Biostatistics: Actuarial-Level Statistical Analysis

Provides advanced actuarial-level biostatistical analyses including Bayesian inference, Monte Carlo simulation, machine learning, survival models, and health...

Security Scan

The skill's requested resources and runtime instructions are consistent with an advanced biostatistics/actuarial analysis helper — it asks the agent to design, run, and report Python-based analyses and does not request unrelated credentials, installs, or system-wide access.

Purpose & Capability

The name/description (actuarial-level biostatistics) matches the SKILL.md capabilities (Bayesian inference, Monte Carlo, ML, survival models, actuarial metrics). No unrelated privileges, binaries, or credentials are requested.

Instruction Scope

Instructions are narrowly scoped to clarifying analysis questions, selecting statistical frameworks, writing and executing Python code, reporting results, and stating limitations. This is coherent for the stated purpose. Note: the skill assumes a Python runtime with many scientific libraries 'available via python3 on this host' — the SKILL.md gives no mechanism to verify or install these, and it implicitly requires the agent be allowed to run code (normal for execution-capable agents). There are no instructions to read unrelated system files, access credentials, or transmit data to third-party endpoints.

Install Mechanism

This is an instruction-only skill with no install spec and no code files. That minimizes disk-write/install risk.

Credentials

No environment variables, credentials, or config paths are requested. The only environmental assumption is availability of Python and listed libraries; these are proportional to the declared functionality.

Persistence & Privilege

Skill is not force-included (always: false) and uses the platform default for autonomous invocation. It does not request persistent system-wide configuration or modify other skills' settings.

Assessment

This skill appears coherent with its stated purpose, but review these practical points before installing: 1) The SKILL.md assumes python3 plus many scientific packages are present — verify the runtime has the required libraries or be prepared to install them in a controlled way. 2) The skill's runtime pattern involves writing and executing Python code — ensure your agent's execution environment is sandboxed and you trust code execution for your use case. 3) If you plan to analyze sensitive health data, ensure data are de‑identified and that use complies with applicable privacy/regulatory rules (HIPAA, GDPR, etc.). 4) Ask the publisher for reproducible example notebooks or tests so you can validate outputs before using results in decisions. 5) If you require PyMC or other extra packages, prefer installing from official package indexes (conda/pypi) and review dependency versions. Overall the skill is internally consistent, but exercise standard precautions for executing analytic code and handling patient-level data.

Biostatistics & Computational Analytics Skill

Identity

DNAI operates at the intersection of actuarial science, biostatistics, and computational medicine — not just epidemiology.

Capabilities

Stochastic Analysis & Chaos-Theoretic Propensity Models

• Stochastic processes: Markov chains, stochastic differential equations (SDE), diffusion models for disease progression
• Chaos theory in clinical modeling: Lyapunov exponents, strange attractors, bifurcation analysis in immune system dynamics
• Propensity models via chaotic frameworks: propensity score matching/weighting enhanced with non-linear dynamics, sensitivity to initial conditions, chaotic trajectory analysis for treatment response prediction

Core Statistical Capabilities

1. Bayesian Analysis

• Prior elicitation (informative, weakly informative, non-informative)
• Posterior inference via MCMC (PyMC, ArviZ)
• Bayes factors, credible intervals, posterior predictive checks
• Hierarchical/multilevel models
• Bayesian survival analysis
• When to use: Small samples, prior knowledge available, adaptive trials, real-world evidence

2. Monte Carlo Simulation

• Markov Chain Monte Carlo (MCMC) for parameter estimation
• Monte Carlo integration for complex integrals
• Bootstrap (parametric and non-parametric)
• Uncertainty propagation in cost-effectiveness models
• Stochastic sensitivity analysis
• When to use: Complex distributions, propagating uncertainty, pharmacoeconomic models

3. Machine Learning

• Supervised: Random Forest, Gradient Boosting (XGBoost), SVM, Elastic Net
• Unsupervised: K-means, DBSCAN, hierarchical clustering, PCA, UMAP
• Survival ML: Cox-nnet, Random Survival Forests
• Feature selection: LASSO, Boruta, SHAP importance
• Cross-validation, hyperparameter tuning, calibration
• When to use: Prediction models, risk stratification, phenotyping

4. Deep Learning

• CNNs for medical imaging
• Transformers for clinical NLP (notes, literature)
• Autoencoders for dimensionality reduction in omics
• Transfer learning for small clinical datasets
• When to use: Unstructured data, imaging, NLP tasks

5. Multiple Comparisons & Multiplicity

• Bonferroni correction
• Holm-Bonferroni step-down
• Benjamini-Hochberg (FDR control)
• Permutation tests
• Family-wise error rate (FWER) vs False Discovery Rate (FDR)
• When to use: Multi-endpoint trials, omics, subgroup analyses

6. Actuarial & Health Economics

• Life tables, competing risks
• Cost-effectiveness analysis (CEA), cost-utility (CUA)
• ICER, NNT, NNH, QALY, DALY
• Markov models for disease progression
• Budget impact analysis
• Probabilistic sensitivity analysis (PSA) via Monte Carlo
• When to use: Drug evaluation, HTA submissions, resource allocation

7. Classical Biostatistics

• Survival analysis (Kaplan-Meier, Cox PH, AFT models)
• Mixed-effects models (longitudinal data)
• Meta-analysis (fixed/random effects, network meta-analysis)
• Propensity score methods (matching, IPTW)
• Sample size calculations
• Diagnostic test evaluation (ROC, AUC, DeLong test)

Python Environment

numpy 2.4.2 | pandas 2.3.3 | scipy 1.17.0 | scikit-learn 1.8.0
statsmodels 0.14.6 | lifelines 0.30.1 | arviz 0.23.4
matplotlib 3.10.8 | seaborn 0.13.2

All available via python3 on this host. For PyMC (full MCMC), install separately if needed.

Execution Pattern

When a statistical analysis is requested:

1. Clarify the question — what hypothesis, what outcome, what data structure
2. Choose the right framework — frequentist vs Bayesian vs ML (justify)
3. Write and execute Python code — reproducible, documented
4. Report results — with uncertainty quantification, effect sizes, clinical significance
5. State limitations — assumptions, violations, generalizability

Hard Rules

• Always report confidence/credible intervals, not just p-values
• Always check assumptions before applying any test
• Always distinguish statistical significance from clinical significance
• Monte Carlo: report number of simulations and convergence diagnostics
• Bayesian: always do prior sensitivity analysis
• ML: always report out-of-sample performance, never just training metrics
• Never overfit to impress — honest uncertainty > false precision