# Every number that defines the simulated world. Analysis plan v1.0, section 5.
# Time unit: month. Hazards are monthly, on the logit scale.
# Starting values are placeholders, to be tuned by calibration
# (simulations/01_calibrate.R, targets in plan section 5.4).
# Physician traits (skill k, style s) and patient frailty F are standard normal;
# their influence is set by the coefficients that multiply them.

size:
  n_patients: 50000
  n_physicians: 1500
  panel_sdlog: 1.0          # lognormal panel weights; median panel about 20 at n = 50,000
  months: 60                # follow-up months 0-59
  window: 6                 # strategy A: PVI in months 0-5

physician:
  rho_skill_style: -0.3     # corr(k, s). Has no effect unless skill matters (world W1)
  obl_intercept: 0.0        # logit P(office-based lab / ASC); about 50% of physicians
  obl_style: 0.8            # high-style physicians more often work in OBL/ASC

patient:
  age_min: 66
  age_shape: 2.5            # age = 66 + Gamma(shape, scale); mean about 76
  age_scale: 4.0
  p_female: 0.50
  p_black: 0.10
  # comorbidities: logit P = qlogis(prev) + age_coef * (age - 76) / 10 + frailty_coef * F
  comorbidity:
    diabetes:  {prev: 0.40, age_coef: -0.10, frailty_coef: 0.4}
    ckd:       {prev: 0.20, age_coef:  0.30, frailty_coef: 0.5}
    chf:       {prev: 0.20, age_coef:  0.40, frailty_coef: 0.6}
    copd:      {prev: 0.25, age_coef:  0.10, frailty_coef: 0.4}
    prior_cvd: {prev: 0.45, age_coef:  0.20, frailty_coef: 0.3}
  smoking: {prev: 0.25, age_coef: -0.40}
  year_min: 2015
  year_max: 2019

severity:                    # latent Z_t: walking limitation / ABI on a standard scale
  z0_diabetes: 0.3
  z0_smoking: 0.3
  z0_ckd: 0.2
  z0_sd: 1.0
  r0: 0.02                   # mean drift per month
  r_diabetes: 0.01
  r_smoking: 0.01
  r_sd: 0.01                 # patient-specific progression rate
  eps_sd: 0.05               # month-to-month noise

treatment:
  window:                    # elective PVI hazard, months 0-5, among the eligible
    intercept: -5.4
    z: 0.9                   # confounding by indication
    frailty: -0.3            # frail patients are less often offered PVI
    age10: -0.3
    diabetes: 0.1
    ckd: -0.1
    smoking: 0.1
    style: 0.5               # physician style; world W2 sets it to the strong level
    obl: 0.3
    year: 0.05               # per calendar year after year_min
  deferred:                  # elective PVI hazard from month 6 on (deferral / crossover)
    intercept: -7.2
    z: 0.0                   # world W4 makes deferral severity-driven
    style: 0.5
    obl: 0.3

effect:                      # what a PVI does. Set by the truth scenario
  relief: 0.0                # immediate drop in Z at PVI
  kappa: 0.0                 # extra Z drift per month after PVI
  skill_kappa: 0.0           # kappa * exp(-skill_kappa * k): skill slows post-PVI progression

outcomes:
  death:  {intercept: -5.0, age10: 0.7, chf: 0.4, ckd: 0.4, copd: 0.3, frailty: 0.4, z: 0.1, clti: 0.8}
  clti:   {intercept: -6.6, z: 0.6, diabetes: 0.4, ckd: 0.3, smoking: 0.3, frailty: 0.2}
  clti_false_code: {intercept: -9.0, style: 0.0}     # factor 3 sets style
  amputation: {intercept: -8.5, clti: 3.0, diabetes: 0.4, ckd: 0.4, z: 0.2}
  bypass: {intercept: -7.8, z: 0.5}
  repeat_revasc: {intercept: -5.3, skill: 0.0, style: 0.0, z: 0.2}   # only after the index PVI
  clti_revasc: {intercept: -4.5}                      # PVI after CLTI (not elective); most CLTI patients get bypass, tibial work, amputation or nothing

negative_controls:           # never depend on PVI
  nco1: {intercept: -6.75, frailty: 0.5, smoking: 0.4, age10: 0.3, z: 0.3}   # hip fracture / pneumonia; z = PVI-free severity (D53)
  nco2: {intercept: -7.1, style: 0.8, venous: 0.5}                  # varicose-vein ablation
  nce:  {intercept: -5.7, style: 0.8, frailty: -0.2}                # low-value procedure, months 0-5

measurement:                 # what the analyst sees. World W5 switches to claims proxies
  severity: exact            # exact: analyst sees Z_t. proxy: only visit/testing counts
  proxy_intercept: -1.0      # monthly count ~ Poisson(exp(proxy_intercept + proxy_z * Z_t))
  proxy_z: 0.5
  proxy_window: 3            # observed severity = log1p(sum of counts over the last 3 months)
  frailty_reliability: 1.0   # frailty index = sqrt(rel) * F + sqrt(1 - rel) * noise
  smoking: true              # true: analyst sees true smoking. coded: sensitivity / specificity below
  smoking_sens: 0.6
  smoking_spec: 0.98

worlds:                      # one factor at a time (plan section 5.2, D24). Overrides on the default.
                             # Intercepts are then re-solved per world by calibration, so every world
                             # matches the same Medicare targets unless `calibration_targets` says otherwise.
  W0: {}
  W1:
    effect: {skill_kappa: 0.5}
    outcomes: {repeat_revasc: {skill: -0.6}}
  W1_rho0:
    physician: {rho_skill_style: 0.0}
    effect: {skill_kappa: 0.5}
    outcomes: {repeat_revasc: {skill: -0.6}}
  W2:
    treatment: {window: {style: 1.6}, deferred: {style: 1.6}}
  W3:
    treatment: {window: {style: 1.6}, deferred: {style: 1.6}}
    outcomes: {clti_false_code: {style: 0.8}, repeat_revasc: {style: 0.6}}
  W4:                        # deferral common (as in trial conservative arms) and severity-driven
    treatment: {deferred: {z: 0.8}}
    calibration_targets: {late_not: 0.15}
  W5:
    measurement: {severity: proxy, frailty_reliability: 0.4, smoking: coded}
  WR:
    effect: {skill_kappa: 0.5}
    treatment: {window: {style: 1.6}, deferred: {style: 1.6, z: 0.8}}   # deferral at the Sorber rate, but severity-driven
    outcomes: {repeat_revasc: {skill: -0.6, style: 0.6}, clti_false_code: {style: 0.8}}
    measurement: {severity: proxy, frailty_reliability: 0.4, smoking: coded}

scenarios:                   # truth scenarios (plan section 5.3, D34)
  "null": {effect: {relief: 0.0, kappa: 0.0}}           # sharp disease null
  harm: {effect: {relief: 0.3, kappa: 0.039}}         # W0: disease-composite RD(A-B1) +5.1 pp at 60 months
  benefit_harm: {effect: {relief: 2.0, kappa: 0.075}} # W0: RD -1.2 pp at 24 months, +3.1 pp at 60
  # tuned in W0 with simulations/explore_scenarios.R (2e5 patients); the same mechanism is used in every world