"""
HC-ONC-007: Leukemia Synthetic Dataset — Simulation Engine
XpertSystems.ai | SKU: HC-ONC-007 | Version 1.0.0 | April 2026
HIPAA-Safe Synthetic Data — No Real Patient Records

Generates biologically consistent leukemia patient cohort covering:
- AML, ALL, CML, CLL, APL subtypes with WHO 2022 classification
- ELN 2022 cytogenetic/molecular risk stratification
- Treatment protocols: 7+3, HyperCVAD, Venetoclax+AZA, ATRA+ATO, TKI
- MRD response trajectory modeling
- Bone marrow transplant (HSCT) outcomes calibrated to CIBMTR benchmarks

Usage:
    python hc_onc_007_simulation_engine.py --n_patients 25000 --seed 42 --output_dir ./output
"""

import argparse
import os
import json
import numpy as np
import pandas as pd
from scipy.stats import weibull_min
from datetime import datetime, timedelta
import uuid

# ─────────────────────────────────────────────────────────────────────────────
# CONSTANTS & CLINICAL BENCHMARKS
# ─────────────────────────────────────────────────────────────────────────────

SUBTYPE_DIST = {
    "AML": 0.35, "ALL": 0.25, "CML": 0.20, "CLL": 0.15, "APL": 0.05
}

# ELN 2022 AML risk distribution (among AML+APL)
ELN_RISK_DIST = {
    "Favorable": 0.30, "Intermediate": 0.40, "Adverse": 0.30
}

# CR rates by subtype (landmark trial calibration)
CR_RATE = {
    "AML_Favorable": 0.82, "AML_Intermediate": 0.65, "AML_Adverse": 0.45,
    "ALL_Pediatric": 0.95, "ALL_Adult": 0.85, "APL": 0.97,
    "CML": None,  # CML uses MMR endpoint
    "CLL": None,  # CLL uses IWCLL response
}

# 5-year OS benchmarks (Weibull lambda calibration)
OS_LAMBDA = {
    "AML_Favorable": 60.0, "AML_Intermediate": 36.0, "AML_Adverse": 14.0,
    "ALL_Pediatric": 110.0, "ALL_Adult": 40.0, "APL": 88.0,
    "CML": 130.0, "CLL": 120.0
}

# BMT acute GvHD Grade II-IV incidence (CIBMTR 2019-2023)
AGVHD_RATE = {"MRD": 0.30, "MUD": 0.45, "MMUD": 0.55, "Haploidentical": 0.48, "Cord_Blood": 0.35}

# NRM at 1-year (CIBMTR benchmarks)
NRM_RATE = {"MRD": 0.10, "MUD": 0.17, "MMUD": 0.22, "Haploidentical": 0.18, "Cord_Blood": 0.20}

INDUCTION_AML_REGIMENS = [
    "7+3_Daunorubicin", "7+3_Idarubicin", "CPX-351", "Venetoclax+AZA",
    "Venetoclax+LDAC", "Midostaurin+7+3", "Enasidenib+AZA", "Ivosidenib+AZA",
    "Glasdegib+LDAC", "BSC"
]
INDUCTION_ALL_REGIMENS = [
    "CALGB_10403", "HyperCVAD_A_B", "BFM_Protocol", "GRAALL_2005",
    "UKALL14", "Blinatumomab+Chemo", "Inotuzumab_Ozogamicin+Chemo",
    "Dasatinib+Steroids", "Ponatinib+Chemo"
]
CML_REGIMENS = [
    "Imatinib_400mg", "Dasatinib_100mg", "Nilotinib_300mg_BID",
    "Bosutinib_400mg", "Ponatinib_45mg", "Asciminib_40mg_BID"
]
CLL_REGIMENS = [
    "Watch_and_Wait", "FCR", "BR", "Ibrutinib", "Acalabrutinib",
    "Venetoclax_Obinutuzumab", "Venetoclax_Rituximab", "Pirtobrutinib",
    "Lisocabtagene_Maraleucel"
]

DONOR_TYPES = ["MRD", "MUD", "MMUD", "Haploidentical", "Cord_Blood"]
DONOR_DIST = [0.35, 0.38, 0.12, 0.10, 0.05]

CONDITIONING = {
    "MAC": ["BuCy", "TBI_Cy", "FluBu4", "Cy_TBI"],
    "RIC": ["FluBu2", "FluMel140", "TBI_Flu_200"],
    "NMA": ["Flu_Cy_TBI_200", "Flu_Cy"]
}

GVHD_PROPHYLAXIS = [
    "Tacrolimus_MTX", "CsA_MTX", "PT_Cy_Tacrolimus_MMF",
    "PT_Cy_Alone", "ATG_CsA_MTX"
]

# ─────────────────────────────────────────────────────────────────────────────
# HELPER FUNCTIONS
# ─────────────────────────────────────────────────────────────────────────────

def clamp(val, lo, hi):
    return max(lo, min(hi, val))

def weibull_sample(rng, k, lam, size=1):
    """Sample from Weibull distribution."""
    return weibull_min.rvs(k, scale=lam, size=size, random_state=rng)

def choice(rng, options, probs=None):
    probs = np.array(probs) if probs else None
    if probs is not None:
        probs = probs / probs.sum()
    return rng.choice(options, p=probs)

def lognorm(rng, mu, sigma, lo, hi):
    v = np.exp(rng.normal(mu, sigma))
    return clamp(v, lo, hi)

# ─────────────────────────────────────────────────────────────────────────────
# MODULE 1: PATIENT DEMOGRAPHICS
# ─────────────────────────────────────────────────────────────────────────────

def generate_demographics(rng, n):
    """Generate demographic attributes."""
    subtypes = rng.choice(list(SUBTYPE_DIST.keys()), size=n, p=list(SUBTYPE_DIST.values()))

    ages = []
    for st in subtypes:
        if st == "ALL":
            # Bimodal: pediatric peak ~6yr, adult peak ~35yr
            if rng.random() < 0.55:  # 55% pediatric ALL
                a = rng.normal(6, 3)
            else:
                a = rng.normal(35, 12)
        elif st == "CLL":
            a = rng.normal(68, 9)
        elif st == "CML":
            a = rng.normal(52, 14)
        elif st == "APL":
            a = rng.normal(44, 13)
        else:  # AML
            a = rng.normal(62, 14)
        ages.append(clamp(a, 0.5, 95))

    ages = np.array(ages)
    sex = rng.choice(["Male", "Female"], size=n, p=[0.55, 0.45])
    race = rng.choice(
        ["White", "Black", "Hispanic", "Asian", "Other"],
        size=n, p=[0.65, 0.12, 0.13, 0.08, 0.02]
    )
    ecog = rng.choice([0, 1, 2, 3, 4], size=n, p=[0.25, 0.35, 0.25, 0.12, 0.03])

    return pd.DataFrame({
        "patient_id": [str(uuid.UUID(int=rng.integers(0, 2**128))) for _ in range(n)],
        "leukemia_type": subtypes,
        "age_at_diagnosis": np.round(ages, 1),
        "pediatric_flag": (ages < 18).astype(int),
        "sex": sex,
        "race": race,
        "ecog_performance_status": ecog,
    })

# ─────────────────────────────────────────────────────────────────────────────
# MODULE 2: DISEASE CHARACTERISTICS
# ─────────────────────────────────────────────────────────────────────────────

def generate_disease_characteristics(rng, demo):
    n = len(demo)
    rows = []

    for i, row in demo.iterrows():
        st = row["leukemia_type"]
        age = row["age_at_diagnosis"]
        d = {}

        # WHO 2022 / FAB classification
        if st == "AML":
            d["who_2022_classification"] = choice(rng, [
                "AML_NPM1", "AML_FLT3_ITD", "AML_CEBPA_biallelic",
                "AML_NUP98", "AML_t8_21", "AML_inv16", "AML_MRC",
                "AML_TP53", "AML_IDH2", "AML_IDH1", "AML_NOS"
            ])
            d["fab_classification"] = choice(rng, ["M0","M1","M2","M3","M4","M5","M6","M7"],
                                             [0.05,0.15,0.25,0.08,0.20,0.15,0.05,0.07])
        elif st == "APL":
            d["who_2022_classification"] = "APL_PML_RARA"
            d["fab_classification"] = "M3"
        elif st == "ALL":
            is_ped = age < 18
            d["who_2022_classification"] = choice(rng,
                ["B-ALL_ETV6_RUNX1", "B-ALL_Hyperdiploidy", "B-ALL_Hypodiploidy",
                 "B-ALL_BCR_ABL1", "B-ALL_KMT2A", "B-ALL_NOS",
                 "T-ALL_NOS", "T-ALL_ETP"],
                [0.25 if is_ped else 0.05,
                 0.25 if is_ped else 0.10,
                 0.02, 0.05 if is_ped else 0.25,
                 0.08, 0.20, 0.10, 0.05])
            d["fab_classification"] = choice(rng, ["L1","L2","L3"], [0.50,0.40,0.10])
        elif st == "CML":
            d["who_2022_classification"] = choice(rng,
                ["CML_CP", "CML_AP", "CML_BP_Myeloid", "CML_BP_Lymphoid"],
                [0.85, 0.08, 0.05, 0.02])
            d["fab_classification"] = "N/A"
        else:  # CLL
            d["who_2022_classification"] = choice(rng,
                ["CLL_IGHV_Mutated", "CLL_IGHV_Unmutated", "SLL"], [0.50, 0.40, 0.10])
            d["fab_classification"] = "N/A"

        d["disease_phase"] = choice(rng,
            ["Newly_Diagnosed", "Relapsed", "Refractory", "Relapsed_Refractory", "MRD_Positive"],
            [0.70, 0.12, 0.08, 0.07, 0.03])
        d["de_novo_vs_secondary"] = choice(rng,
            ["de_novo", "secondary_to_MDS", "therapy_related"],
            [0.80, 0.12, 0.08]) if st in ["AML","APL"] else "N/A"

        # CBC at diagnosis
        if st in ["AML","APL"]:
            d["blast_pct_bone_marrow"] = clamp(rng.normal(55, 20), 20, 98)
            d["blast_pct_peripheral_blood"] = clamp(d["blast_pct_bone_marrow"] * rng.uniform(0.3, 0.9), 0, 95)
            d["wbc_k_ul"] = lognorm(rng, 3.2, 1.1, 0.5, 400)
        elif st == "ALL":
            d["blast_pct_bone_marrow"] = clamp(rng.normal(75, 18), 25, 99)
            d["blast_pct_peripheral_blood"] = clamp(d["blast_pct_bone_marrow"] * rng.uniform(0.4, 0.95), 0, 99)
            d["wbc_k_ul"] = lognorm(rng, 3.5, 1.3, 0.5, 500)
        elif st == "CML":
            d["blast_pct_bone_marrow"] = clamp(rng.normal(8, 5), 0, 19)
            d["blast_pct_peripheral_blood"] = clamp(rng.normal(4, 3), 0, 15)
            d["wbc_k_ul"] = lognorm(rng, 5.0, 0.9, 20, 500)
        else:  # CLL
            d["blast_pct_bone_marrow"] = "N/A"
            d["blast_pct_peripheral_blood"] = "N/A"
            d["wbc_k_ul"] = lognorm(rng, 4.2, 0.8, 5, 300)

        d["hemoglobin_g_dl"] = round(clamp(rng.normal(8.5, 2.1), 4.0, 16.0), 1)
        d["platelets_k_ul"] = round(lognorm(rng, 4.8, 0.9, 5, 800), 0)
        d["ldh_u_l"] = round(lognorm(rng, 6.2, 0.7, 100, 8000), 0)
        d["bone_marrow_cellularity_pct"] = int(clamp(rng.normal(80, 12), 40, 100))
        d["auer_rods_flag"] = int(st == "AML" and rng.random() < 0.30)
        d["splenomegaly_flag"] = int(
            (st == "CML" and rng.random() < 0.55) or
            (st == "CLL" and rng.random() < 0.35) or
            (st in ["AML","ALL"] and rng.random() < 0.15)
        )
        d["lymphadenopathy_flag"] = int(
            (st == "CLL" and rng.random() < 0.65) or
            (st == "ALL" and rng.random() < 0.40) or
            (st in ["AML","APL"] and rng.random() < 0.05)
        )
        d["cns_involvement_flag"] = int(
            (st == "ALL" and rng.random() < 0.15) or
            (st == "AML" and rng.random() < 0.03)
        )

        rows.append(d)

    return pd.DataFrame(rows, index=demo.index)

# ─────────────────────────────────────────────────────────────────────────────
# MODULE 3: CYTOGENETICS & MOLECULAR MARKERS
# ─────────────────────────────────────────────────────────────────────────────

def generate_molecular(rng, demo):
    n = len(demo)
    rows = []

    for i, row in demo.iterrows():
        st = row["leukemia_type"]
        age = row["age_at_diagnosis"]
        d = {}

        # Cytogenetics
        d["t_8_21_flag"] = int(st == "AML" and rng.random() < 0.07)
        d["inv_16_flag"] = int(st == "AML" and rng.random() < 0.05)
        d["t_15_17_pml_rara_flag"] = int(st == "APL")
        d["t_9_22_bcr_abl1_flag"] = int(
            st == "CML" or
            (st == "ALL" and rng.random() < (0.05 if age < 18 else 0.25))
        )
        d["bcr_abl1_transcript"] = (
            "p210" if st == "CML" else
            "p190" if (st == "ALL" and d["t_9_22_bcr_abl1_flag"]) else "None"
        )
        d["inv_3_flag"] = int(st == "AML" and rng.random() < 0.03)
        d["del_7_monosomy7_flag"] = int(st == "AML" and rng.random() < 0.08)
        d["del_5q_monosomy5_flag"] = int(st == "AML" and rng.random() < 0.07)
        d["complex_karyotype_flag"] = int(st == "AML" and rng.random() < 0.12)
        d["monosomal_karyotype_flag"] = int(st == "AML" and rng.random() < 0.08)

        d["t_4_11_kmt2a_flag"] = int(st == "ALL" and age < 2 and rng.random() < 0.70)
        d["t_12_21_etv6_runx1_flag"] = int(st == "ALL" and age < 18 and rng.random() < 0.25)
        d["t_1_19_tcf3_pbx1_flag"] = int(st == "ALL" and rng.random() < 0.05)
        d["hypodiploidy_flag"] = int(st == "ALL" and rng.random() < 0.05)
        d["hyperdiploidy_flag"] = int(st == "ALL" and age < 18 and rng.random() < 0.25)
        d["ikzf1_deletion_flag"] = int(st == "ALL" and rng.random() < 0.15)

        # AML molecular
        cbf_aml = d["t_8_21_flag"] or d["inv_16_flag"]
        d["npm1_mutation"] = choice(rng, ["WT", "Mutant"],
            [0.65, 0.35] if st == "AML" else [1.0, 0.0])
        d["flt3_itd_status"] = choice(rng,
            ["Negative", "Low_Allelic_Ratio", "High_Allelic_Ratio"],
            [0.72, 0.14, 0.14] if st == "AML" else [1.0, 0.0, 0.0])
        d["flt3_itd_allelic_ratio"] = (
            round(lognorm(rng, -0.5, 0.7, 0.01, 10.0), 3)
            if d["flt3_itd_status"] != "Negative" else 0.0
        )
        d["flt3_itd_high_ratio"] = int(d["flt3_itd_allelic_ratio"] >= 0.5)
        d["flt3_tkd_d835_status"] = choice(rng, ["Negative","Positive"],
            [0.93, 0.07] if st == "AML" else [1.0, 0.0])

        d["idh1_mutation"] = choice(rng, ["WT","R132H","R132C","R132S"],
            [0.92, 0.05, 0.02, 0.01] if st == "AML" else [1.0, 0.0, 0.0, 0.0])
        d["idh2_mutation"] = choice(rng, ["WT","R140Q","R172K","R172M"],
            [0.87, 0.07, 0.04, 0.02] if st == "AML" else [1.0, 0.0, 0.0, 0.0])
        d["dnmt3a_mutation"] = choice(rng, ["WT","R882H","R882C","Other"],
            [0.75, 0.12, 0.08, 0.05] if st == "AML" else [1.0, 0.0, 0.0, 0.0])
        d["tet2_mutation"] = int(st == "AML" and rng.random() < 0.20)
        d["asxl1_mutation"] = int(st == "AML" and rng.random() < 0.15)
        d["runx1_mutation"] = int(st == "AML" and rng.random() < 0.12)
        d["tp53_mutation"] = int(
            (st == "AML" and rng.random() < 0.10) or
            (st == "CLL" and rng.random() < 0.10)
        )
        d["cebpa_mutation"] = choice(rng, ["WT","Biallelic","Monoallelic"],
            [0.90, 0.05, 0.05] if st == "AML" else [1.0, 0.0, 0.0])
        d["sf3b1_mutation"] = int(st == "AML" and rng.random() < 0.08)
        d["kit_mutation"] = int(cbf_aml and rng.random() < 0.25)

        # CLL molecular
        d["ighv_mutated_flag"] = int(st == "CLL" and rng.random() < 0.50)
        d["del_17p_flag"] = int(st == "CLL" and rng.random() < 0.08)
        d["del_11q_flag"] = int(st == "CLL" and rng.random() < 0.12)
        d["del_13q_flag"] = int(st == "CLL" and rng.random() < 0.50)
        d["trisomy_12_flag"] = int(st == "CLL" and rng.random() < 0.15)
        d["notch1_mutation_cll"] = int(st == "CLL" and rng.random() < 0.12)
        d["btk_c481s_flag"] = int(st == "CLL" and rng.random() < 0.05)  # ibrutinib resistance

        # ALL molecular
        d["jak_stat_mutation"] = int(st == "ALL" and rng.random() < 0.12)
        d["notch1_fbxw7_mutation"] = int(st == "ALL" and rng.random() < 0.55)

        # MRD method
        d["mrd_method"] = choice(rng,
            ["Flow_Cytometry", "PCR", "NGS_MRD", "Not_Assessed"],
            [0.45, 0.30, 0.15, 0.10])

        # ELN 2022 risk (AML/APL)
        if st in ["AML", "APL"]:
            if st == "APL" or d["t_8_21_flag"] or d["inv_16_flag"] or d["cebpa_mutation"] == "Biallelic":
                d["eln_2022_risk_category"] = "Favorable"
            elif d["tp53_mutation"] or d["complex_karyotype_flag"] or d["monosomal_karyotype_flag"] or d["asxl1_mutation"] or d["runx1_mutation"]:
                d["eln_2022_risk_category"] = "Adverse"
            elif d["flt3_itd_high_ratio"] and d["npm1_mutation"] == "WT":
                d["eln_2022_risk_category"] = "Adverse"
            elif d["npm1_mutation"] == "Mutant":
                d["eln_2022_risk_category"] = "Favorable" if d["flt3_itd_status"] == "Negative" else "Intermediate"
            else:
                d["eln_2022_risk_category"] = "Intermediate"
        elif st == "CML":
            d["eln_2022_risk_category"] = "N/A"
            d["sokal_score"] = choice(rng, ["Low","Intermediate","High"], [0.40, 0.40, 0.20])
            d["eutos_score"] = choice(rng, ["Low","High"], [0.70, 0.30])
        elif st == "CLL":
            d["eln_2022_risk_category"] = "N/A"
            d["cll_rai_stage"] = choice(rng, [0,1,2,3,4], [0.30,0.25,0.20,0.15,0.10])
            d["cll_binet_stage"] = choice(rng, ["A","B","C"], [0.55,0.30,0.15])
        else:  # ALL
            d["eln_2022_risk_category"] = "N/A"
            d["nccn_all_risk"] = choice(rng, ["Standard","High","Very_High"],
                [0.30, 0.45, 0.25] if age >= 18 else [0.55, 0.35, 0.10])

        # Fill N/A for missing fields
        for f in ["sokal_score","eutos_score","cll_rai_stage","cll_binet_stage","nccn_all_risk"]:
            if f not in d:
                d[f] = "N/A"

        rows.append(d)

    return pd.DataFrame(rows, index=demo.index)

# ─────────────────────────────────────────────────────────────────────────────
# MODULE 4: TREATMENT PROTOCOLS
# ─────────────────────────────────────────────────────────────────────────────

def generate_treatment(rng, demo, mol):
    rows = []

    for i, row in demo.iterrows():
        st = row["leukemia_type"]
        age = row["age_at_diagnosis"]
        ecog = row["ecog_performance_status"]
        m = mol.loc[i]
        d = {}

        d["treatment_intent"] = (
            "Palliative" if ecog >= 3 else
            "Watch_and_Wait" if (st == "CLL" and m.get("cll_rai_stage", 0) in [0, 1]) else
            "Curative"
        )

        if st == "AML":
            flt3_pos = m["flt3_itd_status"] != "Negative" or m["flt3_tkd_d835_status"] == "Positive"
            idh1_pos = m["idh1_mutation"] != "WT"
            idh2_pos = m["idh2_mutation"] != "WT"
            fit = ecog <= 2 and age <= 75

            if not fit:
                d["induction_regimen"] = choice(rng, ["Venetoclax+AZA","Venetoclax+LDAC","BSC"], [0.55,0.30,0.15])
            elif flt3_pos:
                d["induction_regimen"] = "Midostaurin+7+3"
            elif idh1_pos:
                d["induction_regimen"] = choice(rng, ["Ivosidenib+AZA","7+3_Idarubicin"], [0.40, 0.60])
            elif idh2_pos:
                d["induction_regimen"] = choice(rng, ["Enasidenib+AZA","7+3_Idarubicin"], [0.35, 0.65])
            elif m.get("eln_2022_risk_category") == "Adverse":
                d["induction_regimen"] = choice(rng, ["CPX-351","7+3_Idarubicin"], [0.45, 0.55])
            else:
                d["induction_regimen"] = choice(rng, ["7+3_Daunorubicin","7+3_Idarubicin"], [0.40, 0.60])

            d["consolidation_regimen"] = choice(rng,
                ["HiDAC_3g_m2", "Intermediate_AraC", "Azacitidine_Maintenance", "None"],
                [0.50, 0.25, 0.15, 0.10])
            d["maintenance_flag"] = int(d["consolidation_regimen"] == "Azacitidine_Maintenance" or
                                        (flt3_pos and rng.random() < 0.50))
            d["maintenance_regimen"] = "Gilteritinib" if (flt3_pos and d["maintenance_flag"]) else (
                "Azacitidine" if d["maintenance_flag"] else "None")
            d["induction_cycles"] = int(rng.choice([1, 2], p=[0.70, 0.30]))
            d["consolidation_cycles"] = int(rng.choice([1, 2, 3, 4], p=[0.15, 0.35, 0.35, 0.15]))
            d["targeted_agent"] = (
                "Midostaurin" if d["induction_regimen"] == "Midostaurin+7+3" else
                "Ivosidenib" if "Ivosidenib" in d["induction_regimen"] else
                "Enasidenib" if "Enasidenib" in d["induction_regimen"] else
                "Venetoclax" if "Venetoclax" in d["induction_regimen"] else "None"
            )
            d["apl_atra_flag"] = 0

        elif st == "APL":
            d["induction_regimen"] = choice(rng, ["ATRA_ATO_APL0406","ATRA_ATO_APML4","ATRA_Daunorubicin_Sanz"], [0.50, 0.25, 0.25])
            d["apl_atra_flag"] = 1
            d["consolidation_regimen"] = choice(rng, ["ATRA_ATO_Consolidation","ATRA_Chemo"], [0.60, 0.40])
            d["maintenance_flag"] = int(rng.random() < 0.70)
            d["maintenance_regimen"] = "ATRA_6MP_MTX" if d["maintenance_flag"] else "None"
            d["induction_cycles"] = 1
            d["consolidation_cycles"] = int(rng.choice([2, 3], p=[0.30, 0.70]))
            d["targeted_agent"] = "ATO+ATRA"

        elif st == "ALL":
            ph_pos = m.get("t_9_22_bcr_abl1_flag", 0) == 1
            if ph_pos:
                d["induction_regimen"] = choice(rng, ["Dasatinib+Steroids","Ponatinib+Chemo","HyperCVAD_Dasatinib"], [0.40, 0.30, 0.30])
                d["targeted_agent"] = "Ponatinib" if "Ponatinib" in d["induction_regimen"] else "Dasatinib"
            elif age < 18:
                d["induction_regimen"] = choice(rng, ["CALGB_10403","BFM_Protocol","COG_AALL0434"], [0.40, 0.35, 0.25])
                d["targeted_agent"] = "None"
            elif m.get("cns_involvement_flag", 0):
                d["induction_regimen"] = "HyperCVAD_A_B"
                d["targeted_agent"] = "None"
            else:
                d["induction_regimen"] = choice(rng,
                    ["CALGB_10403","HyperCVAD_A_B","GRAALL_2005","UKALL14",
                     "Blinatumomab+Chemo","Inotuzumab_Ozogamicin+Chemo"],
                    [0.25, 0.25, 0.15, 0.15, 0.10, 0.10])
                d["targeted_agent"] = (
                    "Blinatumomab" if "Blinatumomab" in d["induction_regimen"] else
                    "Inotuzumab" if "Inotuzumab" in d["induction_regimen"] else "None"
                )
            d["consolidation_regimen"] = choice(rng, ["Consolidation_A_B_C","Maintenance_Only","None"], [0.60, 0.30, 0.10])
            d["maintenance_flag"] = 1
            d["maintenance_regimen"] = "6MP_MTX_Vincristine_Prednisone"
            d["induction_cycles"] = 1
            d["consolidation_cycles"] = int(rng.choice([2, 3, 4], p=[0.25, 0.45, 0.30]))
            d["apl_atra_flag"] = 0

        elif st == "CML":
            phase = m.get("who_2022_classification","CML_CP")
            if "BP" in str(phase):
                d["induction_regimen"] = choice(rng, ["Ponatinib_45mg","Dasatinib_Chemo","Asciminib"], [0.40, 0.35, 0.25])
            else:
                d["induction_regimen"] = choice(rng, CML_REGIMENS, [0.25, 0.25, 0.20, 0.10, 0.10, 0.10])
            d["targeted_agent"] = d["induction_regimen"].split("_")[0]
            d["tki_generation"] = (
                "First" if "Imatinib" in d["induction_regimen"] else
                "Second" if any(x in d["induction_regimen"] for x in ["Dasatinib","Nilotinib","Bosutinib"]) else
                "Third" if "Ponatinib" in d["induction_regimen"] else "STAMP"
            )
            d["consolidation_regimen"] = "N/A"
            d["maintenance_flag"] = 0
            d["maintenance_regimen"] = "Ongoing_TKI"
            d["induction_cycles"] = "Continuous"
            d["consolidation_cycles"] = 0
            d["apl_atra_flag"] = 0

        else:  # CLL
            rai = m.get("cll_rai_stage", 0)
            unfit = ecog >= 2 or age >= 75
            if rai in [0, 1]:
                d["induction_regimen"] = "Watch_and_Wait"
                d["targeted_agent"] = "None"
            elif unfit:
                d["induction_regimen"] = choice(rng, ["Venetoclax_Obinutuzumab","Acalabrutinib","Ibrutinib"], [0.45, 0.30, 0.25])
                d["targeted_agent"] = d["induction_regimen"].split("_")[0]
            elif m.get("del_17p_flag", 0) or m.get("tp53_mutation", 0):
                d["induction_regimen"] = choice(rng, ["Ibrutinib","Acalabrutinib","Venetoclax_Rituximab"], [0.40, 0.35, 0.25])
                d["targeted_agent"] = d["induction_regimen"].split("_")[0]
            elif m.get("ighv_mutated_flag", 0):
                d["induction_regimen"] = choice(rng, ["FCR","Venetoclax_Obinutuzumab","Venetoclax_Rituximab"], [0.35, 0.35, 0.30])
                d["targeted_agent"] = d["induction_regimen"].split("_")[0]
            else:
                d["induction_regimen"] = choice(rng, ["Ibrutinib","Acalabrutinib","Venetoclax_Obinutuzumab"], [0.35, 0.35, 0.30])
                d["targeted_agent"] = d["induction_regimen"].split("_")[0]

            d["consolidation_regimen"] = "N/A"
            d["maintenance_flag"] = 0
            d["maintenance_regimen"] = "Ongoing_BTKi" if "rutinib" in d["induction_regimen"] else "None"
            d["induction_cycles"] = int(rng.choice([6, 12, 24], p=[0.30, 0.40, 0.30]))
            d["consolidation_cycles"] = 0
            d["apl_atra_flag"] = 0

        d["total_treatment_months"] = int(clamp(rng.normal(24, 8), 6, 48))
        d["clinical_trial_enrollment_flag"] = int(rng.random() < 0.25)
        d["trial_phase"] = choice(rng, ["Phase_I","Phase_II","Phase_III"], [0.10, 0.40, 0.50]) if d["clinical_trial_enrollment_flag"] else "None"

        rows.append(d)

    return pd.DataFrame(rows, index=demo.index)

# ─────────────────────────────────────────────────────────────────────────────
# MODULE 5: REMISSION & RESPONSE
# ─────────────────────────────────────────────────────────────────────────────

def generate_remission(rng, demo, mol, treat):
    rows = []

    for i, row in demo.iterrows():
        st = row["leukemia_type"]
        age = row["age_at_diagnosis"]
        m = mol.loc[i]
        t = treat.loc[i]
        d = {}

        if st in ["AML","APL"]:
            eln = m.get("eln_2022_risk_category","Intermediate")
            key = f"AML_{eln}" if st == "AML" else "APL"
            base_cr = CR_RATE.get(key, 0.60)
            d["cr_achieved_flag"] = int(rng.random() < base_cr)
            d["cri_achieved_flag"] = int(not d["cr_achieved_flag"] and rng.random() < 0.15)
            d["pr_achieved_flag"] = int(not d["cr_achieved_flag"] and not d["cri_achieved_flag"] and rng.random() < 0.10)
            d["time_to_cr_days"] = int(clamp(rng.normal(28, 7), 14, 60)) if d["cr_achieved_flag"] else None
            d["cytogenetic_remission_flag"] = int(d["cr_achieved_flag"] and rng.random() < 0.85)
            d["molecular_remission_flag"] = int(d["cytogenetic_remission_flag"] and rng.random() < 0.75)
            d["mrd_negativity_flag"] = int(d["molecular_remission_flag"] and rng.random() < 0.80)
            d["mrd_conversion_cycle"] = int(rng.choice([1,2,3,4], p=[0.40,0.35,0.15,0.10])) if d["mrd_negativity_flag"] else None
            d["hematologic_relapse_flag"] = int(d["cr_achieved_flag"] and rng.random() < (0.20 if eln == "Favorable" else 0.45 if eln == "Intermediate" else 0.65))
            d["molecular_relapse_flag"] = int(d["mrd_negativity_flag"] and rng.random() < 0.25)
            d["time_to_relapse_months"] = round(weibull_sample(rng, 1.5, 18 if eln=="Favorable" else 12)[0], 1) if d["hematologic_relapse_flag"] else None
            d["relapse_site"] = choice(rng, ["Bone_Marrow","Extramedullary","CNS","Peripheral_Blood"], [0.70,0.15,0.08,0.07]) if d["hematologic_relapse_flag"] else "None"
            d["salvage_cr_rate"] = round(rng.uniform(0.30, 0.45) if st=="AML" else rng.uniform(0.40, 0.65), 2) if d["hematologic_relapse_flag"] else None
            # CML-specific (N/A)
            for f in ["bcr_abl1_is_pct","ccyr_achieved_flag","mmr_achieved_flag","dmr_achieved_flag","sokal_score_val","tki_resistance_mechanism"]:
                d[f] = "N/A"
            # CLL-specific (N/A)
            for f in ["cll_iwcll_response","bruton_resistance_flag"]:
                d[f] = "N/A"

        elif st == "ALL":
            is_ped = age < 18
            key = "ALL_Pediatric" if is_ped else "ALL_Adult"
            base_cr = CR_RATE[key]
            d["cr_achieved_flag"] = int(rng.random() < base_cr)
            d["cri_achieved_flag"] = int(not d["cr_achieved_flag"] and rng.random() < 0.08)
            d["pr_achieved_flag"] = int(not d["cr_achieved_flag"] and not d["cri_achieved_flag"] and rng.random() < 0.05)
            d["time_to_cr_days"] = int(clamp(rng.normal(28, 7), 14, 56)) if d["cr_achieved_flag"] else None
            d["cytogenetic_remission_flag"] = int(d["cr_achieved_flag"] and rng.random() < 0.80)
            d["molecular_remission_flag"] = int(d["cytogenetic_remission_flag"] and rng.random() < 0.70)
            d["mrd_negativity_flag"] = int(d["molecular_remission_flag"] and rng.random() < 0.85)
            d["mrd_conversion_cycle"] = int(rng.choice([1,2,3], p=[0.45,0.35,0.20])) if d["mrd_negativity_flag"] else None
            d["hematologic_relapse_flag"] = int(d["cr_achieved_flag"] and rng.random() < (0.05 if is_ped else 0.40))
            d["molecular_relapse_flag"] = int(d["mrd_negativity_flag"] and rng.random() < 0.20)
            d["time_to_relapse_months"] = round(weibull_sample(rng, 1.5, 24 if is_ped else 14)[0], 1) if d["hematologic_relapse_flag"] else None
            d["relapse_site"] = choice(rng, ["Bone_Marrow","CNS","Testicular","Peripheral_Blood"], [0.65,0.20,0.05,0.10]) if d["hematologic_relapse_flag"] else "None"
            d["salvage_cr_rate"] = round(rng.uniform(0.25, 0.50), 2) if d["hematologic_relapse_flag"] else None
            for f in ["bcr_abl1_is_pct","ccyr_achieved_flag","mmr_achieved_flag","dmr_achieved_flag","sokal_score_val","tki_resistance_mechanism"]:
                d[f] = "N/A"
            for f in ["cll_iwcll_response","bruton_resistance_flag"]:
                d[f] = "N/A"

        elif st == "CML":
            d["cr_achieved_flag"] = "N/A"
            d["cri_achieved_flag"] = "N/A"
            d["pr_achieved_flag"] = "N/A"
            d["time_to_cr_days"] = "N/A"
            d["cytogenetic_remission_flag"] = "N/A"
            d["molecular_remission_flag"] = "N/A"
            d["mrd_negativity_flag"] = "N/A"
            d["mrd_conversion_cycle"] = "N/A"
            d["hematologic_relapse_flag"] = int(rng.random() < 0.10)
            d["molecular_relapse_flag"] = int(rng.random() < 0.15)
            d["time_to_relapse_months"] = round(weibull_sample(rng, 1.8, 36)[0], 1) if d["hematologic_relapse_flag"] else None
            d["relapse_site"] = "Peripheral_Blood" if d["hematologic_relapse_flag"] else "None"
            d["salvage_cr_rate"] = "N/A"
            # CML molecular response
            tki_gen = t.get("tki_generation", "Second")
            mmr_base = 0.70 if tki_gen == "First" else 0.80 if tki_gen == "Second" else 0.75
            d["ccyr_achieved_flag"] = int(rng.random() < (mmr_base + 0.10))
            d["mmr_achieved_flag"] = int(rng.random() < mmr_base)
            d["dmr_achieved_flag"] = int(d["mmr_achieved_flag"] and rng.random() < 0.40)
            d["bcr_abl1_is_pct"] = round(lognorm(rng, -2.0, 1.5, 0.001, 100) if d["mmr_achieved_flag"] else lognorm(rng, 0.5, 1.0, 0.1, 100), 4)
            d["tki_resistance_mechanism"] = choice(rng, ["T315I","E255K","F317L","Compound_Mutation","None"], [0.30,0.20,0.15,0.10,0.25]) if d["molecular_relapse_flag"] else "None"
            for f in ["cll_iwcll_response","bruton_resistance_flag"]:
                d[f] = "N/A"

        else:  # CLL
            d["cr_achieved_flag"] = int(rng.random() < 0.25)
            d["cri_achieved_flag"] = "N/A"
            d["pr_achieved_flag"] = int(not d["cr_achieved_flag"] and rng.random() < 0.55)
            d["time_to_cr_days"] = "N/A"
            d["cytogenetic_remission_flag"] = "N/A"
            d["molecular_remission_flag"] = "N/A"
            d["mrd_negativity_flag"] = int(d["cr_achieved_flag"] and rng.random() < 0.60)
            d["mrd_conversion_cycle"] = "N/A"
            d["hematologic_relapse_flag"] = int(rng.random() < 0.25)
            d["molecular_relapse_flag"] = "N/A"
            d["time_to_relapse_months"] = round(weibull_sample(rng, 1.4, 36)[0], 1) if d["hematologic_relapse_flag"] else None
            d["relapse_site"] = "Peripheral_Blood" if d["hematologic_relapse_flag"] else "None"
            d["salvage_cr_rate"] = "N/A"
            d["cll_iwcll_response"] = choice(rng, ["CR","MRD_Negative_CR","PR","SD","PD"], [0.20,0.15,0.45,0.10,0.10])
            d["bruton_resistance_flag"] = int(m.get("btk_c481s_flag", 0))
            for f in ["bcr_abl1_is_pct","ccyr_achieved_flag","mmr_achieved_flag","dmr_achieved_flag","sokal_score_val","tki_resistance_mechanism"]:
                d[f] = "N/A"

        rows.append(d)

    return pd.DataFrame(rows, index=demo.index)

# ─────────────────────────────────────────────────────────────────────────────
# MODULE 6: BONE MARROW TRANSPLANT
# ─────────────────────────────────────────────────────────────────────────────

def generate_bmt(rng, demo, mol, treat, remission):
    rows = []

    for i, row in demo.iterrows():
        st = row["leukemia_type"]
        age = row["age_at_diagnosis"]
        m = mol.loc[i]
        t = treat.loc[i]
        r = remission.loc[i]
        d = {}

        # BMT eligibility
        eln = m.get("eln_2022_risk_category", "Intermediate")
        age_eligible = age <= 75
        if st == "AML":
            bmt_indicated = (eln in ["Intermediate","Adverse"]) and age_eligible and str(r.get("cr_achieved_flag","0")) == "1"
        elif st == "ALL":
            bmt_indicated = age_eligible and str(r.get("cr_achieved_flag","0")) == "1"
        elif st == "APL":
            bmt_indicated = str(r.get("hematologic_relapse_flag","0")) == "1"
        elif st == "CML":
            bmt_indicated = str(r.get("tki_resistance_mechanism","None")) not in ["None","N/A"]
        else:  # CLL
            bmt_indicated = False

        d["bmt_indicated_flag"] = int(bmt_indicated)
        d["bmt_performed_flag"] = int(bmt_indicated and rng.random() < 0.70)

        if d["bmt_performed_flag"]:
            d["bmt_timing"] = choice(rng,
                ["CR1","CR2","Upfront_High_Risk","Relapsed_Refractory"],
                [0.55, 0.25, 0.12, 0.08])
            d["bmt_type"] = choice(rng, ["Allogeneic","Autologous","Haploidentical"], [0.75, 0.15, 0.10])
            if d["bmt_type"] in ["Allogeneic","Haploidentical"]:
                d["donor_type"] = "Haploidentical" if d["bmt_type"] == "Haploidentical" else choice(rng, ["MRD","MUD","MMUD"], [0.45, 0.42, 0.13])
                d["hla_match_grade"] = "5-6/10" if d["donor_type"] == "Haploidentical" else choice(rng, ["10/10","9/10","8/10"], [0.60, 0.30, 0.10])
            else:
                d["donor_type"] = "Autologous"
                d["hla_match_grade"] = "N/A"

            d["donor_recipient_sex_mismatch_flag"] = int(rng.random() < 0.25)
            d["stem_cell_source"] = choice(rng, ["Peripheral_Blood","Bone_Marrow","Cord_Blood"], [0.75, 0.20, 0.05])
            age_fit = age >= 60 or m.get("eln_2022_risk_category","") == "Adverse"
            d["conditioning_intensity"] = choice(rng, ["Myeloablative_MAC","Reduced_Intensity_RIC","Non_Myeloablative_NMA"],
                [0.35, 0.50, 0.15] if age >= 55 else [0.70, 0.25, 0.05])
            cond_type = d["conditioning_intensity"].split("_")[0].split("_")[0]
            if "Mac" in d["conditioning_intensity"] or "Myeloab" in d["conditioning_intensity"]:
                d["conditioning_regimen"] = choice(rng, CONDITIONING["MAC"])
            elif "RIC" in d["conditioning_intensity"]:
                d["conditioning_regimen"] = choice(rng, CONDITIONING["RIC"])
            else:
                d["conditioning_regimen"] = choice(rng, CONDITIONING["NMA"])

            d["gvhd_prophylaxis"] = choice(rng, GVHD_PROPHYLAXIS)
            d["graft_cd34_x10e6_kg"] = round(lognorm(rng, 1.8, 0.4, 1.0, 15.0), 2)
            d["engraftment_day_neutrophil"] = int(clamp(rng.normal(14, 4), 8, 35))
            d["platelet_engraftment_day"] = int(clamp(rng.normal(18, 5), 10, 45))
            d["primary_graft_failure_flag"] = int(rng.random() < 0.05)
            d["secondary_graft_failure_flag"] = int(not d["primary_graft_failure_flag"] and rng.random() < 0.03)

            if d["bmt_type"] in ["Allogeneic","Haploidentical"] and not d["primary_graft_failure_flag"]:
                donor = d["donor_type"]
                agvhd_p = AGVHD_RATE.get(donor, 0.40)
                # PT-Cy prophylaxis reduces aGvHD in haplo
                if "PT_Cy" in d["gvhd_prophylaxis"]:
                    agvhd_p *= 0.65

                d["acute_gvhd_flag"] = int(rng.random() < agvhd_p)
                d["acute_gvhd_grade"] = choice(rng, ["None","I","II","III","IV"],
                    [1-agvhd_p, agvhd_p*0.30, agvhd_p*0.40, agvhd_p*0.20, agvhd_p*0.10]) if d["acute_gvhd_flag"] else "None"
                d["acute_gvhd_organs"] = choice(rng, ["Skin","Gut","Liver","Multi_organ"], [0.45, 0.30, 0.10, 0.15]) if d["acute_gvhd_flag"] else "None"
                d["chronic_gvhd_flag"] = int(rng.random() < 0.40)
                d["chronic_gvhd_severity"] = choice(rng, ["Mild","Moderate","Severe"], [0.45, 0.35, 0.20]) if d["chronic_gvhd_flag"] else "None"
                d["chronic_gvhd_organs"] = choice(rng, ["Skin","Mouth","Eye","Lung","Liver","Multi"], [0.30,0.20,0.15,0.10,0.10,0.15]) if d["chronic_gvhd_flag"] else "None"
                nrm_p = NRM_RATE.get(donor, 0.15)
                d["non_relapse_mortality_flag"] = int(rng.random() < nrm_p)
            else:
                for f in ["acute_gvhd_flag","acute_gvhd_grade","acute_gvhd_organs",
                          "chronic_gvhd_flag","chronic_gvhd_severity","chronic_gvhd_organs",
                          "non_relapse_mortality_flag"]:
                    d[f] = 0 if "flag" in f else "None" if f.endswith("_flag")==False else 0

            # Complications
            is_mac = "Mac" in d["conditioning_intensity"] or "Myeloab" in d["conditioning_intensity"]
            d["vod_flag"] = int(rng.random() < (0.12 if is_mac else 0.04))
            d["vod_severity"] = choice(rng, ["Mild","Moderate","Severe","Very_Severe"], [0.40,0.30,0.20,0.10]) if d["vod_flag"] else "None"
            d["cmv_reactivation_flag"] = int(rng.random() < 0.50)
            d["ebv_reactivation_flag"] = int(rng.random() < 0.18)
            d["invasive_fungal_infection_flag"] = int(rng.random() < (0.10 if is_mac else 0.04))
            d["bacterial_infection_flag"] = int(rng.random() < 0.40)
            d["idiopathic_pneumonia_flag"] = int(rng.random() < 0.05)
            d["thrombotic_microangiopathy_flag"] = int(rng.random() < 0.07)
            d["day_100_mortality_flag"] = int(
                d["primary_graft_failure_flag"] or
                (d["vod_flag"] and d["vod_severity"] in ["Severe","Very_Severe"] and rng.random() < 0.50) or
                (d.get("acute_gvhd_grade","None") in ["III","IV"] and rng.random() < 0.35) or
                rng.random() < 0.05
            )
            d["day_100_chimerism_pct"] = round(clamp(rng.normal(97, 4), 80, 100), 1) if not d["primary_graft_failure_flag"] else round(clamp(rng.normal(60, 20), 10, 80), 1)
            d["relapse_post_bmt_flag"] = int(not d["day_100_mortality_flag"] and rng.random() < 0.28)
            d["time_to_relapse_post_bmt_months"] = round(weibull_sample(rng, 1.3, 14)[0], 1) if d["relapse_post_bmt_flag"] else None
            d["donor_lymphocyte_infusion_flag"] = int(d["relapse_post_bmt_flag"] and rng.random() < 0.45)
            d["second_bmt_flag"] = int(d["relapse_post_bmt_flag"] and rng.random() < 0.07)
            d["one_yr_os_post_bmt"] = int(not d["day_100_mortality_flag"] and not d["non_relapse_mortality_flag"])
            d["two_yr_rfs_post_bmt"] = int(d["one_yr_os_post_bmt"] and not d["relapse_post_bmt_flag"] and rng.random() < 0.70)
        else:
            for f in ["bmt_timing","bmt_type","donor_type","hla_match_grade","donor_recipient_sex_mismatch_flag",
                      "stem_cell_source","conditioning_intensity","conditioning_regimen","gvhd_prophylaxis",
                      "graft_cd34_x10e6_kg","engraftment_day_neutrophil","platelet_engraftment_day",
                      "primary_graft_failure_flag","secondary_graft_failure_flag",
                      "acute_gvhd_flag","acute_gvhd_grade","acute_gvhd_organs",
                      "chronic_gvhd_flag","chronic_gvhd_severity","chronic_gvhd_organs",
                      "vod_flag","vod_severity","cmv_reactivation_flag","ebv_reactivation_flag",
                      "invasive_fungal_infection_flag","bacterial_infection_flag",
                      "idiopathic_pneumonia_flag","thrombotic_microangiopathy_flag",
                      "day_100_mortality_flag","non_relapse_mortality_flag","day_100_chimerism_pct",
                      "relapse_post_bmt_flag","time_to_relapse_post_bmt_months",
                      "donor_lymphocyte_infusion_flag","second_bmt_flag",
                      "one_yr_os_post_bmt","two_yr_rfs_post_bmt"]:
                d[f] = "N/A"

        rows.append(d)

    return pd.DataFrame(rows, index=demo.index)

# ─────────────────────────────────────────────────────────────────────────────
# MODULE 7: TOXICITY
# ─────────────────────────────────────────────────────────────────────────────

def generate_toxicity(rng, demo, treat):
    rows = []
    for i, row in demo.iterrows():
        st = row["leukemia_type"]
        t = treat.loc[i]
        reg = str(t.get("induction_regimen",""))
        d = {}
        d["febrile_neutropenia_flag"] = int(st in ["AML","APL","ALL"] and rng.random() < 0.85)
        d["induction_mortality_flag"] = int(
            (st == "AML" and rng.random() < 0.04) or
            (st == "APL" and rng.random() < 0.02) or
            (st == "ALL" and rng.random() < 0.02)
        )
        d["fungal_infection_flag"] = int(rng.random() < 0.15 if st in ["AML","APL","ALL"] else rng.random() < 0.03)
        d["tumor_lysis_syndrome_flag"] = int(
            (st == "ALL" and rng.random() < 0.18) or
            (st == "AML" and rng.random() < 0.08)
        )
        d["tls_cairo_bishop_grade"] = int(rng.choice([1,2,3], p=[0.50,0.35,0.15])) if d["tumor_lysis_syndrome_flag"] else 0
        d["differentiation_syndrome_flag"] = int(
            (st == "APL" and rng.random() < 0.22) or
            ("IDH" in reg and rng.random() < 0.15)
        )
        d["differentiation_syndrome_severity"] = choice(rng, ["Mild","Moderate","Severe"], [0.50,0.35,0.15]) if d["differentiation_syndrome_flag"] else "None"
        d["anthracycline_cardiotoxicity_flag"] = int("7+3" in reg and rng.random() < 0.08)
        d["lvef_post_treatment_pct"] = int(clamp(rng.normal(58, 8), 30, 75))
        d["peripheral_neuropathy_flag"] = int(st == "ALL" and rng.random() < 0.30)
        d["hepatotoxicity_flag"] = int(rng.random() < 0.15)
        d["mucositis_grade"] = int(rng.choice([0,1,2,3,4], p=[0.40,0.25,0.20,0.10,0.05]))
        d["hemorrhagic_cystitis_flag"] = int(rng.random() < 0.08)
        d["qol_score_baseline_fact_leu"] = int(clamp(rng.normal(130, 22), 60, 176))
        d["qol_score_end_of_induction"] = int(clamp(d["qol_score_baseline_fact_leu"] - rng.normal(20, 12), 40, 176))
        rows.append(d)
    return pd.DataFrame(rows, index=demo.index)

# ─────────────────────────────────────────────────────────────────────────────
# MODULE 8: SURVIVAL OUTCOMES
# ─────────────────────────────────────────────────────────────────────────────

def generate_survival(rng, demo, mol, remission, bmt, tox):
    rows = []
    for i, row in demo.iterrows():
        st = row["leukemia_type"]
        age = row["age_at_diagnosis"]
        m = mol.loc[i]
        r = remission.loc[i]
        b = bmt.loc[i]
        tx = tox.loc[i]
        d = {}

        eln = str(m.get("eln_2022_risk_category","Intermediate"))
        is_ped = age < 18
        induction_dead = tx.get("induction_mortality_flag", 0)
        bmt_dead = str(b.get("day_100_mortality_flag","0")) in ["1","True"]
        nrm_dead = str(b.get("non_relapse_mortality_flag","0")) in ["1","True"]

        if st == "AML":
            key = f"AML_{eln}" if eln in ["Favorable","Intermediate","Adverse"] else "AML_Intermediate"
        elif st == "APL":
            key = "APL"
        elif st == "ALL":
            key = "ALL_Pediatric" if is_ped else "ALL_Adult"
        elif st == "CML":
            key = "CML"
        else:
            key = "CLL"

        lam = OS_LAMBDA.get(key, 36.0)
        os_months = round(weibull_sample(rng, 1.4, lam)[0], 1)

        # Compress OS if induction or BMT mortality
        if induction_dead:
            os_months = round(rng.uniform(0.5, 3.0), 1)
        elif bmt_dead:
            bmt_os = str(b.get("one_yr_os_post_bmt","N/A"))
            if bmt_os == "0":
                os_months = min(os_months, round(rng.uniform(2, 12), 1))

        d["overall_survival_months"] = os_months
        d["event_free_survival_months"] = round(min(os_months, weibull_sample(rng, 1.5, lam * 0.7)[0]), 1)
        d["leukemia_free_survival_months"] = round(min(d["event_free_survival_months"], weibull_sample(rng, 1.6, lam * 0.8)[0]), 1)
        d["relapse_free_survival_months"] = round(min(d["leukemia_free_survival_months"], weibull_sample(rng, 1.7, lam * 0.9)[0]), 1)

        if induction_dead:
            d["vital_status"] = "Dead_Treatment"
            d["cause_of_death"] = "Organ_Failure"
        elif bmt_dead or nrm_dead:
            d["vital_status"] = "Dead_Treatment"
            d["cause_of_death"] = choice(rng, ["GvHD","Infection","Organ_Failure"], [0.35,0.45,0.20])
        elif str(r.get("hematologic_relapse_flag","0")) == "1" and os_months < lam * 0.5:
            d["vital_status"] = "Dead_Disease"
            d["cause_of_death"] = "Relapse"
        elif rng.random() < 0.65:
            d["vital_status"] = "Alive"
            d["cause_of_death"] = "None"
        else:
            d["vital_status"] = choice(rng, ["Dead_Disease","Dead_Other"], [0.75, 0.25])
            d["cause_of_death"] = choice(rng, ["Relapse","Infection","Secondary_Malignancy","Other"],
                [0.60, 0.20, 0.10, 0.10])

        d["second_malignancy_flag"] = int(rng.random() < 0.03)
        rows.append(d)

    return pd.DataFrame(rows, index=demo.index)

# ─────────────────────────────────────────────────────────────────────────────
# MRD LONGITUDINAL DATA
# ─────────────────────────────────────────────────────────────────────────────

def generate_mrd_longitudinal(rng, demo, remission, n_timepoints=12):
    """Generate monthly MRD assessments for a subset of patients."""
    records = []
    sample_ids = demo.sample(frac=0.40, random_state=42)["patient_id"].tolist()

    for pid in sample_ids:
        idx = demo[demo["patient_id"] == pid].index[0]
        st = demo.loc[idx, "leukemia_type"]
        r = remission.loc[idx]
        cr = str(r.get("cr_achieved_flag", "0")) == "1"
        mrd_neg = str(r.get("mrd_negativity_flag", "0")) == "1"

        for month in range(1, n_timepoints + 1):
            mrd_val = None
            if cr and month <= 3:
                mrd_val = round(lognorm(rng, -1.0, 1.2, 0.001, 10.0), 4)
            elif mrd_neg and month > 3:
                mrd_val = round(lognorm(rng, -3.5, 0.8, 0.0001, 0.05), 5)
            elif cr:
                mrd_val = round(lognorm(rng, -2.0, 1.0, 0.001, 1.0), 4)
            records.append({
                "patient_id": pid,
                "leukemia_type": st,
                "assessment_month": month,
                "mrd_value_pct": mrd_val,
                "mrd_negative_flag": int(mrd_val is not None and mrd_val < 0.01),
                "assessment_method": rng.choice(["Flow_Cytometry","PCR","NGS_MRD"]),
                "wbc_k_ul": round(lognorm(rng, 2.3, 0.5, 1.0, 15.0), 1),
                "hemoglobin_g_dl": round(clamp(rng.normal(11.5, 1.8), 7.0, 16.0), 1),
                "platelets_k_ul": round(lognorm(rng, 5.0, 0.4, 50, 400), 0),
                "neutrophils_k_ul": round(lognorm(rng, 1.2, 0.6, 0.1, 12.0), 2),
            })
    return pd.DataFrame(records)

# ─────────────────────────────────────────────────────────────────────────────
# MAIN
# ─────────────────────────────────────────────────────────────────────────────

def main():
    parser = argparse.ArgumentParser(description="HC-ONC-007 Leukemia Simulation Engine")
    parser.add_argument("--n_patients", type=int, default=25000)
    parser.add_argument("--seed", type=int, default=42)
    parser.add_argument("--output_dir", type=str, default="./output")
    parser.add_argument("--format", type=str, default="csv", choices=["csv","parquet","json"])
    args = parser.parse_args()

    os.makedirs(args.output_dir, exist_ok=True)
    rng = np.random.default_rng(args.seed)
    n = args.n_patients

    print(f"[HC-ONC-007] Generating {n:,} synthetic leukemia patients (seed={args.seed})...")

    print("  [1/8] Demographics...")
    demo = generate_demographics(rng, n)

    print("  [2/8] Disease characteristics...")
    disease = generate_disease_characteristics(rng, demo)

    print("  [3/8] Cytogenetics & molecular markers...")
    mol = generate_molecular(rng, demo)

    print("  [4/8] Treatment protocols...")
    treat = generate_treatment(rng, demo, mol)

    print("  [5/8] Remission & response...")
    remission = generate_remission(rng, demo, mol, treat)

    print("  [6/8] Bone marrow transplant outcomes...")
    bmt = generate_bmt(rng, demo, mol, treat, remission)

    print("  [7/8] Toxicity...")
    tox = generate_toxicity(rng, demo, treat)

    print("  [8/8] Survival outcomes...")
    survival = generate_survival(rng, demo, mol, remission, bmt, tox)

    # Combine primary cohort
    primary = pd.concat([demo, disease, mol, treat, remission, bmt, tox, survival], axis=1)
    primary["generation_timestamp"] = datetime.now().isoformat()
    primary["sku"] = "HC-ONC-007"
    primary["version"] = "1.0.0"
    primary["seed"] = args.seed

    out_path = os.path.join(args.output_dir, "hc_onc_007_primary_cohort.csv")
    primary.to_csv(out_path, index=False)
    print(f"\n  Primary cohort: {len(primary):,} rows × {len(primary.columns)} columns → {out_path}")

    # MRD longitudinal
    print("  Generating MRD longitudinal data...")
    mrd_long = generate_mrd_longitudinal(rng, demo, remission)
    mrd_path = os.path.join(args.output_dir, "hc_onc_007_mrd_longitudinal.csv")
    mrd_long.to_csv(mrd_path, index=False)
    print(f"  MRD longitudinal: {len(mrd_long):,} records → {mrd_path}")

    # Summary
    print("\n" + "="*60)
    print("HC-ONC-007 Generation Complete")
    print("="*60)
    print(f"  Patients: {len(primary):,}")
    print(f"  Columns: {len(primary.columns)}")
    print(f"  Subtype distribution:")
    for st, cnt in primary["leukemia_type"].value_counts().items():
        print(f"    {st}: {cnt:,} ({cnt/len(primary)*100:.1f}%)")
    bmt_done = (primary["bmt_performed_flag"] == 1).sum()
    print(f"  BMT performed: {bmt_done:,} ({bmt_done/len(primary)*100:.1f}%)")
    cr_rate = (primary["cr_achieved_flag"].astype(str).isin(["1","1.0"])).mean()
    print(f"  Overall CR rate: {cr_rate*100:.1f}%")
    print(f"  Output: {args.output_dir}/")

if __name__ == "__main__":
    main()