How to Design a Holdout Test for Pharma Media
Design pharma media holdouts with clear units, randomization, contamination controls, power, outcome windows, and preplanned analysis.
The short answer
Define the experimental unit, eligible population, treatment, primary outcome, minimum detectable effect, and analysis before launch. Randomize where feasible, monitor contamination, and keep other treatment differences from overwhelming the media contrast.
Choose the unit that can stay separate
Person- or HCP-level randomization can be efficient when identity and suppression work. Household assignment may reduce cross-device spillover. Geographic tests can suit broad channels but require enough comparable markets and controls for concurrent activity. The unit of analysis must respect the assignment unit.
| Design choice | Key question |
|---|---|
| Eligibility | Who could reasonably receive media and generate the outcome? |
| Assignment | Can treatment be random and persistent? |
| Exposure | Is assignment or actual exposure the primary analysis? |
| Outcome | Is it new-to-brand, total Rx, refill, HCP action, or another measure? |
| Window | When can media plausibly affect the outcome, including data lag? |
| Power | What effect can the available sample detect? |
Prefer intention-to-treat logic
Analyzing people by assigned group preserves the benefit of randomization even when not everyone receives an impression. Comparing only exposed users with controls reintroduces selection because exposure depends on auction and behavior.
Control contamination
Map other paid media, publisher direct activity, CRM, field outreach, organic events, and competitive changes. Apply suppressions where possible and measure residual crossover. Perfect isolation is rare; transparent contamination reporting is better than ignoring it.
Pre-register the decision rules
Specify the primary metric, subgroup policy, exclusions, model, significance level or interval approach, and missing-data treatment. Multiple outcomes and repeated peeking increase false-positive risk. An analyst should document deviations.
Hypothetical design
A target list is randomly split 90/10 at the HCP level, with persistent suppression for the holdout. The primary outcome is a prespecified prescribing measure after a defined follow-up. Power calculations show only a large effect is detectable. The team should either accept that limitation, increase sample or duration, or choose a different decision question. Figures are illustrative.
When a holdout is not feasible
Use matched controls, interrupted time series, geo experiments, or triangulation, and state their assumptions. Do not call an observational comparison randomized. Small cohorts may require portfolio-level learning rather than brand-level certainty.
Practical takeaway
Holdout quality is determined before media starts. The next step is a signed test protocol covering assignment, suppression, power, outcomes, window, contamination, and the business action for each result.
Sources
- NIH, Principles and Practice of Clinical Research: Randomization concepts (experimental-design concepts; not a recommendation to treat media studies as clinical trials)
- NIST/SEMATECH, Confidence Intervals
External guidance and platform documentation change. Links were current at publication; check them again before relying on them for a decision.
Editorial note. Analysis and frameworks are the author's own and do not represent Acxiom or any current or former employer, client, or named platform. Examples labeled hypothetical or illustrative are not results from real campaigns. Nothing here is legal, regulatory, or medical advice.
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