Causal vs Predictive Analysis: How DEPTh Typing Shapes Clinical Research Design
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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?”
- 👉 เพื่อหาว่าการบริโภคคาเฟอีนมาก เป็น สาเหตุ ของการแท้งบุตรหรือไม่
- Therapeutic“Does early mobilization reduce length of hospital stay in elderly hip fracture patients?”
- 👉 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?”
- 👉 เพื่อประเมินว่า ข้อมูลใดช่วยในการวินิจฉัย โรคได้
- Prognostic“Which clinical variables predict ICU admission in COVID-19 patients?”
- 👉 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:
- 👉 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.
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