Cost Modeling Tool Development

Project Overview This engagement will support the development of a cost-effectiveness modeling tool for a diagnostic assay, with applications in health economic and outcomes research (HEOR), payer value communication, and market access strategy. Estimated time to completion is 3 months. The analyst will work closely with the Evidence Generation team to build, validate, and document a functional Excel- and Python-based model that quantifies the economic value of the assay relative to standard diagnostic approaches. The deliverable will be a well-documented, reproducible, and adaptable modeling tool suitable for use in payer discussions, scientific publications, and future regulatory submissions. Project Phases & Timeline Month 1 Discovery & Model Scoping Literature review of relevant economic models; synthesis of clinical inputs, comparator strategies, and cost parameters; agreement on model structure (decision tree or Markov) and target population; setup of coding environment and version control. Month 2 Model Development & Testing Build core Python model structure; populate with clinical and cost parameters from literature and internal data sources; implement deterministic and probabilistic sensitivity analyses (DSA / PSA); internal review and iterative refinement with supervising team. Month 3 Validation, Visualization & Documentation Cross-validate model outputs against published benchmarks; build output tables and visualizations (tornado diagrams, cost-effectiveness planes, CEAC); write technical documentation and user guide; present final deliverable to stakeholders. Key Deliverables 1 Model Scoping Document Written summary of model framework, target population, comparators, clinical inputs, cost parameters, and data sources — approved by supervising team before build begins. 2 Annotated Literature Review Structured summary of published cost-effectiveness analyses for comparable diagnostic assays, including key model structures, input ranges, and reported ICERs. 3 Python Cost-Effectiveness Model Fully functional, modular Python codebase implementing the agreed model structure with parameterized inputs, DSA, PSA, and clearly commented code checked into version control (Git). 4 Output Visualizations Publication-quality figures including tornado diagrams, cost-effectiveness scatter plots, cost-effectiveness acceptability curves (CEACs), and summary results tables. 5 Technical Documentation & User Guide Clear written documentation of model assumptions, data inputs, parameter sources, code structure, and instructions for updating or adapting the model for future analyses. 6 Final Stakeholder Presentation Slide deck summarizing model rationale, methodology, key results, sensitivity analyses, and limitations — suitable for an internal scientific or market access audience. Responsibilities Model Development • Conduct a structured literature review to identify clinical inputs, utility values, cost parameters, and model structures used in comparable diagnostic cost-effectiveness analyses. • Build a Python-based cost-effectiveness model (decision tree and/or Markov cohort structure) reflecting the diagnostic pathway for the assay versus standard of care comparators. • Implement one-way and multi-way deterministic sensitivity analyses (DSA) and probabilistic sensitivity analysis (PSA) using Monte Carlo simulation. • Calculate and report incremental cost-effectiveness ratios (ICERs), net monetary benefit (NMB), and other relevant health economic outcomes. • Populate model parameters with data from published literature, clinical study results, and standard health economic databases (e.g., Medicare fee schedules, published utility values). Validation & Quality Assurance • Perform face validity checks and cross-validate model outputs against published benchmarks or analogous published models. • Document all assumptions, parameter sources, and modeling decisions in a transparent and reproducible manner. • Respond to internal review feedback and iteratively refine the model as directed by the supervising team. Visualization & Reporting • Generate publication-quality output visualizations including tornado diagrams, cost-effectiveness planes, and CEACs. • Prepare clear summary tables of base case and sensitivity analysis results for both technical and non-technical audiences. • Contribute written sections describing the model methodology and results for potential inclusion in a scientific abstract or manuscript. Collaboration & Communication • Attend regular check-ins with the supervising team to review progress, resolve questions, and align on priorities. • Maintain organized project files, code repositories, and documentation throughout the engagement. • Present findings at a final project debrief to internal stakeholders from evidence generation, market access, and clinical teams. Qualifications Education • High school degree and credits toward a BS/BA program in Engineering, Economics, Bio/Statistics, Public Health, or a related field required. Experience • 0–2 years; research setting experience required (academic lab, clinical research, or industry internship). Coding • Proficient in Python and Excel Domain Knowledge • Coursework or exposure to health economics, HEOR, or decision modeling a strong plus. Technical Skills • Python • MATLAB • Git / version control • Decision tree modeling • Markov cohort models • Sensitivity analysis (DSA / PSA) • Literature review & synthesis • Microsoft Excel • Scientific writing