When questioned about a seemingly suspicious cluster of non-responders in trial data, the CEO clarified the event was not statistically improbable. He stated the chance of it happening randomly was a significant 25-30%, demonstrating how to counter narrative-based skepticism with quantitative analysis.
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Deceivers hijack our trust in precision by attaching specific numbers (e.g., "13.5% of customers") to their claims. This gives a "patina of rigor and understanding," making us less likely to question the source or validity of the information itself, even if the number is arbitrary.
Many medtech companies design large trials where a tiny, clinically meaningless response can be statistically significant. Dr. Holman advises entrepreneurs to instead run rigorous trials that prove genuine clinical value, arguing that credible data is the ultimate moat, even if it carries a higher risk of failure.
Vague stories can sound fabricated. Including specific, non-round numbers or precise facts (e.g., "it was 4.2" instead of "around 4") makes a narrative feel more authentic and tangible. This grounds the story in reality and enhances the salesperson's integrity and credibility.
A scientific background can be a major asset in a CEO role, not a liability. The core principles of science—making data-driven, rational, and unemotional decisions—translate directly to the business world. This allows for objective choices that align scientific development with the company's business needs.
The company reports 'overall MMR,' which includes patients maintaining a prior response—a less rigorous metric than 'MMR achievement' (new responses). The CEO notes that discerning investors are focused on the latter, more challenging endpoint, revealing a key area of due diligence for the company's impressive data.
A key warning sign that your KPIs are failing is when leadership meetings devolve into questioning the data's source and meaning. Productive meetings, built on trusted data, bypass this debate and focus immediately on action and strategy: "What are we going to do?"
Manually analyzing 30 data points builds deep intuition and overcomes the tech industry's bias for big data. It's enough to distinguish a major signal (e.g., a 60% rate) from a minor one (10%) and inform immediate action without complex analysis.
After reacquiring a "failed" ALS drug, Neuvivo's team re-analyzed the 200,000 pages of trial data. They discovered a programming error in the original analysis. Correcting this single mistake was a key step in reversing the trial's outcome from failure to success.
The CEO addresses the old belief that inhibiting its target, GSK3-beta, could be dangerous because it was once considered a tumor suppressor. He explicitly states this theory has 'lost its scientific founding' and 'faded into the myth' as research progressed, demonstrating a command of the target's evolving scientific narrative to stakeholders.
At Zimit, the CEO halted lead generation upon finding one inaccurate contact in the CRM. He argued that flawed data renders all subsequent marketing and sales efforts useless, making data quality the top priority over short-term metrics like MQLs.
