Launching a new product is a high-stakes endeavor. You depend on testing data to validate performance, support claims, and satisfy regulators. Yet many companies stumble before they even get to market. Below are three common “testing traps” that can silently undermine your launch readiness and what you should do to avoid them.
Trap #1: Weak or Misaligned Study Design & Protocol
What Goes Wrong
You might believe that any “clinical test” is better than none, but a flawed design can invalidate your results or generate data that regulators will reject.
Some typical errors include:
- Choosing inappropriate endpoints or measurements that don’t align with intended claims
- Switching outcomes mid-study (aka “outcome switching”) after seeing preliminary data, which introduces bias and erodes credibility
- Underpowering the study so that it can’t distinguish meaningful effects (i.e., risk of Type II error)
- Failing to randomize, blind, or conceal properly, which opens the door to bias
Why It’s A Compliance Risk
Regulators expect that the testing you submit maps directly to your intended claims. If your protocol has “wiggle room,” or you retrospectively rationalize changes, you expose yourself to questioning, requests for supplementary data, or outright rejection. In the worst case, you may face regulatory actions for misleading or unreliable results.
How to Avoid It
- Start with the claim first: define exactly what you intend to claim (sensitivity, specificity, detection limits, etc.).
- Build a detailed statistical analysis plan (SAP) before any data review, and hold yourself to it.
- Pre-specify primary/secondary endpoints, stopping rules, and handling of missing data.
Trap #2: Data Integrity & System Validation Oversights
What Goes Wrong
Even with a solid protocol, many companies stumble on data systems, audit trails, and method validation. Examples include:
- Using spreadsheets or legacy systems without audit trails, meaning data changes can’t be tracked or traced
- Failing to validate critical systems (e.g., randomization or supply-management / RTSM systems) under Good Clinical Practice (GCP) or 21 CFR Part 11 principles
- Poor version control or change management in data-capture systems
Why It’s A Compliance Risk
Regulatory reviewers will often flag audits or system weaknesses. If key records lack traceability, it can call into question all of your data. In some warning letters, companies have been cited for using unvalidated spreadsheets or systems that allow deletion or undetected edits.
How To Avoid It
- Use validated Clinical Data Management / EDC systems or platforms designed for regulated trials.
- Ensure every change is audit-trailed, timestamped, and attributable to a unique user.
- Follow a risk-based Computer System Validation (CSV) process (IQ, OQ, PQ) and document it.
- Lock down changes via formal change management, impact assessment, and re-validation when needed.
- Include system validation packages, vendor qualification, and user acceptance testing (UAT) in your trial master file.
- Conduct interim audits / data checks during the study to catch issues early.
Trap #3: Site Execution & Oversight Failures
What Goes Wrong
Your data can be spotless on paper, but if sites don’t follow the protocol or mishandle samples, your results will collapse. This is especially true for testing labs that offer turnaround times or costs that are significantly shorter or less expensive than those of other labs. Some frequent pitfalls:
- Poor training or lack of standardized procedures, leading to inconsistent sample collection or assay execution
- Inadequate monitoring of lab operations (e.g. sample labeling errors, cross-contamination, specimen provenance issues)
- Failure to strictly adhere to delegation logs or oversight of who performed what tasks
- Weak site auditing, oversight, or corrective action when lapses occur
- Cross-border data flow or sample shipping issues (import/export, local regulations) that complicate chain-of-custody
- Inadequate documentation or contracts with sites (e.g., ambiguous responsibilities or deliverables)
Why It’s A Compliance Risk
If one or more sites deviate from protocol, even unknowingly, the validity of your dataset is jeopardized. Regulatory reviewers may discount or exclude suspect data, require re-analysis, or question your QA oversight. In extreme cases, you might face regulatory audits, inspection findings, or an inability to approve products for release.
How To Avoid It
- Develop clear, site-level standard operating procedures (SOPs) and train all personnel before launch.
- Use site initiation visits (SIVs) and ongoing monitoring (remote + on-site) to detect deviations early.
- Implement data quality checks, discrepancy management, and root-cause investigations.
- Maintain a formal delegation log and verify staff qualifications (e.g. certifications, training records).
- For multi-region or cross-jurisdiction studies, ensure that sample shipping, import/export, and data transfer comply with local laws and chain-of-custody rules.
- Structure strong site contracts and agreements with clearly delineated responsibilities, audit rights, and liabilities.
Conclusion: Build Compliance Into Testing, Not as an Afterthought
Product launches that hinge on clinical or diagnostic data can fall apart long before market entry. Not because the core science fails, but because of avoidable compliance mistakes. The three traps above, weak design, data system vulnerabilities, and site execution failures, are among the most common and most damaging.
At CPT℠ Labs, we believe compliance is not a checkbox at the end. It must be embedded throughout study planning, execution, and oversight. A well-run study is not just cleaner scientifically; it’s safer from regulatory pushback, delays, or rejection.
If you’d like help reviewing your protocol, qualifying your systems, or auditing your site operations ahead of launch, contact our Consulting department today.