Seedlabs

Detectability Budget: Pre-Award Power Planner for Quasi-Experimental Evaluations

A tool for researchers to check if their study group is large enough to produce reliable results.

A design-stage calculator that tells research funders and evaluation contractors, before a DiD study is funded, whether the planned panel is large enough to rule out parallel-trends violations that would overturn the findings.

MathematicsAdvanced Causal Inference Techniques
Attention Deficit Hyperactivity DisorderHealth services research funding and evaluation procurement — grant review and study design for agencies and research contractors.

Concept

A web-based, design-stage calculator for quasi-experimental health evaluations. Before a DiD study is funded or contracted, the user enters the planned panel shape (number of treated and comparison units, number of pre- and post-period time points), the expected treatment effect size, and the largest bias they can tolerate. The tool returns:

  1. A power readout for the non-inferiority parallel-trends test at that design — e.g., "with 40 hospitals and 12 quarters you can rule out violations above 35% of the expected effect with 80% power."
  2. A minimum viable design — the unit and time-point combinations needed to rule out violations at the stated margin.
  3. A one-page "detectability budget" memo that can be attached to a grant application or RFP response.

The wedge: the first pre-award number that tells a funder whether a proposed DiD evaluation's core causal assumption is testable at all — before the money is spent.

Why now

  • The new paper supplies the calibration a planner needs: in simulations at ACA-dependent-coverage scale (60 units, 25 time points), the non-inferiority test achieved 88-100% power against large violations (> |5|) but only 21-60% against smaller ones (> 2.1) — testability varies sharply and predictably with design size and violation magnitude, exactly the relationship a planner encodes [0].
  • The conventional parallel-trends test has no design-stage counterpart: because it "passes" when underpowered, funders currently cannot distinguish a testable design from an untestable one before results come in [0].
  • Margin-setting is a mature, regulator-accepted practice from non-inferiority drug trials [1], so funders can adopt the same construct for policy evaluations without new methodology.

Written by GLM · 13 Oct 2026

AI assessment

Backed by 2 papers88

A high-utility, narrow-scope tool that transforms a theoretical statistical improvement into a practical procurement gate for high-stakes health research funding.

Evidence strength
5/5
The idea directly operationalizes the simulation results from the provided text, which explicitly quantify how power varies with unit count and time points for non-inferiority tests.
Market pull
4/5
Major funders like PCORI and AHRQ manage multi-million dollar portfolios where 'failed' studies due to untestable assumptions represent significant wasted capital.
Novelty & moat
3/5
While TrendClear provides the analysis module, this shifts the value proposition from 'post-hoc analysis' to 'pre-award planning,' creating a distinct commercial wedge.
Feasibility
5/5
The tool is essentially a calculator based on known power curves and simulation parameters, requiring minimal engineering to build a credible MVP.
Wedge clarity
5/5
The 'detectability budget' memo is a sharp, specific deliverable that fits perfectly into existing grant application and RFP workflows.
Simplicity / focus
5/5
It avoids 'platform' creep by focusing on a single, clear function: calculating the viability of a DiD design before funding.

Scored by Gemma 4 · 13 Oct 2026. Rated against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity), relative to the closest ideas already in the catalog. A prior estimate to compare ideas before real-world signal arrives.

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Who benefits

  • Agency for Healthcare Research and Quality (AHRQ)organization

    AHRQ funds and commissions evaluations of care-delivery and policy interventions; requiring a detectability budget in applications would screen out quasi-experimental designs whose causal assumptions cannot be tested at the proposed scale.

  • Mathematicacompany

    Mathematica competes for federal evaluation contracts; attaching a power-based design memo to proposals demonstrates up front that the planned DiD can rule out assumption violations, a differentiator in methodological review.

  • PCORI (Patient-Centered Outcomes Research Institute)organization

    PCORI funds comparative effectiveness research that frequently uses quasi-experimental designs; a pre-award testability check raises the rigor of the funded portfolio.

  • Robert Wood Johnson Foundationorganization

    RWJF funds health policy research where DiD is a default design; a detectability budget gives program officers a concrete, quantitative gate for proposal review.

Research it builds on

  1. Nothing to See Here? A Non‐Inferiority Approach to Parallel Trends
    Alyssa Bilinski, Laura A. Hatfield · 2026 · 102 citations
    All ideas from this paper →
  2. Transitioning to active-controlled trials to evaluate cardiovascular safety and efficacy of medications for type 2 diabetes
    Darren K. McGuire, David D. D’Alessio, Stephen James Nicholls et al. · 2022 · 12 citations
    All ideas from this paper →
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feasibility