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README.md

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+---
+language:
+- en
+tags:
+- missing-data
+- lost-to-follow-up
+- retention
+- breast-cancer
+- sub-saharan-africa
+- health-systems
+license: cc-by-nc-4.0
+pretty_name: SSA Breast Missing Data Patterns (Retention & Incomplete Tests)
+task_categories:
+- other
+size_categories:
+- 1K<n<10K
+---
+
+# SSA Breast Missing Data Patterns (Synthetic)
+
+## Dataset summary
+
+This module provides a **synthetic missing-data sandbox** for oncology care in African healthcare contexts, focusing on:
+
+- **Realistic loss-to-follow-up (LTFU) and retention** patterns over 0–24 months.
+- **Incomplete diagnostic and laboratory test results** (ordered vs completed vs available in records).
+- **Non-random missingness** driven by facility type, distance, socioeconomic status (SES), and insurance.
+
+The dataset is anchored in published evidence from:
+
+- The **ABC-DO** sub-Saharan African breast cancer cohort (low LTFU with active tracing).
+- Meta-analyses of **HIV ART retention** (60–70% retained at 2–3 years in routine care).
+- Surveys of **breast cancer pathology services and management** (AORTIC, BMC Health Serv Res, JCO GO).
+- Real-world challenges in SSA breast cancer care (BMJ Open 2021).
+
+All records are **fully synthetic** and intended for methods development and teaching on missing data and retention, not for inference on real facilities.
+
+## Cohort design
+
+### Sample size and populations
+
+- **Total N (baseline patients)**: 6,000.
+- **Populations**:
+  - `SSA_West`: 1,500
+  - `SSA_East`: 1,500
+  - `SSA_Central`: 1,000
+  - `SSA_Southern`: 1,000
+  - `AAW` (African American women): 1,000 (reference/high-resource context)
+
+### Key baseline variables
+
+- `sex`: predominantly `Female` (~96%), with a small proportion of `Male` to allow mixed-sex analyses.
+- `age_years`: 18–90 (mean ~52, SD ~10).
+- `facility_type`:
+  - `Tertiary_urban`
+  - `Regional_hospital`
+  - `District_hospital`
+- `urban_rural`:
+  - `Urban`, `Periurban`, `Rural`.
+- `distance_km` from facility:
+  - Drawn from normal distributions by `urban_rural` (e.g., Urban mean ~5 km, Rural mean ~60 km).
+- `ses` (socioeconomic status): `Low`, `Middle`, `High` with higher `Low` fractions in SSA cohorts.
+- `insurance_status`: `None`, `National_insurance`, `Private`.
+
+These variables drive **missingness mechanisms** (higher LTFU and test missingness with longer distance, low SES, and lack of insurance).
+
+## Follow-up and retention
+
+Patients are scheduled for visits at months:
+
+- `visit_month`: [0, 3, 6, 9, 12, 18, 24].
+
+Retention targets (probability of still in care at 12 and 24 months) vary by facility type, loosely anchored by ABC-DO and HIV ART literature:
+
+- `Tertiary_urban`: 12m ≈ 0.85, 24m ≈ 0.75.
+- `Regional_hospital`: 12m ≈ 0.75, 24m ≈ 0.60.
+- `District_hospital`: 12m ≈ 0.65, 24m ≈ 0.50.
+
+In the generator, a per-interval dropout probability is derived from these targets. This dropout risk is then **modulated by**:
+
+- **Distance**: higher risk for patients living >75 km from the facility.
+- **SES**: higher risk for `Low` vs `High` SES.
+- **Insurance**: higher risk for `None` vs `National_insurance` or `Private`.
+
+At each scheduled month, patients either:
+
+- Remain in care and have a visit record (`visit_attended` True/False).
+- Drop out (no further visits recorded).
+
+Retention at 12 and 24 months is validated against the configuration.
+
+## Test ordering and result availability (missingness)
+
+### Baseline tests
+
+For each patient at baseline, the following are simulated with facility-specific probabilities:
+
+- `baseline_pathology_ordered`
+- `baseline_pathology_result_available` (e.g., ER/PR/HER2 receptors)
+- `baseline_cbc_ordered`
+- `baseline_cbc_result_available`
+- `baseline_imaging_ordered` (staging imaging)
+- `baseline_imaging_result_available`
+
+Approximate patterns by facility type:
+
+- **Pathology receptors**:
+  - High ordering and completion in `Tertiary_urban` (~90%+ completed).
+  - Lower completion in `Regional_hospital` (~65–70%).
+  - Substantial missingness in `District_hospital` (~40–50% completed).
+
+- **CBC and imaging**:
+  - More widely available, but still with gradients by facility and context.
+
+### Follow-up labs (CBC)
+
+At each **attended** follow-up visit (months >0):
+
+- `cbc_ordered` is drawn from facility-specific probabilities.
+- `cbc_result_available` is drawn conditional on ordering and reflects:
+  - Higher completion in tertiary centres.
+  - Lower completion in district hospitals.
+
+This yields **visit-level missingness** that depends on both visit attendance and facility/test capacity.
+
+### Non-random missingness
+
+Missingness is deliberately **not MCAR**:
+
+- Baseline pathology results are more often missing in:
+  - `District_hospital` and `Regional_hospital` than `Tertiary_urban`.
+  - `Low` SES vs `High` SES.
+- Follow-up CBC results are more often missing among:
+  - Patients with long travel distances.
+  - Those without insurance.
+
+The validation script checks for **higher missingness in Low vs High SES** for both baseline pathology and follow-up CBC.
+
+## Files and schema
+
+### Baseline table
+
+Files:
+
+- `missing_data_baseline.parquet`
+- `missing_data_baseline.csv`
+
+Columns (per patient):
+
+- Identifiers and demographics:
+  - `sample_id`
+  - `population`
+  - `region`
+  - `is_SSA`
+  - `sex`
+  - `age_years`
+- Access and facility characteristics:
+  - `facility_type`
+  - `urban_rural`
+  - `distance_km`
+  - `ses`
+  - `insurance_status`
+- Baseline tests (ordered and result availability):
+  - `baseline_pathology_ordered`
+  - `baseline_pathology_result_available`
+  - `baseline_cbc_ordered`
+  - `baseline_cbc_result_available`
+  - `baseline_imaging_ordered`
+  - `baseline_imaging_result_available`
+
+### Visit-level table
+
+Files:
+
+- `missing_data_visits.parquet`
+- `missing_data_visits.csv`
+
+Columns (per scheduled time point while in care):
+
+- Identifiers and baseline covariates:
+  - `sample_id`
+  - `population`
+  - `facility_type`
+  - `urban_rural`
+  - `ses`
+  - `insurance_status`
+  - `distance_km`
+- Visit information:
+  - `visit_month` (0, 3, 6, 9, 12, 18, 24)
+  - `visit_attended` (True/False)
+- Follow-up CBC:
+  - `cbc_ordered`
+  - `cbc_result_available`
+
+Patients with early loss to follow-up have **shorter visit histories**, so the visit table is an **unbalanced panel** that mimics real program data.
+
+## Generation
+
+The dataset is generated with:
+
+- `missing_data_patterns/scripts/generate_missing_data.py`
+
+using configuration:
+
+- `missing_data_patterns/configs/missing_data_config.yaml`
+
+and literature inventory:
+
+- `missing_data_patterns/docs/LITERATURE_INVENTORY.csv`
+
+Key steps:
+
+1. **Baseline cohort**: populations, sex, age, facility type, urban/rural, distance, SES, insurance.
+2. **Baseline tests**: pathology receptors, CBC, and imaging, with ordering/completion probabilities by facility type.
+3. **Visits and retention**: scheduled visits from 0 to 24 months, with facility-specific dropout probabilities tuned to match retention targets and modified by distance/SES/insurance.
+4. **Follow-up CBC**: ordering and result availability for each attended visit, by facility type.
+
+## Validation
+
+Validation is performed with:
+
+- `missing_data_patterns/scripts/validate_missing_data.py`
+
+and summarized in:
+
+- `missing_data_patterns/output/validation_report.md`
+
+Checks include:
+
+- **C01–C02**: Baseline sample size and population counts vs configuration.
+- **C03**: Retention at 12 and 24 months by facility vs configured targets.
+- **C04**: Baseline pathology receptor result availability vs expected rates.
+- **C05**: Follow-up CBC result availability vs expected rates for attended visits.
+- **C06–C07**: Non-random missingness by SES (Low vs High) for baseline pathology and follow-up CBC.
+- **C08**: Overall missingness in key baseline and visit variables.
+
+## Intended use
+
+This dataset is intended for:
+
+- Developing and benchmarking **missing-data methods** (imputation, inverse probability weighting, joint models).
+- Exploring **selection bias** introduced by LTFU and incomplete tests.
+- Teaching about:
+  - How health-system factors (facility, distance, SES, insurance) shape missing data.
+  - Differences between MCAR, MAR, and MNAR mechanisms in realistic African oncology settings.
+
+It is **not intended** for:
+
+- Estimating real-world retention or test completion at specific facilities.
+- Evaluating individual centres or countries.
+- Clinical decision-making.
+
+## Ethical considerations
+
+- All data are **synthetic** and derived from literature-informed parameter ranges.
+- Facility and population labels are generic and **must not** be interpreted as real institutions.
+- The goal is to enable more robust and equitable analyses under realistic data limitations in African healthcare settings.
+
+## License
+
+- License: **CC BY-NC 4.0**.
+- Free for non-commercial research, method development, and education with attribution.
+
+## Citation
+
+If you use this dataset, please cite:
+
+> Electric Sheep Africa. "SSA Breast Missing Data Patterns (Retention & Incomplete Tests, Synthetic)." Hugging Face Datasets.
+
+and relevant literature on retention and pathology services in sub-Saharan Africa (e.g., Foerster et al. 2020, Rosen & Fox 2007, Adesina et al. 2020, Joko-Fru et al. 2021).