Deep Dive: DDO vs. PICO (Aligned with DEPTh + Design Triad)

🧠 Think "DEPTh" First—Not Framework

Before choosing DDO or PICO, classify the clinical challenge:

Type	Description	Examples
Diagnosis	What feature helps us identify X?	What predicts scrub typhus?
Etiognosis	What causes or increases risk?	Does PM2.5 cause preterm birth?
Prognosis	What predicts outcome?	What predicts ICU mortality in sepsis?
Therapeutic	Does intervention improve outcome?	Does metronomic chemo increase OS?
Methodologic	How should we best measure/study?	Is this tool valid/reliable?

🚦 Match framework to challenge type:

• Use DDO for diagnosis, etiognosis, prognosis, and most therapeutic cohort studies.

• Use PICO for randomized therapeutic trials.
  

---

🧩 Framework Dissection: DDO vs PICO

Feature	DDO	PICO
Full Name	Domain–Determinant–Outcome	Patient–Intervention–Comparator–Outcome
Best for	Observational, causal, prognostic, diagnostic	Randomized therapeutic interventions
Structure	Who → What X → What Y	Who → Treatment A vs B → What Y
Analysis Mapping	Easy to plug into `Y = f(X | confounders)`	Direct for superiority/inferiority analysis
Flexibility	High: allows causal/predictive logic	Narrow: fits RCT-specific logic

🔍 Secret Insight: PICO assumes a "treatment effect" structure. If you’re not randomizing, PICO can mislead your method logic.

---

🧪 Advanced Examples with CECS Mapping

🌱 Causal Inference (Etiognosis)

"Does high air pollution exposure increase preterm birth risk?"

• DEPTh: Etiognosis

• DDO:

  • Domain: Pregnant women in polluted cities

  • Determinant: PM2.5 exposure

  • Outcome: Preterm delivery

• Occurrence Equation:

  Preterm = f(PM2.5 | Age, BMI, Comorbidities)

• Design:

  • Method: Retrospective cohort

  • Analysis: Multivariable logistic regression
    

🧬 Diagnostic Accuracy

"What symptoms help distinguish leptospirosis from other febrile illnesses?"

• DEPTh: Diagnosis

• DDO:

  • Domain: Suspected febrile illness cases

  • Determinants: Mud exposure, conjunctival suffusion, WBC count

  • Outcome: Lab-confirmed leptospirosis

• Analysis: ROC curve, AUC, LR+, LR−
  

💉 Therapeutic RCT (PICO justified)

"Does metronomic chemo extend survival in unfit AML patients vs best supportive care?"

• DEPTh: Therapeutic

• PICO:

  • P: Unfit AML patients

  • I: Metronomic chemo

  • C: Best supportive care

  • O: Median survival at 6, 12 months

• Design: Pragmatic RCT

• Analysis: Kaplan–Meier curves, Cox model
  

---

🧠 Why Bad Questions Derail Good Research

🛑 Common Errors:

• PICO logic used for prognostic studies → misfit

• DDO variables unclear → domain/determinant conflated

• Outcome not operationalized → “better outcome” ≠ measurable endpoint

🔍 Secret Insight: Ask yourself, “Can I sketch an occurrence equation?” If not, the framework isn't ready.

---

✅ Key Takeaways

• DDO is your Swiss Army knife for most study types, especially observational, prognostic, and diagnostic.

• PICO is laser-focused: use when testing an intervention in RCT logic.

• DEPTh helps you align clinical logic to research logic.

• Research design flows from clinical challenge → structured question → objectives → design → analysis.

• A solid occurrence equation helps align all decisions downstream.