An Example of Fixed Design with Two Correlated Endpoints

In this vignette, we illustrate how to use TrialSimulator to simulate a trial of two correlated endpoints. Under a fixed design, only one milestone is specified for final analysis.

Simulation Settings

Trial consists of two active arms of high and low dose, and a standard-of-care arm.
Patients are randomized into the trial with ratio 1:1:1.
30 patients are randomized per month for the first 10 months, and 50 patients per month after then until 1000 patients.
Dropout rate is 10% at month 18, which is modeled by an exponential distribution, with rate parameter -log(1 - 0.1)/18.
Modeled by a three-state ill-death model, two endpoints PFS and OS are simulated.
- Primary endpoint OS has a medians of 15, 18.5, 20 months in standard-of-care, low dose and high dose arms, respectively.
- Key secondary endpoint PFS has a median of 7, 9, 10 months in the three arms.
- Pearson’s correlation between OS and PFS are 0.68, 0.65, and 0.60 in the three arms.
To ensures sufficient powers for both endpoints, final analysis is set when we have at least a total of 800 PFS events in the standard-of-care and high dose arm, meanwhile a total of 550 OS events are observed in three arms
- OS is tested using one-sided logrank test.
- PFS is tested using one-sided p-value from the Cox proportional hazard model as the PH assumption is assumed.
- The Bonferroni correction is adopted to split overall $\alpha = 5\%$, i.e., claiming significant effect for an endpoint when p-value is lower than $0.05/4$.

Transition Hazards of `PFS` and `OS`

We adopt the ill-death model to simulate the two endpoints. This ensures PFS $\leq$ OS with probability one, and makes no assumption on latent variables or copula parameters. TrialSimulator offers a function solveThreeStateModel() to convert endpoints’ medians and correlation to the transition hazards, which are required by the built-in generator CorrelatedPfsAndOs3(). Refer to the vignette Simulate Correlated Progression-Free Survival and Overall Survival as Endpoints in Clinical Trials for more details.

pars_soc <- solveThreeStateModel(median_pfs = 7, median_os = 15, corr = .68, 
                                 h12 = seq(.07, .10, length.out = 50))
pars_soc
#>   corr        h01       h02        h12        error
#> 1 0.68 0.07498342 0.0240376 0.08959184 0.0001363832

pars_low <- solveThreeStateModel(median_pfs = 9, median_os = 18.5, corr = .65, 
                                 h12 = seq(.04, .07, length.out = 50))
pars_low
#>   corr        h01        h02        h12       error
#> 1 0.65 0.05059501 0.02642134 0.06204082 0.001646964

pars_high <- solveThreeStateModel(median_pfs = 10, median_os = 20, corr = .60, 
                                  h12 = seq(.02, .06, length.out = 50))
pars_high
#>   corr        h01        h02        h12      error
#> 1  0.6 0.03964373 0.02967099 0.04693878 0.00208503

Transition hazards in three treatment arms
arm	h01	h02	h12
soc	0.075	0.024	0.090
low	0.051	0.026	0.062
high	0.040	0.030	0.047

Define Treatment Arms

We use generator CorrelatedPfsAndOs3() with endpoint() and arm() to define the three treatment arms.

#' define SoC
pfs_os_in_soc <- endpoint(name = c('pfs', 'os'), 
                          type = c('tte', 'tte'), 
                          generator = CorrelatedPfsAndOs3, 
                          h01 = 0.075, h02 = 0.024, h12 = 0.090)

soc <- arm(name = 'soc')
soc$add_endpoints(pfs_os_in_soc)

#' define low dose arm
pfs_os_in_low <- endpoint(name = c('pfs', 'os'), 
                          type = c('tte', 'tte'), 
                          generator = CorrelatedPfsAndOs3, 
                          h01 = 0.051, h02 = 0.026, h12 = 0.062)

low <- arm(name = 'low')
low$add_endpoints(pfs_os_in_low)

#' define high dose arm
pfs_os_in_high <- endpoint(name = c('pfs', 'os'), 
                           type = c('tte', 'tte'), 
                           generator = CorrelatedPfsAndOs3, 
                          h01 = 0.040, h02 = 0.030, h12 = 0.047)

high <- arm(name = 'high')
high$add_endpoints(pfs_os_in_high)

We can request for a summary report of, e.g., the high dose arm, by printing the arm object in R console. The medians of PFS and OS matches to the settings very well.

high

Define a Trial

With three arms, we can define a trial using the function trial(). Recruitment curve are specified through enroller with a built-in function StaggeredRecruiter of piecewise constant rate. We set duration to be an arbitrary large number (500) but controlling the end of trial through a pre-defined milestone later. Note that if seed = NULL, TrialSimulator will pick a seed for the purpose of reproducibility.

accrual_rate <- data.frame(end_time = c(10, Inf),
                           piecewise_rate = c(30, 50))
trial <- trial(
  name = 'Trial-3415', n_patients = 1000,
  seed = 1727811904, duration = 500,
  enroller = StaggeredRecruiter, accrual_rate = accrual_rate,
  dropout = rexp, rate = -log(1 - 0.1)/18, ## 10% by month 18
  silent = TRUE
)

trial$add_arms(sample_ratio = c(1, 1, 1), soc, low, high) ## 1:1:1
trial
#>  ⚕⚕ Trial Name:  Trial-3415  
#>  ⚕⚕ Description:  Trial-3415  
#>  ⚕⚕ # of Arms:  3  
#>  ⚕⚕ Registered Arms:  soc, low, high  
#>  ⚕⚕ Sample Ratio:  1, 1, 1  
#>  ⚕⚕ # of Patients:  1000  
#>  ⚕⚕ Planned Duration:  500  
#>  ⚕⚕ Random Seed:  1727811904

Define Milestone and Action for Final Analysis

To ensure sufficient powers of testing PFS and OS, final analysis is performed when we have at least 700 events for OS and 800 events for PFS. In the action function, we compute one-sided p-value of PFS using the proportional hazard Cox model, and one-sided p-value of OS using the logrank test. This is consistent with the assumption of ill-death model implemented in data generator CorrelatedPfsAndOs3(). Note that five columns are available in locked data: arm, pfs, ‘os’, ‘pfs_event’, and ‘os_event’, which are used to construct model formula. Estimates of hazard ratio are also computed. Built-in functions fitCoxph and fitLogrank return data frames. Refer to their help documants for more details.

action <- function(trial){
  
  locked_data <- trial$get_locked_data('final')
  
  pfs <- fitCoxph((Surv(pfs, pfs_event) ~ arm), placebo = 'soc', 
                  data = locked_data, alternative = 'less', 
                  scale = 'hazard ratio')
  
  os <- fitLogrank((Surv(os, os_event) ~ arm), placebo = 'soc', 
                   data = locked_data, alternative = 'less')
  
  ## Bonferroni test is applied to four hypotheses: 
  ## PFS_low, PFS_high, OS_low, and OS_high
  pfs$decision <- ifelse(pfs$p < .05/4, 'reject', 'accept')
  os$decision <- ifelse(os$p < .05/4, 'reject', 'accept')
  
  trial$save(
    value = pfs %>% filter(arm == 'low') %>% select(estimate, decision, info), 
    name = 'pfs_low')
  
  trial$save(
    value = pfs %>% filter(arm == 'high') %>% select(estimate, decision, info), 
    name = 'pfs_high')
  
  trial$save(
    value = os %>% filter(arm == 'low') %>% select(decision, info), 
    name = 'os_low')
  
  trial$save(
    value = os %>% filter(arm == 'high') %>% select(decision, info), 
    name = 'os_high')
  
}

Now we can define and register the milestone to a listener, which monitors the trial for us through a controller

final <- milestone(name = 'final', action = action, 
                   when = eventNumber(endpoint = 'pfs', n = 450, 
                                      arms = c('soc', 'high')) & 
                     eventNumber(endpoint = 'os', n = 550)
                   )

listener <- listener()
listener$add_milestones(final)
#> A milestone <final> is registered.

controller <- controller(trial, listener)

We can run a massive number of replicates in simulation to study operating characteristics of a trial design by specifying n in Controller$run(). We can set plot_event = FALSE to turn off plotting to save running time. The simulation results can be accessed by calling the member function get_output() of the controller.

controller$run(n = 1000, plot_event = FALSE, silent = TRUE)
output <- controller$get_output()

output %>% 
  head(5) %>% 
  kable(escape = FALSE) %>% 
  kable_styling(bootstrap_options = "striped", 
                full_width = FALSE,
                position = "left") %>%
  scroll_box(width = "100%")

trial	seed	milestone_time_<final>	n_events_<final>_<patient_id>	n_events_<final>_<pfs>	n_events_<final>_<os>	n_events_<final>_<arms>	pfs_low_<estimate>	pfs_low_<decision>	pfs_low_<info>	pfs_high_<estimate>	pfs_high_<decision>	pfs_high_<info>	os_low_<decision>	os_low_<info>	os_high_<decision>	os_high_<info>
Trial-3415	1727811904	39.79807	1000	726	550	c(334, 2….	0.8373861	accept	490	0.8470156	accept	485	reject	372	accept	378
Trial-3415	1580839845	36.97289	1000	731	550	c(334, 2….	0.8293869	accept	504	0.7566906	reject	491	reject	383	accept	372
Trial-3415	1644140445	35.81142	1000	738	550	c(334, 2….	0.7382446	reject	498	0.7208539	reject	504	accept	368	accept	372
Trial-3415	157892763	36.98769	1000	732	550	c(334, 2….	0.6802710	reject	502	0.6518396	reject	490	reject	389	reject	380
Trial-3415	1672612707	37.67231	1000	745	550	c(334, 2….	0.8062252	reject	513	0.6809993	reject	493	reject	381	reject	374

For example, we can compute the powers and summarize the estimates of hazard ratio for PFS.

output %>% 
  summarise(
    across(matches('_<decision>$'), ~ mean(. == 'reject') * 100, .names = 'Power_{.col}'), 
    across(matches('_<estimate>$'), ~ mean(.x), .names = 'HR_{.col}')
  ) %>%
  rename_with(~ sub('_<decision>$', '', .), starts_with('Power_')) %>%
  rename_with(~ sub('_<estimate>$', '', .), starts_with('HR_')) %>% 
  kable(col.name = NULL, digits = 3, align = 'r') %>% 
  add_header_above(c('Low', 'High', 'Low', 'High', 'Low', 'High'), align = 'r') %>% 
  add_header_above(c('PFS' = 2, 'OS' = 2, 'PFS' = 2)) %>% 
  add_header_above(c('Power (%)' = 4, 'Hazard Ratio' = 2)) %>% 
  kable_styling(full_width = TRUE)

Power (%)				Hazard Ratio
PFS		OS		PFS
Low	High	Low	High	Low	High

69	96	66	84	0.785	0.707