For executives in the wellness industry, the primary challenge around innovation is not speed, but deciding where to invest. It’s important to develop new ingredients, launch new formulations, and enter emerging categories, but how can you prioritize where to invest without data?

Clinical research plays an important role, but only if studies are well designed. Further, understanding that not all scientific findings serve the same purpose affords companies the chance to move into a new system of evidence generation. This system can de-risk innovation by going beyond proving a product works and validating the decisions that have already been made, but by taking a systematic approach to exploring what happens next. This is using scientific evidence from the start rather than trying to fit it in after the decisions and investments have been made. However, regardless of how you use the science, the rigor should be the same and there are certain criteria to obtain rigor that need to be considered in order to rely on the science for decision-making or supporting claims.

Every investigation must have clear defined objectives, which ensures the trial is designed and powered appropriately to answer the question(s) of interest, and that data sets are correctly analyzed to answer the question(s). (1) The objectives of a study can be broadly classified into primary and secondary objectives, whereby the study’s hypothesis defines the primary objective. Additional research questions may also be addressed, and those are secondary objectives. And a powered outcome refers to the specific result that will calculate how many participants are needed to measure that main result:

It’s important to understand that the powered outcomes are specific measurement goals, which dictate the required sample size and possess a high, pre-calculated statistical probability of detecting a true clinical effect. Power impacts multiple factors including questions, type of data and how it is collected. Those powered outcomes are the tool to guide innovation investment and pipeline decisions, as they answer the priority questions with confidence. They are often used to support claims and may also inform investment and IP decisions.

In any given trial, there is a great deal more data than is directly relevant to the primary objective. Secondary objectives and endpoints are those extra measures of interest defined when designing the study. However, they are usually not used to calculate the trial size, which may mean they are “underpowered” to show small changes or the findings are not statistically significant.

This doesn’t mean that those non-powered or exploratory findings are not valuable. Quite the opposite when thinking about evidence generation as an innovation and business development strategy. The secondary outcomes provide a more complete picture of the effects of an intervention, delivering signals that can shape what to validate next. Particularly when paired with subpopulation analysis, the deeper data can reveal who responded, how strongly, under what conditions, and against which outcomes. This allows the team to understand:

Directional signals can’t differentiate between true effect or data variability. They are commercially useful insights, but not powered to the same level as primary outcomes.

Each study answers a question on whether a product worked: the outcome that was powered. It can also make the next business decision smarter: exploratory insights. One study sharpens the next product decision, and the next study sharpens the next platform decision. This circle of innovation moves from trial to insight, and pipeline to trial. Ultimately, this means a business can move from assumption-led innovation to evidence-informed investment.

There is another critical consideration related to study outcomes, and that is study design. Each study must be designed appropriately to reliably detect meaningful effects, as well as delivering clear signals on which direction may be worth pursuing. One criticism of decentralized clinical trials (DCTs) is that they could be underpowered because of data variability, protocol deviations, or attrition. However, this criticism is based on a misunderstanding that DCTs do not have the same rigor as site-based studies; study design and protocol is the way to ensure a trial is appropriately powered to meet its objectives with high-quality data. In addition, it has been suggested that a DCT can have increased enrollment and higher retention by reducing participant burden, while yielding treatment effects similar to site-based trials. (2)

Radicle Science custom designs each study, which is IRB approved, with a focus on identifying the appropriate primary and secondary outcomes, and is powered to find an effect in the target population.

“In the context of a clinical trial, power is all about giving a study its best chance to detect an effect if there really is one there,” says Valerie Starratt, PhD, vice president of science and data, Radicle Science. “By considering the outcome measure, the nature of the intervention and how it’s supposed to work, and how many participants the study will include, you can calculate the probability that the study won’t miss something that actually exists.”

There is no shortage of compelling hypotheses in the wellness category, with credible background science, interesting mechanisms of action, or a compelling consumer need. However, a product that sounds interesting on paper can still fail to create a meaningful outcome in one population, while delivering interesting signals for a new direction to explore. This helps companies decide where to invest capital, talent and attention.

Evidence generation changes a company’s innovation strategy. Powered outcomes help leaders decide where to invest. Exploratory findings help teams decide what to develop and validate next. Together, they turn research into something more than just a study. It’s an operating system for smarter innovation. By treating evidence as a strategic asset and using data to focus the innovation pipeline, the next generation wellness companies can make informed decisions on which opportunities are truly investable for greatest success.

1. Indian J Psychiatry. 2024;66(5):472-76.

2. Clin Pharmacol Ther. 2022 May 17;112(2):344-352. DOI:10.1002/cpt.2628

Evidence-based innovation is the practice of using clinical data to decide which ingredients, formulations, and claims are worth investing in before commitments are made. Instead of fitting research in after decisions are set, it puts evidence at the start of the pipeline. This gives leaders a defensible basis for capital, talent, and attention.

Powered outcomes are the primary endpoints a trial is sized to answer with statistical confidence, so they carry the weight for claims and major investment decisions. Exploratory findings are secondary signals the study was not powered to prove, but they point to what to validate next. One tells you whether the product worked. The other tells you where to look next.

Sub-population analysis breaks trial outcomes down by meaningful subgroups to reveal who responded, how strongly, and under what conditions. A product with a neutral top-line result can still work dramatically well for a specific population. That signal shows teams which formulations to refine and which populations to focus on.

Yes. The rigor of a decentralized clinical trial comes from study design and protocol, not from where the participant is located. A well-designed DCT is powered to meet its objectives with high-quality data, and evidence suggests DCTs can improve enrollment and retention while yielding treatment effects similar to site-based trials.

Companies use each study to answer a priority question and to sharpen the next decision. Powered outcomes guide where to invest, while exploratory findings and sub-population analysis shape what to develop next. This moves a business from assumption-led innovation to evidence-informed investment.