Clinical Research Variables and the Occurrence Equation

🔍 Why Start with Variables?

Every clinical research question—whether it’s about diagnosis, treatment, or prognosis—can be boiled down to a relationship between variables. Your job as a clinical investigator is to define those variables clearly and to make sure your study design respects the logic behind how one (or more) exposures (Xs) affect a specific outcome (Y).

The simplest way to conceptualize this? Think like a statistician with a stethoscope.

🧱 Meet Your Variable Trio

1. Study Determinants (X) = ตัวแปรต้น

These are your independent variables.
Can be an exposure (smoking), a treatment (drug), a biological factor (CRP level), or even a behavior.
Must be chosen based on your object design (e.g., Are you trying to diagnose, explain, predict, or treat?)

2. Study Outcomes (Y) = ตัวแปรตาม, ตัวแปรผลลัพธ์

These are dependent variables—what you’re trying to influence, predict, or explain.
Can be binary (dead/alive), ordinal (severity scale), or continuous (blood pressure, length of stay).

3. Other Variables

Covariates = variables related to Y that you adjust for.
Confounders = variables related to both X and Y that can bias the X→Y relationship.
Effect Modifiers = variables that alter the strength/direction of the X→Y effect (e.g., sex may change how a drug works).

🧪 Clinical Endpoint Parameters: The Numbers that Matter

After defining X and Y, you need a statistical effect measure:

Odds Ratio (OR) for case-control and logistic regression.
Risk Ratio (RR) for cohort studies.
Hazard Ratio (HR) for time-to-event/survival analysis.
Mean Difference for continuous outcomes.

These parameters directly link your hypothesis to interpretable clinical outcomes.

🔄 Enter the Occurrence Equation

The occurrence equation is your study’s DNA.

Y = f(X₁, X₂, ..., Xₙ | Confounders, Bias, Random Error)

It formalizes the belief that your outcome (Y) is a function of one or more exposures (Xs), conditional on bias control. It maps directly to the regression models you use.

For example:

logit(Delirium) = β₀ + β₁ × Benzo + β₂ × Age + β₃ × ICU

This equation becomes the blueprint for your analysis strategy.

🛠️ From Theory to Method: Design Flow Recap

Step	Description
Identify Clinical Challenge	Use DEPTh: diagnosis, etiognosis, prognosis, therapy, method
Translate into Question	e.g., “Does X cause Y?”
Define Study Domain & Variables	Who, what, and how are measured?
Select Endpoint Parameter	OR, RR, HR, Mean Difference
Build Occurrence Equation	Model X→Y with confounders in mind

🧩 Example: Etiognostic Study Using the Occurrence Equation

Let’s apply this to a concrete case.

🎯 Clinical Challenge (Etiognostic)

Event: Postoperative delirium
Suspected Cause: Benzodiazepine use
Setting: Elderly surgical inpatients

🧱 Object Design

Element	Value
DEPTh Type	Etiognostic
Clinical Question	Does preoperative benzodiazepine use cause increased risk of delirium after surgery?
Y (Outcome)	Post-op delirium (yes/no)
X (Determinant)	Benzodiazepine use within 48 hours pre-op

🧪 Method Design

Element	Value
Study Domain	Patients aged ≥65 undergoing major surgery
Study Base	Retrospective cohort (from EMR)
Calendar Time	Retrospective (past 3 years)
Covariates	Age, sex, cognitive status, ICU stay, surgery type

🔬 Occurrence Equation

Functional Model

Delirium = f(Benzodiazepine | Age, Cognitive Status, Surgery Type, ICU)

Logistic Regression Form

log(P(Delirium) / (1 − P(Delirium))) = β₀ + β₁ · Benzo + β₂ · Age + ...

✅ Summary Table

Step	Design Choice
DEPTh	Etiognostic
Study Design	Retrospective Cohort
Analysis	Logistic Regression
X (Determinant)	Benzodiazepine use
Y (Outcome)	Delirium
Occurrence Eq	Y = f(X \| Confounders)

📌 Key Takeaways

The occurrence equation is the universal structure behind all clinical research designs.
DEPTh typing defines the function logic as causal vs predictive.
Distinguish X and Y early and precisely.
Match your analysis model to Y’s nature (binary, continuous, time-to-event).
Bake in your confounding control from the start.