Reading Clinical Trial Results as an Investor
NovaGo Therapeutics is a Swiss biotech company. When they shared results from a spinal cord injury trial, they quickly changed plans. They decided to make a “new and improved” version of their drug. Investors who only read the headline might think this change was good news. However, experts saw a different story. The first drug failed to meet its goals.
Investors who are not scientists face a hard task. Trial results are full of hard math and medical terms. Companies often make bad results look good. They highlight small wins and hide bad data. Stock prices can jump or drop by 20% based on how people read these reports.
Swiss investors looking at biotech firms in Basel or Zurich face risks. You cannot simply trust what others say. You need a way to spot real success versus unclear math. This guide helps you find the numbers that matter.
The Anatomy of a Trial Result Announcement
Announcements often follow a set pattern. Knowing this pattern helps you find the truth.
Primary endpoint presentation usually comes first in honest reports. This endpoint is the main goal the trial tried to prove. The company picks this goal before the trial starts. For example, BioNTech announced results for a skin cancer treatment. They started by saying the trial met its primary goal.
This statement is the most key fact. It tells you the trial worked.
Statistical significance presentation comes next. These numbers use p-values to show if the result is real or just luck. Learn more about p-values below.
Safety data presentation often appears last. Companies list bad side effects here. They put this at the end so you see the good news first.
Watch out for reports that hide the main goal. If a report talks about “next steps” without saying the main goal was met, take that as a warning sign.
P-Values Explained for Non-Statisticians
A p-value answers one simple question. Could these results happen by pure luck?
- p < 0.05: This number means there is less than a 5% chance the result was random. Here you have the standard rule for success.
- p < 0.01: This number means there is less than a 1% chance the result was random. It shows higher trust in the result.
- p < 0.001: This number means the result is very trustworthy.
P-values do not tell you how well a drug works. A huge study can have a good p-value for a tiny change in health. Always ask how much better the patients got, not just if the math works.
Confidence Intervals: Understanding Uncertainty
A confidence interval gives you a range of likely results. It shows how sure the researchers are.
Imagine a drug claims a 50% improvement. If the range of uncertainty is 10% to 90%, the result is not very stable. If the range is 25% to 35%, the result is much more reliable.
If a range crosses zero, such as -5% to +15%, be careful. The drug might not help at all. The result is not clear enough to trust.
Clinical Significance vs. Statistical Significance
Companies often confuse these two terms.
Statistical significance asks if the result is real.
Clinical significance asks if the result helps the patient.
A drug might lower blood pressure by a tiny amount. The math might say this result is real. However, that tiny change might not help the patient live longer. Doctors care about real health changes. Investors should care too.
Primary, Secondary, and Exploratory Endpoints
Researchers pick goals before they start. This order tells you what counts as success.
- Primary endpoint: The main question. The trial passes or fails based on this goal.
- Secondary endpoints: Extra questions. They support the main goal but do not prove success on their own.
- Exploratory endpoints: Early observations. They help plan future tests.
If a company says the trial missed the main goal but hit secondary goals, be careful. The trial failed its main job.
The Post-Hoc Analysis Warning
Post-hoc analysis happens after the trial ends. Researchers look back at the data to find patterns they did not plan to look for.
If a company talks a lot about post-hoc results, the main results were probably weak. If you look at enough data, you will find some lucky matches. Be skeptical about such findings.
Safety Data: What to Look For
Safety data usually falls into specific groups.
- Adverse events (AEs): Any bad health issues during the trial. compare these rates to the group that did not get the drug.
- Serious adverse events (SAEs): Major issues like hospital stays or death.
- Treatment-related AEs: Bad issues caused by the drug itself.
- Discontinuation rate: The percent of patients who stop treatment. If many people quit, the drug might be too hard to take.
Pay attention if more people in the group die or stop taking the new drug. Even if the company says the drug is safe, these numbers tell the real story.
In one example, Memo Therapeutics showed their treatment was safe in Phase 1. Good because Phase 1 tests for safety. It allowed them to move to the next step.
Swiss Regulatory Context
Swiss biotech firms often run trials that must satisfy rules in Switzerland, the USA, and Europe.
Swissmedic is the Swiss agency for medicines. They have strict rules for reporting. They require dual reviews and quick reporting of bad side effects.
When you look at a Swiss company, check where the trial happened. A trial run in many countries is often stronger than a small study only in Switzerland. However, the rules for reading the results stay the same.
Common Investor Mistakes
- Mistake 1: Confusing math with real health wins. A low p-value does not mean the drug is a cure-all. Look for the size of the benefit.
- Mistake 2: Thinking all phases are equally important. Phase 1 success is just the start. Many drugs fail in Phase 3.
- Mistake 3: Ignoring the comparison group. It is easy to beat a sugar pill. It is hard to beat the best current medicine.
- Mistake 4: Trusting subgroups too much. Finds like “it worked for patients over 65” are often weak if they were not planned ahead of time.
- Mistake 5: Falling for word tricks. Terms like “promising trends” often mean the trial failed the math test.
Your Evaluation Checklist
Use this list when you read a new report.
- Before reading: Check what the main goal was on ClinicalTrials.gov. Check how many patients were in the study.
- While reading: Did they meet the main goal? Is the benefit large enough to matter? Are there safety worries?
- Red flags: Specific focus on secondary goals. High drop-out rates. Claims of success without clear numbers.
- After reading: Ask if the drug is truly better than what exists now. Look for opinions from doctors who are not paid by the company.
Platforms Designed for Informed Investing
Investing in growth-stage biotech takes skill. You must read reports carefully and know when to ask experts. New platforms help Swiss investors manage these risks. They offer tools and checked data to help you decide.
CapiWell plays a key role in this ecosystem. CapiWell helps Swiss investors balance higher-risk biotech bets with more stable investments. By using a multi-asset approach, you can mix growth opportunities with steadier assets like real estate. This strategy helps protect your portfolio while you explore the life sciences sector.
