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The generator intentionally creates cohort-specific aliases, units, modality
availability, and visit cadence before mapping every record into the common
data model. Nothing in this module represents a real participant or a reported
cohort result.
"""
from __future__ import annotations
from dataclasses import dataclass
from datetime import date, timedelta
import numpy as np
import pandas as pd
from etl.base import CohortTables
COHORTS = ("ADNI", "NACC", "AIBL", "OASIS", "UK Biobank", "TLSA", "SANSCOG")
PUBLIC_COHORTS = frozenset({"ADNI", "NACC", "AIBL", "OASIS", "UK Biobank"})
INDIAN_COHORTS = frozenset({"TLSA", "SANSCOG"})
FEATURE_SPECS: dict[str, tuple[str, str]] = {
"cognitive_score": ("cognition", "points"),
"memory_score": ("cognition", "z-score"),
"executive_score": ("cognition", "z-score"),
"hippocampal_volume_mm3": ("mri", "mm3"),
"wmh_burden_ml": ("mri", "mL"),
"hba1c_percent": ("biochem", "%"),
"hs_crp_mg_l": ("biochem", "mg/L"),
"rnfl_um": ("oct", "um"),
"vessel_density_percent": ("oct", "%"),
"apoe_e4_count": ("genomics", "alleles"),
"ancestry_pc1": ("genomics", "z-score"),
}
SHORT_NAMES = {
"cognitive_score": "cog_total",
"memory_score": "memory_z",
"executive_score": "exec_z",
"hippocampal_volume_mm3": "hippo_vol",
"wmh_burden_ml": "wmh",
"hba1c_percent": "hba1c",
"hs_crp_mg_l": "hscrp",
"rnfl_um": "rnfl",
"vessel_density_percent": "vessel_density",
"apoe_e4_count": "apoe4",
"ancestry_pc1": "pc1",
}
MISSINGNESS: dict[str, dict[str, float]] = {
"ADNI": {"mri": 0.08, "biochem": 0.12, "oct": 0.86, "genomics": 0.24},
"NACC": {"mri": 0.34, "biochem": 0.36, "oct": 0.91, "genomics": 0.44},
"AIBL": {"mri": 0.14, "biochem": 0.14, "oct": 0.80, "genomics": 0.32},
"OASIS": {"mri": 0.05, "biochem": 0.72, "oct": 0.96, "genomics": 0.82},
"UK Biobank": {"mri": 0.34, "biochem": 0.05, "oct": 0.43, "genomics": 0.06},
"TLSA": {"mri": 0.24, "biochem": 0.08, "oct": 0.24, "genomics": 0.34},
"SANSCOG": {"mri": 0.54, "biochem": 0.12, "oct": 0.34, "genomics": 0.54},
}
@dataclass(frozen=True)
class DemoCohortBundle:
"""Common-model tables plus derived demo views and mapping evidence."""
tables: CohortTables
baseline: pd.DataFrame
trajectories: pd.DataFrame
harmonization_manifest: pd.DataFrame
cohort_summary: pd.DataFrame
seed: int
def _sigmoid(value: float) -> float:
return 1.0 / (1.0 + np.exp(-value))
def _cohort_tag(cohort: str) -> str:
return cohort.lower().replace(" ", "_")
def _source_spec(cohort: str, feature: str) -> tuple[str, str, str]:
"""Return source alias, source unit, and a human-readable conversion."""
alias = f"{_cohort_tag(cohort)}_{SHORT_NAMES[feature]}"
canonical_unit = FEATURE_SPECS[feature][1]
if feature == "hippocampal_volume_mm3" and cohort in {"AIBL", "OASIS"}:
return alias, "cm3", "cm3 x 1000 -> mm3"
if feature == "hba1c_percent" and cohort == "UK Biobank":
return alias, "mmol/mol", "IFCC / 10.929 + 2.15 -> %"
if feature == "rnfl_um" and cohort == "OASIS":
return alias, "mm", "mm x 1000 -> um"
return alias, canonical_unit, "identity"
def _round_trip_source(cohort: str, feature: str, canonical: float) -> tuple[float, str, str, str]:
"""Simulate a cohort-native value, then convert it back to canonical units."""
alias, source_unit, conversion = _source_spec(cohort, feature)
source_value = float(canonical)
if feature == "hippocampal_volume_mm3" and source_unit == "cm3":
source_value = canonical / 1000.0
normalized = source_value * 1000.0
elif feature == "hba1c_percent" and source_unit == "mmol/mol":
source_value = (canonical - 2.15) * 10.929
normalized = source_value / 10.929 + 2.15
elif feature == "rnfl_um" and source_unit == "mm":
source_value = canonical / 1000.0
normalized = source_value * 1000.0
else:
normalized = source_value
return float(normalized), alias, source_unit, conversion
def _diagnosis(score: float, progressed: bool) -> str:
if score < 19.5 or (progressed and score < 22.0):
return "dementia"
if score < 25.5 or progressed:
return "mci"
return "cognitively_unimpaired"
def _participant_setting(cohort: str) -> str:
if cohort == "TLSA":
return "urban"
if cohort == "SANSCOG":
return "rural"
return "reference"
def _manifest() -> pd.DataFrame:
rows: list[dict[str, object]] = []
for cohort in COHORTS:
for feature, (modality, canonical_unit) in FEATURE_SPECS.items():
alias, source_unit, conversion = _source_spec(cohort, feature)
rows.append(
{
"cohort": cohort,
"source_variable": alias,
"canonical_feature": feature,
"modality": modality,
"source_unit": source_unit,
"canonical_unit": canonical_unit,
"conversion": conversion,
"provenance": "synthetic_demo_mapping_v1",
}
)
return pd.DataFrame(rows)
def generate_demo_cohort(seed: int = 42, n_per_cohort: int = 120) -> DemoCohortBundle:
"""Create a reproducible seven-cohort longitudinal demonstration bundle.
The default produces exactly 840 participants. ``n_per_cohort`` is exposed
so tests and development checks can build smaller, faster cohorts.
"""
if n_per_cohort < 4:
raise ValueError("n_per_cohort must be at least 4")
rng = np.random.default_rng(seed)
participant_rows: list[dict[str, object]] = []
visit_rows: list[dict[str, object]] = []
feature_rows: list[dict[str, object]] = []
outcome_rows: list[dict[str, object]] = []
baseline_rows: list[dict[str, object]] = []
trajectory_rows: list[dict[str, object]] = []
for cohort_index, cohort in enumerate(COHORTS):
setting = _participant_setting(cohort)
for person_index in range(n_per_cohort):
participant_id = f"{_cohort_tag(cohort).upper()}-{person_index + 1:04d}"
sex = "Female" if rng.random() < 0.54 else "Male"
sex_binary = 1 if sex == "Male" else 0
age_center = 64.0 if cohort == "UK Biobank" else 70.0
if cohort in INDIAN_COHORTS:
age_center -= 2.0
age = float(np.clip(rng.normal(age_center, 7.0), 48.0, 88.0))
education_center = 9.0 if cohort == "SANSCOG" else (12.0 if cohort == "TLSA" else 14.0)
education = float(np.clip(rng.normal(education_center, 3.0), 0.0, 22.0))
apoe_e4 = int(rng.choice([0, 1, 2], p=[0.69, 0.27, 0.04]))
ancestry_pc1 = float(rng.normal(1.15 if cohort in INDIAN_COHORTS else 0.0, 0.35))
rural_indicator = 1 if setting == "rural" else 0
vulnerability = float(
0.055 * (age - 65.0)
- 0.075 * (education - 12.0)
+ 0.56 * apoe_e4
+ 0.18 * sex_binary
+ 0.28 * rural_indicator
+ rng.normal(0.0, 0.65)
)
baseline_cognition = float(np.clip(27.8 - 1.25 * vulnerability + rng.normal(0.0, 1.15), 15.0, 30.0))
hippocampal_volume = float(np.clip(7100.0 - 310.0 * vulnerability - 20.0 * (age - 65) + rng.normal(0, 320), 3500, 9000))
wmh_burden = float(np.clip(2.2 + 1.1 * vulnerability + 0.10 * (age - 60) + rng.normal(0, 1.0), 0.1, 18.0))
hba1c = float(np.clip(5.45 + 0.17 * vulnerability + 0.18 * rural_indicator + rng.normal(0, 0.35), 4.2, 8.8))
hs_crp = float(np.clip(np.exp(rng.normal(0.05 + 0.20 * vulnerability, 0.45)), 0.1, 12.0))
rnfl = float(np.clip(94.0 - 2.6 * vulnerability - 0.10 * (age - 65) + rng.normal(0, 3.0), 62.0, 112.0))
vessel_density = float(np.clip(48.5 - 1.2 * vulnerability + rng.normal(0, 2.0), 34.0, 58.0))
memory_score = float(np.clip((baseline_cognition - 25.0) / 2.8 + rng.normal(0, 0.25), -3.0, 2.5))
executive_score = float(np.clip((baseline_cognition - 25.0) / 3.1 + rng.normal(0, 0.3), -3.0, 2.5))
logit_hazard = -2.75 + 0.72 * vulnerability + 0.18 * (25.0 - baseline_cognition) + 0.035 * wmh_burden
annual_hazard = float(np.clip(_sigmoid(logit_hazard), 0.018, 0.62))
sampled_event_year = float(rng.geometric(annual_hazard) - rng.uniform(0.05, 0.65))
event = int(sampled_event_year <= 5.0)
event_time_years = sampled_event_year if event else float(rng.uniform(5.05, 5.8))
event_time_days = int(round(event_time_years * 365.25))
modality_available = {
modality: rng.random() >= MISSINGNESS[cohort][modality]
for modality in ("mri", "biochem", "oct", "genomics")
}
n_visits = int(rng.integers(3, 7))
start_date = date(2015 + int(rng.integers(0, 5)), 1 + int(rng.integers(0, 12)), 1 + int(rng.integers(0, 25)))
participant_rows.append(
{
"participant_id": participant_id,
"cohort": cohort,
"sex": sex,
"birth_year": int(start_date.year - age),
"education_years": round(education, 1),
"language": "Kannada" if cohort in INDIAN_COHORTS else "cohort_recorded",
"urban_rural": setting,
"synthetic": True,
}
)
baseline_features: dict[str, float | None] = {}
decline_rate = float(-0.14 - 0.55 * annual_hazard - 0.10 * max(vulnerability, 0.0))
for visit_index in range(n_visits):
year_offset = float(visit_index + rng.normal(0.0, 0.06)) if visit_index else 0.0
baseline_days = int(round(max(year_offset, 0.0) * 365.25))
visit_id = f"{participant_id}-V{visit_index}"
progressed = bool(event and year_offset >= event_time_years)
extra_decline = -0.52 * max(0.0, year_offset - event_time_years) if progressed else 0.0
cognitive_score = float(
np.clip(baseline_cognition + decline_rate * year_offset + extra_decline + rng.normal(0, 0.22), 5.0, 30.0)
)
values = {
"cognitive_score": cognitive_score,
"memory_score": float(np.clip(memory_score + decline_rate * year_offset / 2.2 + rng.normal(0, 0.08), -4, 3)),
"executive_score": float(np.clip(executive_score + decline_rate * year_offset / 2.6 + rng.normal(0, 0.08), -4, 3)),
"hippocampal_volume_mm3": float(max(2800.0, hippocampal_volume - (32 + 35 * annual_hazard) * year_offset + rng.normal(0, 35))),
"wmh_burden_ml": float(max(0.1, wmh_burden + (0.13 + 0.18 * annual_hazard) * year_offset + rng.normal(0, 0.12))),
"hba1c_percent": float(np.clip(hba1c + rng.normal(0, 0.08), 4.0, 10.0)),
"hs_crp_mg_l": float(np.clip(hs_crp * rng.lognormal(0, 0.08), 0.05, 15.0)),
"rnfl_um": float(np.clip(rnfl - 0.16 * year_offset + rng.normal(0, 0.4), 55, 115)),
"vessel_density_percent": float(np.clip(vessel_density - 0.07 * year_offset + rng.normal(0, 0.3), 30, 60)),
"apoe_e4_count": float(apoe_e4),
"ancestry_pc1": ancestry_pc1,
}
for feature, (modality, canonical_unit) in FEATURE_SPECS.items():
available = modality == "cognition" or modality_available.get(modality, True)
if visit_index > 0 and modality in {"mri", "oct", "biochem"}:
available = available and rng.random() > 0.08
if visit_index > 0 and modality == "genomics":
available = False
if not available:
if visit_index == 0:
baseline_features[feature] = None
continue
normalized, source_variable, source_unit, conversion = _round_trip_source(cohort, feature, values[feature])
if visit_index == 0:
baseline_features[feature] = normalized
feature_rows.append(
{
"feature_row_id": f"{visit_id}-{feature}",
"participant_id": participant_id,
"visit_id": visit_id,
"cohort": cohort,
"modality": modality,
"feature_name": feature,
"value": normalized,
"unit": canonical_unit,
"source_variable": source_variable,
"source_unit": source_unit,
"conversion": conversion,
"qc_flag": "pass",
"derived": feature in {"memory_score", "executive_score", "ancestry_pc1"},
}
)
diagnosis = _diagnosis(cognitive_score, progressed)
visit_rows.append(
{
"visit_id": visit_id,
"participant_id": participant_id,
"cohort": cohort,
"visit_index": visit_index,
"age_at_visit": age + max(year_offset, 0.0),
"visit_date": start_date + timedelta(days=baseline_days),
"baseline_days": baseline_days,
"diagnosis": diagnosis,
"cdr_global": 0.0 if diagnosis == "cognitively_unimpaired" else (0.5 if diagnosis == "mci" else 1.0),
"cognitive_status": diagnosis,
}
)
trajectory_rows.append(
{
"participant_id": participant_id,
"cohort": cohort,
"urban_rural": setting,
"year": max(year_offset, 0.0),
"visit_index": visit_index,
"cognitive_score": cognitive_score,
"diagnosis": diagnosis,
}
)
for horizon in (1, 3, 5):
event_by_horizon = int(event and event_time_years <= horizon)
outcome_rows.append(
{
"outcome_id": f"{participant_id}-risk-{horizon}y",
"participant_id": participant_id,
"anchor_visit_id": f"{participant_id}-V0",
"endpoint": f"incident_progression_{horizon}y",
"horizon_days": int(round(horizon * 365.25)),
"event": event_by_horizon,
"event_time_days": event_time_days,
"future_score": np.nan,
"censoring_reason": "study_end" if not event else "",
}
)
baseline_rows.append(
{
"participant_id": participant_id,
"cohort": cohort,
"urban_rural": setting,
"sex": sex,
"sex_binary": sex_binary,
"age": age,
"education_years": education,
"event": event,
"event_time_days": event_time_days,
"event_by_1y": int(event and event_time_years <= 1),
"event_by_3y": int(event and event_time_years <= 3),
"event_by_5y": int(event and event_time_years <= 5),
"annual_hazard_latent": annual_hazard,
**{feature: baseline_features.get(feature) for feature in FEATURE_SPECS},
}
)
participants = pd.DataFrame(participant_rows)
visits = pd.DataFrame(visit_rows).sort_values(["participant_id", "visit_index"]).reset_index(drop=True)
modality_features = pd.DataFrame(feature_rows).sort_values(["participant_id", "visit_id", "feature_name"]).reset_index(drop=True)
outcomes = pd.DataFrame(outcome_rows).sort_values(["participant_id", "horizon_days"]).reset_index(drop=True)
baseline = pd.DataFrame(baseline_rows).sort_values(["cohort", "participant_id"]).reset_index(drop=True)
trajectories = pd.DataFrame(trajectory_rows).sort_values(["participant_id", "year"]).reset_index(drop=True)
summary_rows = []
for cohort, group in baseline.groupby("cohort", sort=False):
summary_rows.append(
{
"cohort": cohort,
"participants": len(group),
"role": "Development" if cohort in PUBLIC_COHORTS else ("India adaptation" if cohort == "TLSA" else "External India validation"),
"setting": _participant_setting(cohort),
"five_year_event_rate": float(group["event_by_5y"].mean()),
}
)
return DemoCohortBundle(
tables=CohortTables(
participants=participants,
visits=visits,
modality_features=modality_features,
outcomes=outcomes,
),
baseline=baseline,
trajectories=trajectories,
harmonization_manifest=_manifest(),
cohort_summary=pd.DataFrame(summary_rows),
seed=seed,
)
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