pvisim: parameters, simulator, claims records, tests; calibration of all worlds to Medicare targets
- latent-severity DGP with physicians (skill, style), policies A/B1/B2/sequential, common random numbers - sharp null exact at the patient level (tested) - 01_calibrate.R: joint logit-scale update; all worlds match early PVI 3.2%, late PVI 22.5/3.6%, CLTI 7.8%, death 35% - W4 deferral target 15% (trial-like crossover); post-CLTI PVI lowered so late PVI is mostly elective Co-Authored-By: Claude Opus 5.5 <[email protected]>
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Claude Opus 5.5
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# Every number that defines the simulated world. Analysis plan v1.0, section 5.
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# Time unit: month. Hazards are monthly, on the logit scale.
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# Starting values are placeholders, to be tuned by calibration
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# (simulations/01_calibrate.R, targets in plan section 5.4).
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# Physician traits (skill k, style s) and patient frailty F are standard normal;
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# their influence is set by the coefficients that multiply them.
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size:
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n_patients: 50000
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n_physicians: 1500
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panel_sdlog: 1.0 # lognormal panel weights; median panel about 20 at n = 50,000
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months: 60 # follow-up months 0-59
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window: 6 # strategy A: PVI in months 0-5
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physician:
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rho_skill_style: -0.3 # corr(k, s). Has no effect unless skill matters (world W1)
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obl_intercept: 0.0 # logit P(office-based lab / ASC); about 50% of physicians
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obl_style: 0.8 # high-style physicians more often work in OBL/ASC
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patient:
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age_min: 66
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age_shape: 2.5 # age = 66 + Gamma(shape, scale); mean about 76
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age_scale: 4.0
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p_female: 0.50
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p_black: 0.10
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# comorbidities: logit P = qlogis(prev) + age_coef * (age - 76) / 10 + frailty_coef * F
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comorbidity:
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diabetes: {prev: 0.40, age_coef: -0.10, frailty_coef: 0.4}
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ckd: {prev: 0.20, age_coef: 0.30, frailty_coef: 0.5}
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chf: {prev: 0.20, age_coef: 0.40, frailty_coef: 0.6}
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copd: {prev: 0.25, age_coef: 0.10, frailty_coef: 0.4}
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prior_cvd: {prev: 0.45, age_coef: 0.20, frailty_coef: 0.3}
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smoking: {prev: 0.25, age_coef: -0.40}
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year_min: 2015
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year_max: 2019
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severity: # latent Z_t: walking limitation / ABI on a standard scale
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z0_diabetes: 0.3
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z0_smoking: 0.3
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z0_ckd: 0.2
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z0_sd: 1.0
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r0: 0.02 # mean drift per month
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r_diabetes: 0.01
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r_smoking: 0.01
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r_sd: 0.01 # patient-specific progression rate
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eps_sd: 0.05 # month-to-month noise
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treatment:
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window: # elective PVI hazard, months 0-5, among the eligible
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intercept: -5.4
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z: 0.9 # confounding by indication
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frailty: -0.3 # frail patients are less often offered PVI
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age10: -0.3
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diabetes: 0.1
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ckd: -0.1
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smoking: 0.1
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style: 0.5 # physician style; world W2 sets it to the strong level
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obl: 0.3
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year: 0.05 # per calendar year after year_min
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deferred: # elective PVI hazard from month 6 on (deferral / crossover)
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intercept: -7.2
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z: 0.0 # world W4 makes deferral severity-driven
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style: 0.5
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obl: 0.3
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effect: # what a PVI does. Set by the truth scenario
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relief: 0.0 # immediate drop in Z at PVI
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kappa: 0.0 # extra Z drift per month after PVI
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skill_kappa: 0.0 # kappa * exp(-skill_kappa * k): skill slows post-PVI progression
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outcomes:
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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}
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clti: {intercept: -6.6, z: 0.6, diabetes: 0.4, ckd: 0.3, smoking: 0.3, frailty: 0.2}
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clti_false_code: {intercept: -9.0, style: 0.0} # factor 3 sets style
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amputation: {intercept: -8.5, clti: 3.0, diabetes: 0.4, ckd: 0.4, z: 0.2}
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bypass: {intercept: -7.8, z: 0.5}
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repeat_revasc: {intercept: -5.3, skill: 0.0, style: 0.0, z: 0.2} # only after the index PVI
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clti_revasc: {intercept: -4.5} # PVI after CLTI (not elective); most CLTI patients get bypass, tibial work, amputation or nothing
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negative_controls: # never depend on PVI
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nco1: {intercept: -6.4, frailty: 0.5, smoking: 0.4, age10: 0.3} # hip fracture / pneumonia
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nco2: {intercept: -7.1, style: 0.8, venous: 0.5} # varicose-vein ablation
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nce: {intercept: -5.7, style: 0.8, frailty: -0.2} # low-value procedure, months 0-5
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measurement: # what the analyst sees. World W5 switches to claims proxies
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severity: exact # exact: analyst sees Z_t. proxy: only visit/testing counts
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proxy_intercept: -1.0 # monthly count ~ Poisson(exp(proxy_intercept + proxy_z * Z_t))
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proxy_z: 0.5
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proxy_window: 3 # observed severity = log1p(sum of counts over the last 3 months)
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frailty_reliability: 1.0 # frailty index = sqrt(rel) * F + sqrt(1 - rel) * noise
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smoking: true # true: analyst sees true smoking. coded: sensitivity / specificity below
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smoking_sens: 0.6
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smoking_spec: 0.98
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worlds: # one factor at a time (plan section 5.2, D24). Overrides on the default.
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# Intercepts are then re-solved per world by calibration, so every world
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# matches the same Medicare targets unless `calibration_targets` says otherwise.
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W0: {}
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W1:
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effect: {skill_kappa: 0.5}
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outcomes: {repeat_revasc: {skill: -0.6}}
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W1_rho0:
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physician: {rho_skill_style: 0.0}
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effect: {skill_kappa: 0.5}
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outcomes: {repeat_revasc: {skill: -0.6}}
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W2:
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treatment: {window: {style: 1.6}, deferred: {style: 1.6}}
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W3:
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treatment: {window: {style: 1.6}, deferred: {style: 1.6}}
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outcomes: {clti_false_code: {style: 0.8}, repeat_revasc: {style: 0.6}}
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W4: # deferral common (as in trial conservative arms) and severity-driven
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treatment: {deferred: {z: 0.8}}
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calibration_targets: {late_not: 0.15}
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W5:
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measurement: {severity: proxy, frailty_reliability: 0.4, smoking: coded}
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WR:
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effect: {skill_kappa: 0.5}
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treatment: {window: {style: 1.6}, deferred: {style: 1.6, z: 0.8}} # deferral at the Sorber rate, but severity-driven
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outcomes: {repeat_revasc: {skill: -0.6, style: 0.6}, clti_false_code: {style: 0.8}}
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measurement: {severity: proxy, frailty_reliability: 0.4, smoking: coded}
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scenarios: # truth scenarios (plan section 5.3, D34)
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"null": {effect: {relief: 0.0, kappa: 0.0}} # sharp disease null
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harm: {effect: {relief: 0.3, kappa: 0.03}} # tuned to RD about +5 pp (calibration)
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benefit_harm: {effect: {relief: 1.2, kappa: 0.04}} # curves cross; tuned in calibration
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