'Femi Fajimi

From Numbers to Narrative: Learning to Understand Clinical Data as a Medical Writer 

By ‘Femi Fajimi | 22 May 2025 

One of the most valuable skills I’m developing as a regulatory medical writer is the ability to interpret clinical data clearly and accurately. I’m learning that writing about trial outcomes isn’t just about listing results — it’s about understanding what the data shows, how it relates to the study objectives, and how to communicate it responsibly. 

Why It Matters 

Regulatory writers are responsible for turning complex tables and statistical outputs into narrative summaries that: 

• Accurately reflect study findings 

• Remain objective and non-promotional 

• Are consistent with protocol endpoints and statistical outputs 

• Support ethical, regulatory-compliant communication 

Even without performing the analyses, we need to understand what we’re reporting and what we’re not. 

Example 1: Efficacy Table (EASI Scores) 

Visit	Dermalexiin (n=50)	Placebo (n=50)
Week 4	–38%	–20%
Week 8	–52%	–28%
Week 12	–65%	–35%

How I interpreted it: 

“Participants receiving Dermalexiin 5% cream demonstrated a greater mean percentage reduction in EASI scores at Week 12 compared to those receiving placebo (–65% vs –35%). The treatment effect was evident from Week 4 and continued to improve throughout the 12-week period.” 

Key points: 

• I avoided saying “significant improvement” (no p-value was given) 

• I used “compared to” instead of “better than” 

• I described the trend without overinterpreting the outcome 

Example 2: Safety Table – Adverse Events 

Adverse Event	Dermalexiin (n=50)	Placebo (n=50)
Application site irritation	5 (10%)	2 (4%)
Pruritus	3 (6%)	1 (2%)
Headache	2 (4%)	2 (4%)
Serious adverse events (SAEs)	0	0
Withdrawals due to AEs	1	1

How I interpreted it: 

“The overall incidence of treatment-emergent adverse events (TEAEs) was slightly higher in the Dermalexiin group. Application site irritation (10%) and pruritus (6%) were the most reported AEs, but no serious adverse events occurred in either group. One participant from each group withdrew due to mild AEs.” 

Key points: 

• I reported the frequency and percentage of transparency 

• I didn’t speculate on causality (e.g., “due to the drug”) 

• I stated that SAEs were absent (a clinically relevant detail) 

What This Has Taught Me 

As a developing writer, I’ve realised that data interpretation in medical writing isn’t about deep statistical analysis, it’s about asking: 

• What does this show? 

• Is it aligned with the protocol endpoint? 

• Am I overstating anything? 

• Am I using consistent terminology? 

Understanding how to interpret and present data responsibly helps support clear, compliant, and ethical documentation and builds trust with regulators and trial participants. 

Summary of Key Concepts I’m Applying 

Concept	Why It Matters in Writing
Mean, median, standard deviation	Basic for summarising demographics, scores, labs
P-values and confidence intervals	Needed to report statistical significance accurately
Relative vs absolute change	Helps prevent overstatement of results
AE frequency tables	Core content in safety sections (CSR, IB)
TEAEs	Standard term for adverse events post-treatment

Final Thoughts 

Being a medical writer means bridging the gap between clinical data and communication. As I gain experience, I’m learning to respect the data, understand its limitations, and let the results speak for themselves with clarity and balance. 

Open to feedback — always learning and always aiming to write better.