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Core simtrial package functions for time-to-event clinical trial simulation. Use when generating survival data, performing weighted logrank tests, or running TTE simulations.
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Bayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.
Foundational knowledge for writing current PyMC models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.
Foundational knowledge for writing modern Stan models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.
Group sequential design methods for interim analyses, alpha spending, and futility stopping. Use when designing trials with interim looks or implementing spending functions.
Core Mediana package functions for Clinical Scenario Evaluation (CSE). Use when designing data models, analysis models, evaluation models, and running comprehensive trial simulations.
Deep methodology knowledge for MAIC including assumptions, weight diagnostics, ESS interpretation, and anchored vs unanchored decisions. Use when conducting or reviewing MAIC analyses.
| 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 by Merck provides fast, extensible clinical trial simulation for time-to-event endpoints. Key features:
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 stratumenroll_time: Calendar time of enrollmenttreatment: Treatment assignment ("control" or "experimental")fail_time: Time from enrollment to eventdropout_time: Time from enrollment to dropoutcte: 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 originGenerates 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
)
)
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
)
)
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 at specified calendar time.
trial_data <- sim_pw_surv(n = 400) |>
cut_data_by_date(cut_date = 24) # 24 months
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
)
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 estimatese: Standard errorz: Z-score (negative favors experimental)info: Statistical informationinfo0: Information under nullWeight = 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 |
Designed for delayed treatment effects.
mb(delay = 4, w_max = 2) # Zero weight before delay, capped at w_max
Sets weights to zero during early period.
early_zero(early_period = 6) # Zero weight first 6 months
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 descriptionz: Z-scores for each component testp_value: Combined p-valuecorr: Correlation matrix (if return_corr = TRUE)Alternative endpoint for non-proportional hazards.
data |> rmst(tau = 24) # RMST at 24 months
Test survival difference at fixed time point.
data |> milestone(ms_time = 12, test_type = "naive")
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:
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)
)
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 reusable test function
my_test <- create_test(wlr, weight = fh(rho = 0, gamma = 0.5))
my_test(trial_data_cut) # Apply to cut data
# 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
)
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)
)
)
sim_pw_surv(
n = 300,
block = c(rep("experimental", 2), "control") # 2:1 ratio
)
library(future)
plan("multisession", workers = 4)
results <- sim_gs_n(
n_sim = 10000,
sample_size = 400,
# ... other parameters
)
plan("sequential") # Reset to single-threaded
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)
)
cp_data <- trial_data |>
cut_data_by_event(200) |>
counting_process(arm = "experimental")
Estimate piecewise rates from observed data.
fit_pwexp(
data,
intervals = c(0, 6, 12, Inf) # Breakpoints
)
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
Built-in datasets for various scenarios:
ex1_delayed_effect: Delayed treatment benefitex2_delayed_effect: Alternative delayed scenarioex3_cure_with_ph: Cure models with proportional hazardsex4_belly: Complex non-proportional hazardsex5_widening: Diverging survival curvesex6_crossing: Crossing survival curvesmb_delayed_effect: Magirr-Burman delayed effectset.seed()