Model Coder

The Challenge

Turning a model specification into a working, validated health economic model is one of the most technically demanding steps in the HTA and market access process. A single Markov cohort model must faithfully translate hundreds of parameters, transition probabilities, cost inputs, and utility weights into code that is not only correct but auditable, reproducible, and ready for submission. The stakes are high: a misspecified transition, a transposed parameter, or an inconsistent formula can undermine an entire value dossier.

Compounding the technical challenge is a practical one: the choice of platform. Some payers and regulators expect to see an Excel model they can open and inspect. Others prefer or require a programmatic implementation in R. Many teams default to one platform based on internal capability rather than what the situation demands, because building the same model in both formats doubles the development time and introduces the risk that the two implementations diverge. The result is a forced trade-off between accessibility and rigor that should not have to exist.

When the pressure is on, corners get cut. Code is copied from previous projects and adapted without full review. Assumptions are hardcoded rather than parameterized. Sensitivity analyses are deferred or implemented inconsistently. The result is a model that works well enough to submit but is fragile, difficult to audit, and expensive to update when a payer asks for a scenario analysis or a structural sensitivity.

And when it comes time for an HTA submission or an internal review, the question is always the same: can we demonstrate, step by step, how this model was built and why every assumption was made? Most manually built models struggle to answer that question completely.

How It's Done Today

Today, building a health economic model from specification to validated, executable code typically takes one week at the very minimum, and often stretches to several weeks depending on model complexity and the experience of the developer. The process begins with a technical team member reading through the model specification, interpreting structural decisions, and translating them into formulas or code line by line. Every transition matrix, every utility weight, every cost parameter must be located in the specification, verified against the source, and implemented correctly.

If the model is needed in both Excel and R, which is increasingly common when different stakeholders have different requirements, the implementation effort effectively doubles. A second developer, or the same developer working in a different language, must rebuild the same logic in a different format, then the team must verify that both implementations produce identical results. In practice, this cross-platform consistency check is difficult to maintain and frequently reveals discrepancies that require additional debugging time.

Once a first draft is complete, the code must be reviewed to catch errors and ensure it matches the specification. Sensitivity analyses, including one-way, probabilistic, and scenario analyses, must be implemented separately, often adding days to the timeline. By the time the model is validated and ready for submission, the team has spent weeks on implementation work that, in principle, is a mechanical translation of decisions already made.

The AI-Enabled Approach

You upload your model specification. From that single document, Model Coder autonomously builds two complete, validated models: a fully functional Excel workbook and an interactive R/Shiny application. The system begins by extracting the structural blueprint from your specification: health states, treatment lines, transition logic, cost categories, and utility structures. It uses that blueprint to drive all downstream generation. Every parameter value is located in the specification, validated, and populated into a structured input layer that both models share.

For the Excel model, the system generates organized input worksheets, builds Markov engine worksheets with cell-level formulas for population tracking and outcome accumulation, creates results summaries, and constructs the infrastructure for one-way and probabilistic sensitivity analyses, including tornado diagrams, cost-effectiveness scatter plots, and cost-effectiveness acceptability curves. For the R model, the system generates modular, well-documented code organized by domain (efficacy, mortality, discontinuation, costs, utilities), assembles it into a cohesive simulation engine, and then builds an interactive Shiny application on top, giving stakeholders a browser-based interface to explore scenarios and adjust parameters without touching code.

Critically, the system does not stop at generation. It validates its own output. The Excel model is checked for formula errors and Markov mass conservation. The R code is executed and tested for runtime errors. When issues are found, the system diagnoses the problem and applies targeted fixes automatically, repeating the cycle until validation passes. What you receive is not a first draft that needs debugging. It is a validated, ready-to-use model in both formats, built from the same specification and producing consistent results across platforms.

The result is not just a faster process. It is a more transparent one. Every parameter is traceable to its source. Every structural decision is documented. Every verification step is recorded. The model arrives with the evidence of its own correctness built in, designed for the scrutiny of auditors, regulators, and internal reviewers.

Built-in Quality Control

Three-Layer Verification

Model Coder does not just generate code and hope for the best. Every model passes through a rigorous, automated verification pipeline before it reaches you.

Parameter Verification checks that every parameter value extracted from the specification falls within its natural limits. Transition probabilities sum correctly, costs are non-negative, utility values are bounded, and discount rates are within expected ranges. Values that fall outside these bounds are flagged for review.

Code Verification compares the generated code against the original specification, domain by domain. The system checks whether the implementation faithfully represents the structural decisions, treatment logic, cost accumulation rules, and utility assignments described in your specification. This is not a syntax check. It is a semantic comparison: does the code do what the specification says it should do?

Black-Box Testing evaluates the model's behavior under controlled conditions. The system runs a comprehensive suite of behavioral tests: zeroing all costs to confirm total costs equal zero, setting all utilities to one to verify QALYs equal life-years, adjusting time horizons to confirm results respond appropriately, checking cohort conservation at every cycle, verifying that the dead state never decreases, and testing monotonicity of costs and utilities when parameters change. These tests verify that the model behaves correctly as a system, not just that individual formulas are syntactically valid.

The results of all three verification layers are documented in structured QC reports that cover what was checked, what passed, what needs attention, and why.

Interactive Review and Revision

You Review. You Decide. The System Implements.

After the model is built and verified, you do not simply receive a file and a report. You enter an interactive review session with the system.

In this session, you can explore QC findings in detail, ask questions about specific parameters, formulas, or structural decisions, and understand exactly why a particular check passed or flagged an issue. If something needs to change, you build a revision plan collaboratively with the system, specifying what should be modified and why.

When you finalize your revision plan, the system applies each change sequentially to the model, then re-runs the full verification pipeline. A delta QC report shows you exactly what changed: which checks were resolved, whether any new issues were introduced, and how the revised model compares to the original. You can iterate on this process until you are satisfied with the model's quality.

This is not a one-shot generation. It is a structured, transparent collaboration between you and the system, where the researcher drives every scientific decision and the system handles the implementation.

What It Means for You

• One specification produces two complete models, Excel and R/Shiny, eliminating the platform trade-off that forces teams to choose between accessibility and programmatic rigor.
• Both models are built from the same structural extraction and parameter set, so results are consistent across platforms without the manual cross-verification that dual implementations normally require.
• Sensitivity analyses are built in from the start: one-way and probabilistic sensitivity analyses, tornado diagrams, cost-effectiveness scatter plots, and cost-effectiveness acceptability curves are generated automatically, not bolted on after the fact.
• The R/Shiny application gives non-technical stakeholders a browser-based interface to explore scenarios and adjust parameters, making the model accessible to audiences who would never open a code file.
• Built-in quality control catches formula errors, mass conservation violations, and runtime failures before you ever see the output. The system diagnoses and fixes its own mistakes autonomously.
• What once required one to several weeks of implementation, debugging, and review is delivered as a validated, ready-to-use model, freeing your team to focus on interpretation and strategic analysis rather than mechanical translation.
• Every parameter in the model is traced back to its source in the specification. You can follow any value from the model output to the evidence that informed it.
• Quality control is not a separate step you perform after receiving the model. It is built into the generation process itself: parameter verification, code verification against the specification, and behavioral testing are all automated and documented.
• After generation, an interactive review session lets you explore QC findings, ask questions, and collaboratively plan revisions. The system applies your changes, re-validates, and shows you exactly what improved.