simtrial-fundamentals

name	simtrial-fundamentals
description	Core simtrial package functions for time-to-event clinical trial simulation. Use when generating survival data, performing weighted logrank tests, or running TTE simulations.

simtrial Fundamentals

When to Use This Skill

• Simulating time-to-event (survival) clinical trial data
• Generating piecewise exponential failure/dropout times
• Modeling delayed treatment effects or non-proportional hazards
• Performing weighted logrank tests (Fleming-Harrington, Magirr-Burman)
• Running MaxCombo tests for non-proportional hazards
• Simulating group sequential designs
• Calculating RMST or milestone endpoints

Package Overview

simtrial (v1.0.2) by Merck provides fast, extensible clinical trial simulation for time-to-event endpoints. Key features:

• Piecewise exponential distributions for flexible hazard modeling
• Built-in support for non-proportional hazards scenarios
• Integration with gsDesign2 for group sequential designs
• Parallel computation via doFuture/foreach
• Pipe-friendly API using data.table for performance

Core Data Generation Functions

sim_pw_surv() - Main Simulation Function

Generates stratified time-to-event outcome randomized trial data.

sim_pw_surv(
  n = 100,                    # Total sample size
  stratum = data.frame(       # Stratum definitions
    stratum = "All",
    p = 1                     # Prevalence/probability
  ),
  block = c(rep("control", 2), rep("experimental", 2)),  # Randomization block
  enroll_rate = data.frame(   # Enrollment rates by period
    rate = 9,
    duration = 1
  ),
  fail_rate = data.frame(     # Failure rates by stratum/treatment/period
    stratum = rep("All", 4),
    period = rep(1:2, 2),
    treatment = c(rep("control", 2), rep("experimental", 2)),
    duration = rep(c(3, 1), 2),
    rate = log(2) / c(9, 9, 9, 18)  # Hazard rates
  ),
  dropout_rate = data.frame(  # Dropout rates
    stratum = rep("All", 2),
    period = rep(1, 2),
    treatment = c("control", "experimental"),
    duration = rep(100, 2),
    rate = rep(0.001, 2)
  )
)

Returns: Data frame with columns:

• stratum: Patient stratum
• enroll_time: Calendar time of enrollment
• treatment: Treatment assignment ("control" or "experimental")
• fail_time: Time from enrollment to event
• dropout_time: Time from enrollment to dropout
• cte: Calendar time of event (enroll_time + min(fail_time, dropout_time))
• fail: Event indicator (1 = event, 0 = censored)

Attributes:

• ratio: Randomization ratio (experimental:control)
• generate_by_simpwsurv: Marker indicating data origin

rpwexp() - Piecewise Exponential Random Generation

Generates failure times from piecewise exponential distribution.

rpwexp(
  n = 100,
  fail_rate = data.frame(
    duration = c(3, 100),
    rate = c(0.08, 0.04)  # Hazard rates for each period
  )
)

rpwexp_enroll() - Enrollment Time Generation

Generates enrollment times with piecewise constant rates.

rpwexp_enroll(
  n = 100,
  enroll_rate = data.frame(
    rate = c(5, 10, 20),      # Patients/month
    duration = c(2, 2, 10)    # Period durations
  )
)

Data Cutting Functions

cut_data_by_event() - Event-Based Cutoff

Cut data when target number of events is reached.

trial_data <- sim_pw_surv(n = 400) |>
  cut_data_by_event(n_events = 200)

cut_data_by_date() - Calendar Time Cutoff

Cut data at specified calendar time.

trial_data <- sim_pw_surv(n = 400) |>
  cut_data_by_date(cut_date = 24)  # 24 months

get_analysis_date() - Advanced Cutoff Logic

Derive analysis date given multiple conditions.

get_analysis_date(
  data,
  planned_calendar_time = 36,        # Minimum calendar time

  target_event_overall = 300,        # Target events
  max_extension_for_target_event = 42,  # Max wait for events
  min_n_overall = 200,               # Minimum enrolled
  min_followup = 12,                 # Minimum follow-up after enrollment
  min_time_after_previous_analysis = 6  # Gap from previous analysis
)

Statistical Analysis Functions

wlr() - Weighted Logrank Test

Supports multiple weighting schemes.

# Standard logrank (FH(0,0))
data |> wlr(weight = fh(rho = 0, gamma = 0))

# Fleming-Harrington weights
data |> wlr(weight = fh(rho = 0, gamma = 0.5))   # Late effects emphasis
data |> wlr(weight = fh(rho = 1, gamma = 0))     # Early effects emphasis

# Magirr-Burman weights (for delayed effects)
data |> wlr(weight = mb(delay = 4, w_max = 2))

# Early zero weights (Xu 2017)
data |> wlr(weight = early_zero(early_period = 6))

Returns: List with:

• method: "WLR"
• parameter: Weight specification (e.g., "FH(rho=0, gamma=0.5)")
• estimate: Treatment effect estimate
• se: Standard error
• z: Z-score (negative favors experimental)
• info: Statistical information
• info0: Information under null

Weight Functions

fh() - Fleming-Harrington Weights

Weight = S(t)^rho * (1-S(t))^gamma

rho	gamma	Effect
0	0	Standard logrank
0	0.5	Moderate late emphasis
0	1	Strong late emphasis
1	0	Early effects
0.5	0.5	Balanced

mb() - Magirr-Burman Weights

Designed for delayed treatment effects.

mb(delay = 4, w_max = 2)  # Zero weight before delay, capped at w_max

early_zero() - Early Zero Weights

Sets weights to zero during early period.

early_zero(early_period = 6)  # Zero weight first 6 months

maxcombo() - MaxCombo Test

Combines multiple weighted logrank tests for non-proportional hazards.

data |> maxcombo(
  rho = c(0, 0, 1),
  gamma = c(0, 1, 1),
  return_corr = TRUE
)

Returns: List with:

• method: "MaxCombo"
• parameter: Combined test description
• z: Z-scores for each component test
• p_value: Combined p-value
• corr: Correlation matrix (if return_corr = TRUE)

rmst() - Restricted Mean Survival Time

Alternative endpoint for non-proportional hazards.

data |> rmst(tau = 24)  # RMST at 24 months

milestone() - Milestone Analysis

Test survival difference at fixed time point.

data |> milestone(ms_time = 12, test_type = "naive")

Simulation Functions

sim_fixed_n() - Fixed Design Simulation

Simulate multiple trials with fixed sample size.

sim_fixed_n(
  n_sim = 1000,
  sample_size = 400,
  target_event = 200,
  enroll_rate = data.frame(rate = 20, duration = 12),
  fail_rate = data.frame(
    stratum = "All",
    duration = c(4, 100),
    fail_rate = log(2)/12,
    hr = c(1, 0.7),
    dropout_rate = 0.001
  ),
  timing_type = 2,  # Time at target events
  rho_gamma = data.frame(rho = c(0, 0), gamma = c(0, 0.5))
)

timing_type options:

1. Planned study duration
2. Time at target events
3. Planned minimum follow-up
4. Max of (1) and (2)
5. Max of (2) and (3)

sim_gs_n() - Group Sequential Simulation

Simulate group sequential designs with interim analyses.

library(gsDesign2)

# Define enrollment
enroll_rate <- define_enroll_rate(
  duration = c(4, 12),
  rate = c(10, 30)
)

# Define failure rates with delayed effect
fail_rate <- define_fail_rate(
  duration = c(3, 100),
  fail_rate = log(2)/9,
  hr = c(1, 0.6),
  dropout_rate = 0.001
)

# Define cutting functions
ia1_cut <- create_cut(
  planned_calendar_time = 20,
  target_event_overall = 100,
  max_extension_for_target_event = 24
)

ia2_cut <- create_cut(
  planned_calendar_time = 32,
  target_event_overall = 200,
  min_time_after_previous_analysis = 10
)

fa_cut <- create_cut(
  planned_calendar_time = 45,
  target_event_overall = 350
)

# Run simulation
results <- sim_gs_n(
  n_sim = 1000,
  sample_size = 400,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  test = wlr,
  cut = list(ia1 = ia1_cut, ia2 = ia2_cut, fa = fa_cut),
  weight = fh(rho = 0, gamma = 0)
)

create_cut() - Create Cutting Function

cutting <- create_cut(
  planned_calendar_time = 36,
  target_event_overall = 300,
  max_extension_for_target_event = 42,
  min_n_overall = 200,
  min_followup = 12,
  min_time_after_previous_analysis = 6
)

create_test() - Create Test Function

# Create reusable test function
my_test <- create_test(wlr, weight = fh(rho = 0, gamma = 0.5))
my_test(trial_data_cut)  # Apply to cut data

Common Patterns

Delayed Treatment Effect

# 3-month delay before treatment effect kicks in
fail_rate <- data.frame(
  stratum = rep("All", 4),
  period = rep(1:2, 2),
  treatment = c(rep("control", 2), rep("experimental", 2)),
  duration = c(3, 100, 3, 100),
  rate = log(2) / c(12, 12, 12, 18)  # HR=1 then HR=0.67
)

Stratified Analysis

sim_pw_surv(
  n = 400,
  stratum = data.frame(stratum = c("Low", "High"), p = c(0.3, 0.7)),
  fail_rate = data.frame(
    stratum = rep(c("Low", "High"), each = 2),
    period = rep(1, 4),
    treatment = rep(c("control", "experimental"), 2),
    duration = rep(100, 4),
    rate = c(0.05, 0.03, 0.08, 0.05)
  )
)

Unequal Randomization (2:1)

sim_pw_surv(
  n = 300,
  block = c(rep("experimental", 2), "control")  # 2:1 ratio
)

Parallel Computation

library(future)
plan("multisession", workers = 4)

results <- sim_gs_n(
  n_sim = 10000,
  sample_size = 400,
  # ... other parameters
)

plan("sequential")  # Reset to single-threaded

Integration with gsDesign2

library(gsDesign2)

# Design a group sequential trial
design <- gs_design_ahr(
  analysis_time = c(12, 24, 36),
  alpha = 0.025,
  beta = 0.1
) |> to_integer()

# Simulate with updated bounds
sim_gs_n(
  n_sim = 1000,
  sample_size = max(design$analysis$n),
  enroll_rate = design$enroll_rate,
  fail_rate = design$fail_rate,
  test = wlr,
  cut = NULL,  # Automatically created from design
  original_design = design,
  weight = fh(rho = 0, gamma = 0)
)

Utility Functions

counting_process() - Convert to Counting Process Format

cp_data <- trial_data |>
  cut_data_by_event(200) |>
  counting_process(arm = "experimental")

fit_pwexp() - Fit Piecewise Exponential

Estimate piecewise rates from observed data.

fit_pwexp(
  data,
  intervals = c(0, 6, 12, Inf)  # Breakpoints
)

to_sim_pw_surv() - Format Conversion

Convert gsDesign2 fail_rate format to sim_pw_surv format.

fail_rate_gs2 <- define_fail_rate(...)
converted <- to_sim_pw_surv(fail_rate_gs2)
# Returns list with $fail_rate and $dropout_rate

Example Datasets

Built-in datasets for various scenarios:

• ex1_delayed_effect: Delayed treatment benefit
• ex2_delayed_effect: Alternative delayed scenario
• ex3_cure_with_ph: Cure models with proportional hazards
• ex4_belly: Complex non-proportional hazards
• ex5_widening: Diverging survival curves
• ex6_crossing: Crossing survival curves
• mb_delayed_effect: Magirr-Burman delayed effect

Best Practices

1. Reproducibility: Always set seed via SimParameters or set.seed()
2. Validation: Compare sim results to analytical solutions where possible
3. Efficiency: Use counting_process() once then multiple wlr() calls
4. Parallelization: Use plan("multisession") for large simulations
5. Non-PH: Consider MaxCombo or weighted tests for delayed effects
6. Documentation: Record all simulation parameters for regulatory submissions