TRUST Research Center

05/10/2026

🚀 Introducing StatTest Finder by TRUST Research Center! 📊🩺

Choosing the right statistical test shouldn’t be confusing or time-consuming. StatTest Finder helps medical researchers identify the most appropriate statistical test through a simple series of questions about their study design, variables, and research objective.
🔹 Answer a few guided questions
🔹 Get a tailored statistical test recommendation
🔹 Save valuable time in your research
🔹 Designed specifically for medical and health researchers

📱 Now available on Google Play:
https://play.google.com/store/apps/details?id=org.trustresearch.stattestfinder

🍎 Using an iPhone or iPad?
You can access StatTest Finder directly through your browser:
https://app.trustresearch.org/

🔬 Stop wondering which statistical test to use. Let StatTest Finder guide you.

Developed by TRUST Research Center.

04/10/2026

🔬 Not every “negative” study is a failed study.

Scientific integrity depends not only on publishing studies that show statistically significant benefits, but also on reporting findings that are neutral, negative, or contrary to expectations. When negative studies remain unpublished, the scientific record becomes distorted, publication bias grows, and researchers, clinicians, and patients may be misled about the true effects of an intervention.

But there is another important lesson: a p-value should never be the sole judge of whether a finding matters.

A statistically significant result does not necessarily mean that the effect is clinically important. Conversely, a non-significant result does not necessarily mean that there is no meaningful effect—it may simply reflect insufficient precision or statistical power.

👉 Instead of asking only “Is p < 0.05?”, we should ask:

- How large is the treatment effect?
- How precise is the estimate?
- Does the confidence interval include a clinically meaningful effect?
- Does the effect exceed the Minimal Clinically Important Difference (MCID)?

The 95% confidence interval should be interpreted alongside the MCID, rather than treating statistical significance as a simple “yes/no” verdict. This approach helps distinguish results that are statistically significant but clinically trivial from results that may be clinically important but remain inconclusive because of limited precision.

📚 This discussion is part of Chapter 10: “Navigating Health Research: From Fundamentals to Advanced Practices in Biostatistics and Study Design.”

🎥 Watch the video to explore why negative studies matter—and why we need to look beyond the p-value.

25/09/2026

🎉 **A Three-Year Journey — Finally Published!** 📚✨

After three years of dedication, collaboration, countless revisions, and hard work, we are incredibly proud and grateful to celebrate the publication of:

📖 **Navigating Health Research: From Fundamentals to Advanced Practices in Biostatistics and Study Design**

This book is much more than a publication. For us, it represents a promise, a journey, and a legacy.

The journey began with Professor Ahmed Hassouna , who initiated this project and brought us together with the vision of creating a comprehensive resource to guide researchers, students, and clinicians through the challenging journey of health research.

Sadly, Professor Hassouna passed away before seeing the book completed. We felt a responsibility to honor his vision and fulfill the promise to finish what he had started. Continuing this work became a tribute to his memory and to the vision with which he began the project.

After Professor Hassouna, Professor Aliaa Rehan took the lead and carried the project forward with remarkable dedication. Her guidance, persistence, hard work, and commitment throughout the long editorial journey were instrumental in bringing this book to completion. We are deeply grateful for everything she contributed to making this goal a reality. 🌷

We were also truly privileged to work alongside Professor Eugenio Pompeo, whose sincere support, collaboration, and encouragement throughout the pathway were invaluable. His contribution made this journey richer, and we are grateful to have shared this experience with him.

And a special appreciation goes to Dr Mohammed Gumaa for his valuable contribution to the editorial work, coordination, and organization that helped move the project forward through its many stages.

For TRUST Research Center, this publication carries an especially special meaning. We are incredibly proud that three of the four editors — Professor Ahmed Hassouna, Professor Aliaa Rehan Youssef, and Dr Mohammed Gumaa — are founders of TRUST Research Center. This book therefore reflects not only an academic collaboration, but also a part of the journey and vision of TRUST itself. ❤️

From an idea started three years ago…
through loss, perseverance, collaboration, and countless hours of work…
to finally holding the published book in our hands.

**The promise has been fulfilled.** 📚

Today, we celebrate the publication of this book, but we also celebrate the people, vision, friendship, and dedication behind it.

To the memory of Professor Ahmed Hassouna — this achievement carries your name and your vision with it.
To Professor Aliaa Rehan Youssef, Professor Eugenio Pompeo, and Dr Mohammed Gumaa — thank you for making this journey possible.

🌟 **From fundamentals to advanced practices, we hope this book helps researchers navigate the journey toward better research, better evidence, and ultimately better health.**

**Proudly published by Academic Press / Elsevier.** 📖

19/09/2026

19/09/2026

🍲 **Systematic Review & Meta-Analysis: The Art of Cooking Scientific Evidence!** 🔬👨‍🍳

Thomas Keller once said:
"A recipe has no soul. You, as a cook, must bring soul to the recipe."

The same applies to a **systematic review and meta-analysis. 📚✨

Conducting a meta-analysis is much like preparing a delicious meal. The quality of the final dish depends on every step of the process:

🛒 Searching the market (Literature Search)
Just as a chef searches for the best ingredients, researchers search the literature to find all relevant studies.

🥕 Selecting and preparing ingredients (Data Extraction)
The right data must be carefully collected from each study, just as ingredients are carefully chosen and prepared before cooking.

🔍 Quality Assessment
A skilled chef checks the freshness and quality of ingredients. Similarly, researchers assess the quality and risk of bias of included studies.

⚖️ Effect Size Calculation
This is where the ingredients are measured and combined. Effect sizes are calculated, weighted, and synthesized to estimate the true effect.

🍲 Choosing the Cooking Method (Fixed vs Random Effects Models)
Different recipes require different cooking techniques. Likewise, meta-analyses require selecting the appropriate statistical model.

📊 Testing Homogeneity (Q Test & I²)
Are all ingredients blending together smoothly? Researchers evaluate heterogeneity to understand how consistent the study results are.

🧂 Moderator Analysis & Meta-Regression
Sometimes a small change in ingredients can alter the final taste. Moderator analyses help identify factors influencing study outcomes.

✅ Sensitivity Analysis & Publication Bias Assessment
Before serving the meal, a chef tastes and double-checks everything. Researchers perform sensitivity analyses and assess publication bias to ensure robust conclusions.

🎯 A systematic review is not just collecting studies—it is transforming scattered pieces of evidence into a meaningful, reliable conclusion that can guide clinical practice and future research.

Want to become the chef who can turn research evidence into high-quality scientific conclusions? 👨‍🍳📚🔬

Want to learn systematic review & meta-analysis in details, join our upcoming course?

📩 To receive full course details on WhatsApp, click here:
https://wa.me/201119678899?text=Systematic+review+%26+Meta-analysis+course+details

08/09/2026

📊 The right sample size is the foundation of a reliable clinical study.

Too few participants may leave your study underpowered, making it difficult to detect a real effect.

Too many participants can waste time, resources, and effort unnecessarily.

So, how do you determine the right sample size for your research?

🎯 Our Sample Size Calculation for Clinical Research course is designed to make this complex topic simple, practical, and understandable.

Over 5 days | 2 hours/day | Online, you will learn:

🧠 The key concepts behind sample size calculation
Understand the factors that determine how many participants your study needs.

📊 Sample size for different outcome types
From continuous and categorical outcomes to time-to-event outcomes.

💻 Hands-on practical training
Learn how to calculate sample size using G*Power and free online calculators.

🔍 Practical examples
See how sample size calculations are applied to real clinical research scenarios.

🎥 Flexible learning
Missed a lecture? No worries. All lectures are recorded and available anytime.

Whether you are planning a clinical trial, observational study, or another type of clinical research, understanding sample size calculation is essential for designing a study that can produce meaningful and reliable results.

🎯 Right sample. Reliable results. Stronger research.

📩 Want to learn more about the course? Get the full details on WhatsApp: https://wa.me/201119678899?text=Sample+Size+Calculation+course+details

31/08/2026

⏰ TODAY IS THE LAST DAY to catch the 1,000 EGP discount!

Are you planning to conduct a Systematic Review & Meta-analysis of Randomized Controlled Clinical Trials?

Our practical online course is designed to guide you through the complete journey — from your research question to evidence synthesis and final conclusions. 📚🔎📊

Over 10 days (20 hours), you will learn:

🔎 How to conduct a systematic review step by step
📋 How to extract and manage study data
⚖️ How to assess the risk of bias using the Cochrane RoB 2 tool
📊 How to perform a meta-analysis using fixed- and random-effects models
🌲 How to create and interpret forest plots
📈 How to assess heterogeneity and publication bias
🏆 How to evaluate the certainty of evidence using GRADE

💻 Practical training with powerful FREE tools:
• TERA
• Meta-Essentials
• OpenMeta[Analyst]
• RevMan

🎥 Missed a lecture? No worries! All lectures are recorded and available for you.

🔥 Today is your last chance to save 1,000 EGP on the course. Don't miss the discount!

📩 Get full course details and join us via WhatsApp:

https://wa.me/201119678899?text=Systematic+review+%26+Meta-analysis+course+details

26/08/2026

📚 *Systematic Review in simple steps*

A systematic review is NOT just reading papers.
It is a structured scientific method to collect, evaluate, and synthesize evidence with minimal bias.

🔹 Main stages include:

1️⃣ Define a focused research question
2️⃣ Perform a comprehensive literature search
3️⃣ Critically assess study quality & risk of bias
4️⃣ Extract and synthesize data
5️⃣ Report findings using PRISMA guidelines

📈 Sometimes a meta-analysis is added to statistically combine results from multiple studies.

💡 Important concept:
The strength of a systematic review depends on:
✔ quality of included studies
✔ quality of methodology
✔ transparency of reporting

---

Want to learn systematic review & meta-analysis practically from A to Z?

📩 To receive full course details on WhatsApp, click here:
https://wa.me/201119678899?text=Systematic+review+%26+Meta-analysis+course+details

24/08/2026

📊 Why does Repeated Measures ANOVA often detect differences when Independent ANOVA does not?
The answer lies in one word:
🔇 Noise
This infographic explains one of the most powerful concepts in biostatistics using a simple "house of variability" analogy 👇
---
🏠 The House of Variability
Whenever we analyze data, the total variability can be thought of as a house.
Part of that variability is:
✅ The signal (effect we want to detect)
and part is:
❌ The noise (unexplained variability)
The challenge is that noise can hide real effects.
---
👥 Independent One-Way ANOVA
Imagine comparing:
🩺 Patients with cirrhosis and HCC
🩺 Patients with cirrhosis without HCC
🩺 Healthy controls
These are completely different individuals.
Each person brings unique characteristics:
✔ genetics
✔ lifestyle
✔ metabolism
✔ disease severity
All these individual differences become part of the error term.
🏠 Result:
A large portion of the house is occupied by unexplained variability.
---
🔄 Repeated Measures ANOVA
Now imagine following:
👤 The SAME patient
at:
🕐 Time 1
🕑 Time 2
🕒 Time 3
Instead of comparing different people, we compare each person with themselves.
💡 The person's natural characteristics remain constant.
As a result:
✂️ A large source of variability is removed from the error term.
---
🏠 The House Gets Cleaner
In Independent ANOVA:
📦 Treatment Effect
+
🗑️ Subject Differences
+
🗑️ Random Error
all compete within the analysis.
In Repeated Measures ANOVA:
📦 Treatment/Time Effect
+
📦 Subject Variability (separated out)
+
🗑️ Smaller Residual Error
The unexplained "mess" becomes much smaller.
---
📈 Why This Matters
The F-ratio is essentially:
📌 Signal ÷ Noise
When noise decreases:
⬆️ F-value increases
and
⬇️ P-value decreases
making it easier to detect true differences.
---
💊 The Warfarin Example
The infographic demonstrates how repeated measures analysis:
✔ reduced unexplained error substantially
✔ increased the F-statistic
✔ improved statistical power
without collecting a single additional participant.
💡 This is one of the most efficient ways to increase study power.
---
⚠️ Important Assumption
Repeated Measures ANOVA requires:
🔄 Sphericity
This means the variability of differences between time points should be similar.
When violated, adjustments such as:
✔ Greenhouse-Geisser correction
✔ Huynh-Feldt correction
may be required.
---
📌 Bottom line:
Independent ANOVA asks:
👥 "Are different groups different?"
Repeated Measures ANOVA asks:
👤 "How does the same person change over time?"
By removing between-person variability from the error term, Repeated Measures ANOVA often achieves:
🎯 Higher power
🎯 Higher F-values
🎯 Lower p-values
🎯 More efficient studies
---
Want to understand statistics using simple clinical examples instead of complicated formulas?
👉 Join our upcoming **Statistics for Clinicians (S4C)** course.
📩 To receive full course details on WhatsApp, click here:
https://wa.me/201119678899?text=I%20want%20to%20join%20the%20Statistics%20for%20Clinicians%20course

20/08/2026

⏰ TODAY IS THE LAST DAY!

If you’ve been thinking about joining our Statistics for Clinicians (S4C) course, this is your final chance to catch the 1,000 EGP discount! 🔥

📊 Statistics for Clinicians (S4C) is a practical online course designed to help you:

🧠 Understand statistical concepts
📈 Analyze your data correctly
🔍 Interpret statistical results with confidence
🎯 Make your data meaningful

💻 12-day online course
⏱️ 2 hours/day
🎓 Hands-on training using SPSS + free software (JASP or Jamovi)
🎥 All lectures are recorded for flexible learning

Whether you're a clinician, resident, researcher, or simply want to become more confident in handling medical data, S4C is designed to take your statistical skills to the next level.

🔥 SAVE 1,000 EGP — TODAY IS THE LAST DAY TO CATCH THE DISCOUNT!

📩 Want the full course details?
Send us a WhatsApp message now:
https://wa.me/201119678899?text=S4C%20course%20details