Advanced Techniques in Stat4tox for Toxicologists

Stat4tox Best Practices: From Data Cleaning to Reporting

1. Project setup and versioning

• Create a project structure: separate folders for raw data, processed data, scripts, results, and reports.
• Use version control: track scripts and configuration with Git; include a README describing data sources and processing steps.
• Document environment: capture software versions (Stat4tox version, R/Python, packages) in a lockfile or session info.

2. Data import and validation

• Standardize formats: require consistent column names, units, and date formats upon import.
• Validate schema: check required fields, data types, and allowed ranges (e.g., dose ≥ 0).
• Checksum raw files: store hashes to detect accidental changes to source data.

3. Data cleaning and harmonization

• Handle missing data explicitly: classify missingness (MCAR/MAR/MNAR) and record decisions (impute, exclude, or model).
• Unit conversion and normalization: convert all measurements to standard units before analysis.
• Outlier management: flag extreme values with reproducible rules; keep original values and document any removals.
• Consistent coding for categorical variables: use controlled vocabularies or ontologies where possible.

4. Reproducible data processing

• Script all transformations: avoid manual edits; implement steps as scripts or notebooks that run end-to-end.
• Parameterize workflows: use configuration files for dataset names, thresholds, and options so analyses are reproducible.
• Use checkpoints: save intermediate datasets with clear filenames (e.g., processed_v1.csv).

5. Statistical analysis best practices

• Pre-specify analysis plans: define endpoints, models, contrasts, and multiplicity handling before running analyses.
• Model selection and diagnostics: choose models appropriate for the data (GLMs, mixed models) and perform diagnostic checks (residuals, fit).
• Adjustment for confounders: include relevant covariates and justify selection.
• Multiple comparisons: control family-wise error or false discovery rate as appropriate.

6. Visualization and exploratory analysis

• Clear, reproducible plots: script plots with labeled axes, units, and legends; save vector formats for publication.
• EDA before modeling: use summary tables, histograms, boxplots, and correlation matrices to understand distributions and relationships.
• Annotation of key findings: annotate plots with sample sizes, p-values, or effect sizes where useful.

7. Reporting and outputs

• Automate reporting: generate reports (HTML/PDF) from scripts or notebooks to ensure consistency between code and results.
• Include provenance: report data version, script versions, parameters, and environment info in the report.
• Provide both summary and full data: include aggregated result tables plus access to the underlying processed dataset for verification.

8. Quality control and review

• Independent code review: have a second analyst review scripts, assumptions, and outputs.
• Re-run key analyses: verify results by re-running from raw data using saved scripts.
• Audit trails: log who ran analyses and when; keep records of manual interventions.

9. Security and confidentiality

• Protect sensitive data: apply access controls, encryption at rest/transit, and de-identification where required.
• Minimal data export: export only necessary fields for reporting; avoid including direct identifiers.

10. Archival and reproducibility

• Package deliverables: include raw and processed data, scripts, environment info, and final reports in an archive.
• Assign identifiers: use versioned filenames or DOIs for major releases of datasets and reports.
• Long-term storage: store archives in a secure, backed-up repository.

Quick checklist

• Project structure and README ✓
• Version control and environment capture ✓
• Schema validation and unit standardization ✓
• Scripted, parameterized workflows ✓
• Pre-specified analysis plan and diagnostics ✓
• Automated, provenance-rich reporting ✓