Causal vs Predictive Analysis: How DEPTh Typing Shapes Clinical Research Design

The analysis design stage forces a fundamental decision:

🧠 Are we explaining a cause, or predicting an event?

This logic determines how we build occurrence equations—and whether our statistical model needs confounder adjustment or predictive calibration.

🧠 Two Core Logics in Analysis Design

DEPTh Category	Occurrence Equation Template	Logic Type	Research Intent
Etiognostic	Y = f(X \| confounders)	Causal	Explain: “Is X a cause of Y?”
Therapeutic	Y = f(Treatment \| confounders)	Causal	Explain: “Does treatment lead to outcome?”
Diagnostic	Y = f(Signs, Symptoms, History, Labs)	Predictive	Predict disease status
Prognostic	Y = f(Biomarkers, Vitals, Clinical Data)	Predictive	Forecast future events

⚖️ Causal Occurrence Relations

Common in etiognostic and therapeutic studies. The goal is to test whether and how strongly X affects Y—while controlling for confounding and ensuring proper temporal order.

Examples:

Etiognostic“Does heavy caffeine intake increase risk of first-trimester miscarriage?”

Miscarriage = f (Caffeine intake ∣ Maternal age, Prior pregnancy loss)

👉 เพื่อหาว่าการบริโภคคาเฟอีนมาก เป็น สาเหตุ ของการแท้งบุตรหรือไม่

Therapeutic“Does early mobilization reduce length of hospital stay in elderly hip fracture patients?”

Length of Stay = f (Early mobilization ∣ Surgical delay, Baseline function)

👉 เพื่อพิสูจน์ว่า intervention (early mobilization) มีผลต่อ outcome (length of stay)

🔮 Predictive Occurrence Relations

Found in diagnostic and prognostic research. Focused not on why, but on how well we can anticipate an outcome based on measurable variables.

Examples:

Diagnostic: “Which symptoms and signs best predict acute appendicitis in children?”

Appendicitis = f (RLQ pain, Rebound tenderness, WBC count)

👉 เพื่อประเมินว่า ข้อมูลใดช่วยในการวินิจฉัย โรคได้

Prognostic“Which clinical variables predict ICU admission in COVID-19 patients?”

ICU Admission = f (Oxygen saturation ↓, CRP ↑, Age ↑)

👉 เพื่อหาว่าตัวแปรใด พยากรณ์ การเข้า ICU ได้ดีที่สุด

🧩 Case Revisit: Benzodiazepine–Delirium

Let’s plug this back into a classic clinical puzzle:

DEPTh Type: Etiognostic
Logic: Causal
Equation:

Delirium = f (Benzodiazepine use ∣ Age, Cognitive baseline, ICU exposure)

👉 จุดประสงค์: ต้องการทราบว่า ยาเบนโซ เป็น สาเหตุให้เกิด ภาวะเพ้อหรือไม่ (ไม่ใช่แค่คาดเดา)

🧠 Key Takeaways

DEPTh typing clarifies whether your model should explain causality or predict outcomes.
Causal designs (Etiognostic, Therapeutic) must address confounding and temporality.
Predictive designs (Diagnostic, Prognostic) prioritize discrimination and calibration.
Let your research question guide your equation logic, not the other way around.

Causal vs Predictive Analysis: How DEPTh Typing Shapes Clinical Research Design

🧠 Two Core Logics in Analysis Design

⚖️ Causal Occurrence Relations

Examples:

🔮 Predictive Occurrence Relations

Examples:

🧩 Case Revisit: Benzodiazepine–Delirium

🧠 Key Takeaways

Comments