SBR Stages a Structured Debate on Whether Pharma SMGs Still Earn Their Keep

The journal Statistics in Biopharmaceutical Research doesn’t run structured debates casually. Volume 18, Issue 2 (April–June 2026) contains exactly that: the Davenport et al. primary paper on statistical methodology groups in pharma, two invited commentaries (pp. 206–207, pp. 208–210), a one-page response by José Pinheiro (p. 211), and a three-page rejoinder from the original authors (pp. 212–214). When a journal stages this kind of multi-voice response, it means something is being fought about.

SMGs — sponsor-side groups that develop methodology and consult across project teams — have never had a simple value proposition. They sit between academic research and submission-ready practice, expected to be rigorous enough for publication and pragmatic enough for SAPs. What’s squeezing them now is a double pressure: AI is automating the routine methodological standards work they once owned, while regulators are demanding more sophisticated innovation — Bayesian designs, estimands operationalization, RWE integration — than any project team can absorb alone.

The full text is paywalled, so the specific lines of disagreement across the commentaries remain opaque — whether the tensions concern headcount justification, the applied-versus-research balance, or how CRO-heavy organizations handle methodology governance at all. The rejoinder’s three-page length suggests the authors held their ground rather than conceded much, but that reading is necessarily provisional.

What’s not provisional is the organizational context. The ASA BIOP’s Efficiency+ Working Group — launched July 2025 with roughly 20 members across 10+ companies — is extending statistical innovation into enrollment forecasting, site selection, and drug supply chain: operational territory that a traditional SMG mandate wouldn’t cover. AbbVie’s statistical innovation group, highlighted at the 2025 RISW plenary, shows one large-pharma answer: portfolio-wide ML systems built inside an SMG structure. Meanwhile, a May 2026 Pharmaceutical Statistics viewpoint argues AI will elevate core statistician competencies rather than eliminate them — which implies SMGs have a future, just not necessarily the one they were built for.

The practical question for biometrics leaders is whether SMGs remain the right organizational unit to absorb both pressures simultaneously, or whether that work increasingly belongs to cross-industry working groups, AI-augmented project teams, or some hybrid that doesn’t have a name yet. The debate has started; the answer hasn’t arrived.

Protocol read: Whether SMGs survive as a distinct organizational unit is now a genuinely contested question — the answer will be operational, not theoretical, and biometrics leaders building or restructuring methodology functions in the next year should read the rejoinder with that question in mind.

What to do now:

• For leaders defending SMG headcount, lean on the rejoinder’s argument structure but supplement with locally-grounded ROI examples (estimand operationalization, Bayesian-design uplift) — the abstract debate alone won’t carry a budget conversation.
• Evaluate the Efficiency+ model (operations-extended methodology) as a possible expansion path rather than as a replacement for traditional SMG work.
• Track the Pharmaceutical Statistics viewpoint and the SBR debate together — they’re framing the next two years of organizational-design conversations.