illness-death

name

illness-death

description

Guide users through simulating clinical trials using the illness-death model with response. Use this skill when the user asks about: multi-state models for oncology trials, simulating correlated OS/PFS/ORR endpoints, transition rates between disease states, illness-death model calibration, building ADTTE datasets from simulation, analysis cut date determination, or theoretical survival curves from transition rates.

Illness-Death Model with Response

This skill covers the illness-death model implementation in gMCPLite/inst/simulation/, which simulates correlated OS, PFS, and ORR endpoints for oncology trial design.

Source files

The model lives in inst/simulation/ within the gMCPLite package:

• sim_illness_death.R — build_transition_rates(), sim_illness_death(), and internal CDF helpers (.pfs_cdf(), .os_cdf())
• cut_illness_death.R — get_analysis_dates(), cut_illness_death()

Load via source():

source(file.path(rprojroot::find_root("DESCRIPTION"), "inst", "simulation", "sim_illness_death.R"))
source(file.path(rprojroot::find_root("DESCRIPTION"), "inst", "simulation", "cut_illness_death.R"))

Model overview

The illness-death model has four states with piecewise exponential transition times:

  State 0 ─── response ───> State 1 (responded)
  (alive,     prog_0 ────> State 2 (progressed)
   no resp)   death_0 ───> State 3 (dead)

  State 1 ─── prog_1 ────> State 2
  (responded)  death_1 ───> State 3

  State 2 ─── death_2 ───> State 3
  (progressed)

Derived endpoints

• PFS: Time from randomization to progression or death (whichever first)
• OS: Time from randomization to death
• ORR: Binary indicator of response before progression/death
• TTR: Time to response

Key property: endpoint correlation

Because PFS and OS share the same underlying state transitions, they are automatically correlated — a patient who progresses early tends to die earlier. This makes the model more realistic than independently simulating PFS and OS.

Key functions

build_transition_rates()

Calibrates transition rates from clinically interpretable inputs (median PFS/OS, ORR, hazard ratios). Uses numerical root-finding so that control arm marginal PFS and OS medians match the inputs exactly.

sim_illness_death()

Simulates enrollment, randomization, and multi-state outcomes. Uses simtrial::rpwexp() for piecewise exponential transition times and simtrial::rpwexp_enroll() for enrollment.

get_analysis_dates()

Computes calendar cut dates from timing rules: minimum follow-up after FPE, event-driven triggers with caps, and maximum extensions.

cut_illness_death()

Applies administrative censoring at a cut date and produces ADTTE-format datasets with OS, PFS, TTR, and ORR endpoints.

Internal CDF helpers

• .pfs_cdf(t, ...) — Marginal PFS CDF from transition rates
• .os_cdf(t, ...) — Marginal OS CDF from transition rates
• .conv2_cdf(t, a, b) — CDF of sum of two independent exponentials
• .conv3_cdf(t, a, b, c) — CDF of sum of three independent exponentials

These allow computing theoretical survival curves without simulation.

Workflow patterns

For detailed code templates, read references/code_patterns.md.

Topics covered:

• Building transition rates from clinical assumptions
• Simulating a single trial
• Analysis cut date determination (calendar + event-driven)
• Cutting data to ADTTE format
• Computing nominal p-values (logrank, stratified logrank, risk difference)
• Piecewise transition rate modification (progression, response)
• Separate design vs simulation effect sizes
• Theoretical survival curves from transition rates
• Prevalence-weighted overall population curves
• Multiple strata with different treatment effects

Important design considerations

• Piecewise rates: build_transition_rates() produces constant rates. To model piecewise PFS or response, modify the output by splitting rows into multiple time periods with different rates and durations.

• Design vs simulation effects: Build separate transition rate tables with different HRs for design (sample size) and simulation (operating characteristics). Weaker simulation HRs reveal realistic power under conservative assumptions.

• Response rate timing: Multiply the base response rate for early periods (e.g., 2.25× for $t < 6$ months) and reduce it later (e.g., 10% of base) to model response windows.

• Calibration: build_transition_rates() numerically calibrates control arm rates to match specified median PFS and OS. Experimental arm rates are derived by scaling with HRs, then re-calibrating response rate for the target ORR.

• death_wo_prog_rate: Baseline rate of death without prior progression (default 0.02/month). This controls the fraction of patients who die without progressing.

• responder_prog_ratio: Ratio of progression rate for responders vs non-responders (default 0.5). Lower values mean responders have longer PFS.

• Piecewise rates: transition_rate supports multiple rows per (stratum, treatment, transition) with different durations, enabling time-varying hazards.

• Enrollment: Uses simtrial::rpwexp_enroll() — specify ramp-up as increasing rates over duration periods.

• FPE-relative timing: Analysis cut dates are computed relative to the final patient enrolled (FPE), not study start.

• Event-driven timing with caps: get_analysis_dates() supports both calendar-based and event-driven triggers with maximum extension caps.

• Stratified analyses: For overall population p-values, use strata() in survdiff() (logrank) or sample-size-weighted stratum-specific risk differences (ORR). Beware that survdiff() with strata() returns obs/exp as matrices — sum across strata columns.