Can I use this skill to create oncology models?

Yes, it includes specific patterns for Partitioned Survival Analysis (PSM), which is the standard methodology for modeling progression-free and overall survival in oncology economic evaluations.

How does this skill handle model uncertainty?

It provides comprehensive workflows for Probabilistic Sensitivity Analysis (PSA), allowing you to define parameter distributions and generate CE planes and Cost-Effectiveness Acceptability Curves (CEAC).

Does it support Value of Information (VOI) analysis?

Yes, it includes implementation methods for calculating the Expected Value of Perfect Information (EVPI) and Expected Value of Partial Perfect Information (EVPPI) to prioritize future research.

Which R packages does this skill primarily use?

This skill provides guidance and code patterns for industry-standard packages including BCEA for cost-effectiveness, heemod for Markov modeling, hesim for PSM, and flexsurv for survival analysis.

Health Economics Analysis in R

Name: Health Economics Analysis in R
Author: choxos

bychoxos

Data Science & ML

Performs advanced health economic evaluations including cost-effectiveness analysis, Markov modeling, and probabilistic sensitivity analysis in R.

About

This skill provides specialized implementation patterns for health economists and researchers to conduct rigorous economic evaluations within the R environment. It covers foundational metrics like ICERs and NMB, advanced disease modeling using Markov cohorts and Partitioned Survival Analysis (PSM), and comprehensive uncertainty quantification via Probabilistic Sensitivity Analysis (PSA). By integrating industry-standard packages such as BCEA, heemod, and hesim, it streamlines the creation of health technology assessment (HTA) models and visualizations like Cost-Effectiveness Acceptability Curves (CEAC).

Key Features

Advanced Markov cohort modeling with time-dependent parameters and cycle corrections
Comprehensive ICER, QALY, and Net Monetary Benefit (NMB) calculations
Value of Information (VOI) analysis including EVPI and EVPPI calculations
0 GitHub stars
Partitioned Survival Analysis (PSM) for oncology and chronic disease simulations
Probabilistic Sensitivity Analysis (PSA) with automated CEAC and CE Plane plotting

Use Cases

Conducting budget impact analyses for new medical interventions and pharmaceutical products
Simulating long-term patient health outcomes and costs from clinical trial data
Developing health technology assessment (HTA) models for regulatory and reimbursement submissions

About

Key Features

Advanced Markov cohort modeling with time-dependent parameters and cycle corrections
Comprehensive ICER, QALY, and Net Monetary Benefit (NMB) calculations
Value of Information (VOI) analysis including EVPI and EVPPI calculations
0 GitHub stars
Partitioned Survival Analysis (PSM) for oncology and chronic disease simulations
Probabilistic Sensitivity Analysis (PSA) with automated CEAC and CE Plane plotting

Use Cases

Conducting budget impact analyses for new medical interventions and pharmaceutical products
Simulating long-term patient health outcomes and costs from clinical trial data
Developing health technology assessment (HTA) models for regulatory and reimbursement submissions