrualTime = 0, accrualIntensity=42, maxNumberOfSubjects = maxNumberOfSubjects)
# plot result
plot(time,nSubjects$numberOfSubjects,type="l",lwd=2,
main="Number of patients and expected number of events over time",
xlab="Time from study start (months)",
ylab="Number of patients / events")
lines(time,expEvent,col="blue",lwd=2)
lines(time,expEventCtrl,col="green")
lines(time,expEventInterv,col="orange")
legend(x=28,y=900,
legend=c("Recruitment","Expected events - All subjects",
"Expected events - Control","Expected events - Intervention"),
col=c("black","blue","green","orange"),lty=1,lwd=c(2,2,1,1))
#'
#' # Interim analyses for multiple time-to-event endpoints
#'
#' The function `getEventProbabilities` can also be used to align interim analyses of different time to event endpoints as illustrated in two examples below.
#'
#' ## Example: PFS and OS as co-primary endpoints, OS interim aligned with PFS final analysis
#'
#' - Overall type I error 5% (two-sided), 1:1 randomized with PFS and OS as co-primary endpoints.
#' - Assumptions for PFS (1% type I error assigned):
#' + Median PFS control is 6 months, target hazard ratio is 0.65, 95% power at two-sided 1% significance level.
#' + Only one primary analysis.
#' - Assumptions for OS (4% type I error assigned):
#' + Median OS control is 12 month, target hazard ratio is 0.75, 80% power at two-sided 4% significance level.
#' + Interim analysis at the primary PFS analysis, final analysis when the target number of OS events has been reached.
#' + O'Brien&Fleming type $\alpha$-spending function.
#' - Uniform recruitment of 60 patients/month over 10 months, no dropout.
#'
#' To display results more concisely, all outputs below rely on the **`simpleBoundarySummary` function**.
#'
#' The first step is to calculate the **required number of events and the timing of the primary analyses for PFS**:
## ---- include=TRUE, echo=TRUE--------------------------------------------
sampleSizePFS <- getSampleSizeSurvival(beta = 0.05,sided = 2,alpha = 0.01,
lambda2 = log(2)/6,hazardRatio = 0.65,
accrualTime = c(0,10),accrualIntensity = 60)
simpleBoundarySummary(sampleSizePFS)
#'
#' Based on this, the estimated time of the primary PFS analysis is after `r formatC(sampleSizePFS$analysisTime,2,format="f")` months.
#'
#' Second, we **calculate the expected number of OS events at the final PFS analysis** after `r formatC(sampleSizePFS$analysisTime,2,format="f")` months.
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
# Expected number of OS events at the time of the final PFS analysis
probOS <- getEventProbabilities(time = 16.37,
accrualTime = c(0,10),accrualIntensity = 60,
lambda2 = log(2)/12, hazardRatio = 0.75)
probOS$overallEventProbabilities*600
#'
#' Third, we caculate the **required number of events and the timing of analyses for OS using an initial guess for the information fraction at the OS interim analysis** of 50%:
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
# Preliminary group-sequential design for OS assuming interim at 50% information
designOS1 <- getDesignGroupSequential(sided = 2, alpha = 0.04,beta = 0.2,
informationRates = c(0.5, 1), typeOfDesign = "asOF")
# Preliminary sample size for OS assuming interim at 50% information
sampleSizeOS1 <- getSampleSizeSurvival(designOS1,
lambda2 = log(2)/12,hazardRatio = 0.75,
accrualTime = c(0,10),accrualIntensity = 60)
simpleBoundarySummary(sampleSizeOS1)
#'
#' Finally, we **recalculate** the required number of events and timing of analyses for OS using **an updated estimate of the information fraction at the interim analysis**. Specifically, we calculate this fraction as the expected number of events at the time of the final PFS analysis (i.e. `r ceiling(probOS$overallEventProbabilities*600)` events) relative to the maximal number of events based on the calculation for OS above (i.e. `r ceiling(sampleSizeOS1$maxNumberOfEvents)`):
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
# Updated group-sequential design for OS assuming an updated information fraction
designOS2 <- getDesignGroupSequential(sided = 2,alpha = 0.04,beta = 0.2,
informationRates = c(258/407, 1), typeOfDesign = "asOF")
# Updated sample size for OS
sampleSizeOS2 <- getSampleSizeSurvival(designOS2,
lambda2 = log(2)/12,hazardRatio = 0.75,
accrualTime = c(0,10),accrualIntensity = 60)
simpleBoundarySummary(sampleSizeOS2)
#'
#' According to the updated calculation, the panned timing of the OS interim analysis is after `r formatC(sampleSizeOS2$analysisTime[1,1],2,format="f")` months which is sufficiently close to the planned timing of the PFS final analysis after `r formatC(sampleSizePFS$analysisTime,2,format="f")` months for planning purposes.
#'
#' ## Example: Power considerations for OS as a secondary endpoint, OS interim aligned with primary PFS analysis
#'
#' - Overall type I error 5% (two-sided), 1:1 randomized with PFS as the primary endpoint.
#' - Sample size calculation for PFS resulted in the following design:
#' + No interim analyses for PFS.
#' + Uniform recruitment of 80 patients/month over 10 months (800 patients in total), no dropout.
#' + Final PFS analysis with 80% power for the targeted HR is expected after a study duration of 24 months.
#' - Asssumption for OS:
#' + Median OS 30 months, OS HR expected to be around 0.8.
#' + OS is only formally tested if PFS is significant (hierarchical testing).
#' + One interim analysis at the time of the final PFS analysis.
#' + It is anticipated that the OS analysis cannot be fully powered. The target number of OS events at the final OS analysis will be pre-defined after considering all possible trade-offs between study power and duration.
#' + O'Brien&Fleming type $\alpha$-spending function.
#'
#' The code below calulates study duration and power for the final OS analysis (conditional on significance for the primary PFS analysis) depending on the target number of OS events at the final analysis. The resulting plots should facilitate the final choice of the timing of the OS analysis.
#'
## ---- include=TRUE, echo=TRUE--------------------------------------------
# 1) Calculate expected number of OS events at the time of the final PFS analysis
noDeathsAtPFSAnalysis <- getEventProbabilities(time = 24,
lambda2 = log(2)/30, hazardRatio = 0.8,
accrualTime = c(0,20),accrualIntensity = 40)$overallEventProbabilities*800
noDeathsAtPFSAnalysis
# 2) Set up data frame which will contain number of OS events and
# corresponding power and study durations
# (vary target number of OS events from 250-700)
powerOSevents <- data.frame(
maxNumberOSEvents=seq(250,700,by=10),
power=NA,
durationFinalOSAnalysis=NA)
# 3) Calculate power and study durations depending on target number of OS events
for (i in 1:nrow(powerOSevents)){
# Define OF-type design, with first interim after noDeathsAtPFSAnalysis events
# and final analysis after maxNumberOSEvents[i] events
# PS: beta not explicitly set here because boundary and
# subsequent power calculations do not depend on it
informationRates <- c(noDeathsAtPFSAnalysis,powerOSevents$maxNumberOSEvents[i])/
powerOSevents$maxNumberOSEvents[i]
design <- getDesignGroupSequential(sided = 2, alpha = 0.05,typeOfDesign= "asOF",
informationRates = informationRates)
# Power and study duration after maxNumberOSEvents[i]
powerResult <- getPowerSurvival(
design = design,
lambda2 = log(2)/30, hazardRatio = 0.8,
accrualTime = c(0,20),accrualIntensity = 40,
maxNumberOfEvents = powerOSevents$maxNumberOSEvents[i])
powerOSevents$power[i] <- powerResult$overallReject
powerOSevents$durationFinalOSAnalysis[i] <- powerResult$maxStudyDuration
}
# 4) Plot results
par(mfrow=c(1,2))
# events cs power
plot(powerOSevents$maxNumberOSEvents,
powerOSevents$power,type="l",
xlab="Required # OS events at final analysis",
ylab="Power",ylim=c(0,1),lwd=1.5)
# Duration vs power
plot(powerOSevents$durationFinalOSAnalysis,
powerOSevents$power,type="l",
xlab="Time until final OS analysis (months)",
ylab="Power",ylim=c(0,1),lwd=1.5)
#'
#' # Extracting information from rpact objects
#' `names(rpact_object)` gives variable names of an rpact object which can be accessed via `rpact_object$varname`. For example, `sampleSizeResult$studyDurationH1` contains the expected study duration under H1. For more details on using R generics for rpact objects, please refer to vignette "Example: using rpact with R generics" (written by rpact, [link](https://vignettes.rpact.org/html/rpact_generic_functions.html)).
#'
#'
#'
#' ***
#'
#' System: rpact `r packageVersion("rpact")`, `r R.version.string`, platform: `r R.version$platform`
#'
## ---- include=TRUE, echo=FALSE, results='asis'---------------------------
print(citation("rpact"), bibtex = FALSE)
#'