In high-stakes decision-making, particularly within regulatory submissions to agencies such as the FDA, PMDA, EMA, Health Canada, MHRA, ANVISA, models serve as a weight of evidence approach rather than exploratory tools. Regulatory evaluation extends beyond assessing model fit to determining whether the model is sufficiently credible to support decisions that affect patient safety, labeling, and dosing strategies. This includes consideration of both the risk associated with model-informed decisions and the degree of impact the model has on those decisions. Recent ICH M15 guidance highlights the importance of aligning model credibility with model risk and decision impact, reinforcing that transparency is a fundamental scientific and regulatory requirement.

This blog post is the first installment of a series outlining relevant topics such as practical principles, common pitfalls, and review-driven insights that extend beyond formal guidelines, focusing on what drives reviewer confidence in a modeling approach. In this post, we’ll focus on model transparency.

What Model Transparency Means in Practice

Transparency enables reviewers to understand the model in the context of use, including its structure, assumptions, constraints, how it supports the submission, to independently verify results, and evaluate how these elements influence conclusions. In practice, this level of transparency is achieved through a set of core, interdependent elements that ensure traceability, reproducibility, and scientific rigor throughout the modeling workflow. Transparency is not a single action, but is built through multiple deliberate practices across the modeling lifecycle, including:

1. Explicit linkage between modeling objective and regulatory decision (i.e., context of use)
2. Clear description of model structure, assumptions, constrains, and covariate relationships with scientific justification
3. Traceable parameter values and datasets with documented sources and derivations
4. Executable model code and defined software environments
5. Scientifically justified assumptions and covariate relationships
6. Diagnostics aligned with conclusions
7. Clear communication of uncertainty and limitations

What Documentation Do Regulators Expect?

Regulators expect submissions to provide a complete, reproducible, and decision-focused package. While guidance documents outline requirements, review practice consistently emphasizes clarity, traceability, and reproducibility. To meet these expectations, sponsors must ensure that key components of the modeling workflow are clearly documented and aligned with the decision being supported, including:

1. Clear question of interest linked to the regulatory decision (e.g., dose selection, labeling)
2. Final analysis parameter values and/or datasets with full traceability and documented derivations
3. Model files, scripts, software versions, and instructions
4. Diagnostics package consistent with agency expectations
5. Justified handling of BLQ data, outliers, and missingness with sensitivity analyses
6. Clinically meaningful covariate evaluation
7. Simulations that directly support conclusions and reflect uncertainty
8. Explicit discussion of limitations and their impact on decisions

Transparency is not merely a matter of providing complete information; it is about clearly exposing the scientific logic and assumptions that underly a model. True transparency allows others to understand how conclusions are reached, to rigorously test those conclusions, and to judge their credibility with confidence. Without this level of visibility, even a well-documented model can fall short of being trustworthy.

In the next article, we turn to the context in which interpretability matters most. Specifically, where models play a direct role in shaping regulatory decisions. It is at these critical junctures that the need for clear, defensible, and fully interpretable modeling becomes paramount.