TRUST Research Center

🍎🍊 “We can’t compare apples to oranges. Or May be we can!!!"

Propensity Score Matching (PSM)
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⚠️ **The core problem: Selection Bias**

In real-world studies, treatment groups are often different *before treatment even starts.*

Example:

💊 New treatment group → younger, lower-risk patients
🏥 Standard treatment group → older, sicker patients

So if outcomes differ later…
❌ it may NOT be because of the treatment itself.

This is the classic:

🍎 **Apples vs Oranges problem**

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🎯 **What PSM tries to do**

PSM creates *fairer comparisons* by matching patients with similar baseline characteristics.

Think of it as:

🧍‍♂️ Matching each treated patient
with
🧍‍♀️ a similar control patient

based on:
✔ age
✔ s*x
✔ comorbidities
✔ disease severity
✔ other baseline variables

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📌 **The 4-step idea behind PSM**

1️⃣ Collect baseline characteristics
2️⃣ Calculate a “propensity score”
→ probability of receiving the treatment

3️⃣ Match patients with similar scores
4️⃣ Compare balanced groups

⚖️ After matching, groups become more comparable → less biased analysis

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💡 Important insight:

PSM does **NOT** create perfect randomization.

It only balances:
✔ measured variables

But:
❌ hidden or unmeasured confounders may still exist

That’s why:
🧪 RCTs remain the gold standard
📊 But PSM greatly improves observational studies

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📌 Bottom line:

Without adjustment:
🍎 ≠ 🍊

With proper matching:
🍎 🤝 🍊

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To learn more about observational studies and PSM, join our upcoming course

👉 https://wa.me/201119678899?text=I%20want%20to%20join%20the%20observationa%20studies%20course

📊 **Correlation vs Regression — one of the most misunderstood concepts in research**

Many treat them as interchangeable… they’re not.

This infographic breaks it down clearly 👇

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🔗 **Correlation = Relationship (No direction)**
It answers: *Are two variables linked?*

✔ Measures **strength + direction** (r)
✔ **Symmetrical** → X with Y = Y with X
✔ No prediction, no causation

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📈 **Regression = Prediction (Directional model)**
It answers: *Can X predict Y?*

✔ Builds an equation:
Y = b₀ + b₁X

✔ **Directional** → X predicts Y
✔ Quantifies **how much Y changes when X changes**

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🏥 **Simple example:**

💰 Hospital expenses = fixed costs + (cost per patient × number of patients)

👉 **Intercept (b₀):** Running costs even with zero patients
👉 **Slope (b₁):** Added cost per patient

📊 If R² = 79% → most variation in cost is explained by patient numbers

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⚠️ **The BIG trap: Correlation ≠ Causation**

🍦 A real-life analogy:

A man noticed:
• When he ordered **vanilla ice cream** → his car didn’t work ❌
• When he ordered **strawberry** → his car worked ✔️

At first glance:
👉 Ice cream flavor seems “linked” to car failure!

But the real reason was:

⏱️ **Preparation time**
Vanilla was served faster → he returned to the car sooner → engine still overheated
Strawberry took longer → engine had time to cool → car worked fine

💡 Hidden factor = **waiting time**, not the flavor

👉 So yes, there is **correlation**
❌ But no **causation**

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📌 **What to report in research:**

✔ Correlation → r, p-value
✔ Regression → slope (B), CI, R²
✔ Always include descriptive statistics

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💡 Bottom line:
Correlation tells you **“they move together”**
Regression tells you **“how one predicts the other”**
Neither proves causation on its own.

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If you want to *actually understand statistics and apply it in clinical research*…

👉 Message us here to join **Statistics for Clinicians (S4C):**
https://wa.me/201119678899?text=I%20want%20to%20join%20the%20Statistics%20for%20Clinicians%20course

Bias can silently undermine your research ⚠️

This infographic highlights the **main sources of bias in observational studies**:
🔹 Selection Bias (who gets included)
🔹 Information Bias (how data is collected)
🔹 Confounding & Interaction (hidden variables)
🔹 Detection & Time Biases (when and how outcomes are measured)

💡 It also shows practical strategies to **prevent and adjust for bias** during study design, data collection, and analysis.

Because strong research is not just about results — it is about how reliable those results are.

📩 Want to confidently identify and handle bias in your studies? join our upcoming course:

👉 https://wa.me/201119678899?text=I%20want%20to%20join%20the%20Observational%20Studies%20course

Observational study designs made simple 👇

This infographic breaks down the core analytical designs:
🔹 Case-Control → looking from present to past
🔹 Cohort → following from present to future
🔹 Cross-Sectional → a snapshot at one point in time
🔹 Hybrid designs (Nested case-control & Case-crossover)

💡 Each design serves a different purpose — choosing the right one is critical for generating valid evidence.

If you work with real-world data or are planning a research project, understanding these designs is essential.

📩 Want to learn how to choose and apply the right design with confidence? click on the following link

👉 https://wa.me/201119678899?text=I%20want%20to%20join%20the%20Observational%20Studies%20course

📊 Comparing 3 or more groups? This is where most people go wrong

Many still run multiple t-tests ❌
That’s a fast track to **false positives (Type I error inflation)

👉 The correct approach: **One-Way ANOVA**

This infographic simplifies it—but here’s the key idea using a simple analogy 👇

🏠 The “House Analogy” (ANOVA made intuitive)

Think of your data as a house:

🏠 = Total variability (everything happening in your data)
🚪 Rooms = Groups (e.g., different treatments)

Now inside the house:

👥 Inside each room → Within-group variability
(people differ even if they receive the same treatment)

🚶‍♂️ Space between rooms → Between-group variability
(differences caused by the treatment itself)

🔍 What ANOVA does:
It compares between-group variation vs within-group noise

👉 If rooms are very different from each other (big between-group)
👉 And people inside each room are similar (low within-group)

💥 Then you get a **significant result**

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📌 Example: Different patient groups (cirrhosis with HCC, without HCC, normal) showed clear differences in fasting blood sugar → confirmed by a strong F-ratio and very small p-value.

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⚠️ **But don’t stop at “p < 0.05”**

A proper ANOVA report MUST include:
✔ Means & SDs for each group
✔ F-statistic + degrees of freedom + p-value
✔ Effect size (η² or ω²)
✔ Post-hoc tests (to know *which* groups differ)

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💡 Bottom line:
ANOVA tells you **“there is a difference”**
Post-hoc tests tell you **“where the difference is”**

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If you want to confidently apply this in your research and *actually understand what you're doing*…

👉 Message us here to join **Statistics for Clinicians (S4C):**
https://wa.me/201119678899?text=I%20want%20to%20join%20the%20Statistics%20for%20Clinicians%20course

📊 **Struggling to choose the right statistical test? Start here.**

The **Unpaired t-test** is one of the most essential tools in clinical research—but many people use it without fully understanding *when*, *why*, and *how*.

This infographic simplifies it for you:

🔹 **When to use it?**
To compare the means between two independent groups (e.g., Drug A vs Drug B)

🔹 **Key idea:**
You’re comparing **signal (mean difference)** to **noise (standard error)** → giving you the *t-value*

🔹 **Critical insight:**
Small samples = more uncertainty → higher critical values → heavier tails (that’s why we use the t-distribution, not Z)

🔹 **What most people miss:**
✔ Checking variance (equal vs unequal) changes the calculation
✔ Reporting is not just p-value — you need CI + effect size (Cohen’s d)

🔹 **Real-world takeaway:**
Statistical significance is not enough—interpret magnitude and clinical relevance.

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💡 If you want to **master statistics in a practical, clinician-friendly way**, join our upcoming **Statistics for Clinicians (S4C)** course. click on the following link and click send:
https://wa.me/201119678899?text=I%20want%20to%20join%20the%20Statistics%20for%20Clinicians%20course

🔬📊 THE CLINICIAN'S GUIDE TO CHI-SQUARE TESTING 📊🔬
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Ever wondered how researchers test whether occupation influences disease — or whether a drug truly changes outcomes? The answer lies in one powerful tool: the Chi-Square Test (χ²) ✅

🧩 WHAT DOES IT DO?
✔️ Goodness of Fit → Does your observed data match a theoretical model?
✔️ Test of Independence → Are two qualitative variables associated with each other?

🏥 REAL CLINICAL EXAMPLES:
👨‍⚕️ Are doctors more hypertensive than nurses?
💊 Does the type of anticoagulant affect thromboembolic complications?
👶 Is the gender ratio of newborns consistent with the expected 1:1 ratio?

🧮 THE FORMULA:
χ² = Σ [(O − E)² / E]
→ Sum the standardized squared differences between Observed (O) and Expected (E) frequencies

📏 KEY RULES TO REMEMBER:
📌 Reject H₀ when p < 0.05
📌 Always report Effect Size (Phi φ or Cramér's V) — not just p-value!
📌 Apply Cochran's Rule: 80% of cells must have expected count > 5
📌 If conditions are violated → use Yates' Correction or Fisher's Exact Test

🔍 WHEN YOU HAVE >2 GROUPS:
Don't stop at the overall chi-square!
➡️ Perform Post Hoc 2×2 comparisons
➡️ Apply Bonferroni correction to avoid inflated p-values

💡 PRO TIP:
A significant p-value tells you THAT a difference exists.
Effect Size (like Odds Ratio or Relative Risk) tells you HOW BIG that difference is.
Both matter! 🎯

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📊 Measure of Uncertainty — What does the p-value really tell us?

Let’s simplify it with a practical example 👇

Imagine 200 patients randomized into two groups:
🔹 Treatment A → 90% success
🔹 Treatment B → 80% success

That’s a 10% difference. Sounds convincing, right?

When we analyze this difference, we get a p-value = 0.047 (4.7%)

💡 So what does this actually mean?

The p-value is:
👉 The probability of observing this 10% difference if the two treatments were actually equal

Since 4.7% is less than the accepted 5% threshold:
✔️ We reject the idea that both treatments are equal
✔️ We conclude that Treatment A is likely superior

—but here’s the critical part many people miss 👇

⚠️ The p-value is NOT telling you how big or important the difference is**
👉 The 10% difference* shows the size (effect)
👉 The p-value* shows how uncertain we are about that conclusion

🧠 Think of it this way:

* Smaller p-value → more confidence in your conclusion
* Larger p-value → more uncertainty
* But NEVER a measure of effect size or clinical importance

🎥 Watch the full video “Measure of Uncertainty” to grasp this concept clearly and avoid one of the most common mistakes in research interpretation.
https://youtu.be/zMpYHcKUIJY?si=XrNf0_qylrwIrrME

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🎯 What’s the real meaning of “statistically significant”?

In biological research, variability isn’t a flaw—it’s the rule. Even when patients receive the *same* treatment, their responses can vary widely: some improve dramatically, others moderately, and some may not improve at all.

So here’s the reality:
We can *never* be 100% certain in our conclusions.

👉 That’s why researchers made a “deal”:

✔️ Every study must calculate the probability of error (using statistical tests)
✔️ The scientific community agrees to take results seriously only if this error is ≤ 5%

This 5% threshold is what we call alpha (α) — the acceptable risk of being wrong.

And the value we calculate in each study?
That’s the p-value.

💡 So what does “statistically significant” actually mean?
It simply means:
➡️ The p-value should respect alpha (0.05)

📌 If p ≤ 0.05 → Statistically significant
📌 If p > 0.05 → Not statistically significant

No magic. No mystery. Just a practical agreement to manage uncertainty in science.

🎥 Watch the full video: https://www.youtube.com/watch?v=QTzrL7P1Uxo to understand this concept in a simple, intuitive way.

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