Guide to Common Measures (Data type, Clinimetrics) in Clinical Research Using the DEPTh Model

🔍 Why Clinical Metrics Matter (Beyond “Significance”)

Imagine you’re comparing two treatments in a heart failure unit. Or evaluating a new diagnostic AI in diabetic retinopathy. Or modeling survival in glioblastoma.

In each case, you’ll eventually report clinical measures—but if you don’t start from a clear DEPTh challenge, you risk matching the wrong metric to the wrong question.

🩺 Key Principle: A measure only matters if it’s aligned to the clinical decision you're informing.

This is where the DEPTh model reframes your approach:

DEPTh Challenge	Core Clinical Question
Diagnosis	Is test X accurate for detecting disease Y?
Etiognosis	Is exposure X a cause or risk factor for outcome Y?
Prognosis	What’s the likely outcome for a patient with Z?
Therapeutic	Does treatment X improve patient outcome Y?
Methodologic	How should we best measure/model/analyze?

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🧪 Clinimetrics = Metrics with a Mission

Clinimetrics combines clinical logic with measurement theory. You aren’t just calculating numbers—you’re quantifying questions.

To build metrics that matter, first structure your study using the Design Triad:

1. Object Design: DEPTh challenge

2. Method Design: Domain, base, determinant (X), endpoint (Y), calendar time

3. Analysis Design: Causal (explanatory) vs Predictive (risk modeling)
   

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🧠 Start With the DEPTh Type

Let’s now walk through each DEPTh category, replacing old examples with fresh, original, and clinically realistic cases.

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💊 Therapeutic Research: Comparing Interventions

🎯 Research Question

Does continuous positive airway pressure (CPAP) reduce morning systolic blood pressure more than high-flow nasal cannula (HFNC) in patients with OSA?

🔬 Study Design

• RCT with binary X: CPAP (1), HFNC (0)

• Continuous Y: Morning SBP (mmHg)
  

🧮 Metrics

Outcome Type	Preferred Metric	Interpretation
Continuous	Mean difference / Mean ratio	Average BP reduction
Binary	Risk ratio, Odds ratio, NNT	Proportion with controlled BP
Time-to-event	Hazard ratio, Rate ratio	Time to normalization

Example result: Mean SBP after CPAP: 124.2 mmHg Mean SBP after HFNC: 133.4 mmHg Mean Difference = −9.2 mmHg 95% CI: (−13.1, −5.3) → Clinically meaningful reduction

🔍 Secret Insight: Use MCID to interpret whether this 9.2 mmHg drop crosses the “minimal important” threshold for decision-making.

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⚠️ Etiognostic Research: Identifying Risk Factors

🎯 Research Question

Does high sodium intake increase the risk of frequent nocturia in older adults?

🔬 Study Design

• Prospective cohort

• Binary X: High sodium diet (1), normal sodium (0)

• Binary Y: Nocturia ≥2 times/night (1), otherwise (0)
  

🧮 Metrics

Metric	Use Case
Risk Ratio	Incidence in exposed vs unexposed
Odds Ratio	Use in case-control or rare outcomes
Hazard Ratio	Use if timing to event matters

Example result:

• High sodium group risk = 30%

• Normal sodium group risk = 12% Risk Ratio = 2.5 Risk Difference = 18%

🛠 Use DAGs to rule out confounding by other dietary or renal variables before interpreting causality.

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⏳ Prognostic Research: Predicting Outcomes

🎯 Research Question

Among stroke survivors aged >70, what predicts functional independence at 6 months?

🔬 Study Design

• Prospective cohort of post-stroke patients

• X = Age, NIHSS score, rehab intensity (continuous/categorical)

• Y = Modified Rankin Scale (mRS ≤2 = good outcome)
  

🧮 Metrics

Metric	Purpose
Odds Ratio	Risk prediction modeling (logistic regression)
AUROC	Discrimination of the model
Calibration Plot	Agreement between predicted and actual rates
Kaplan-Meier	For time-to-functional-recovery modeling

Example result:

• AUROC = 0.82 (very good discrimination)

• NIHSS ≥10 associated with OR = 0.3 for good outcome

🔍 Secret Insight: In prognostic modeling, calibration is just as important as discrimination—models must be right on average and accurate across risk ranges.

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🧪 Diagnostic Research: Measuring Test Accuracy

🎯 Research Question

Can a new retinal AI scan accurately detect early-stage diabetic retinopathy?

🔬 Study Design

• Cross-sectional study

• Binary X = AI prediction: DR detected (1), not detected (0)

• Binary Y = Ophthalmologist-confirmed DR (gold standard)
  

🧮 Metrics

Metric	Interpretation
Sensitivity	Ability to detect actual DR
Specificity	Ability to exclude non-DR
LR+ / LR–	Change in odds after test result
AUROC	Global accuracy
PPV / NPV	Population-specific predictive values

Example result:

• Sensitivity = 88%

• Specificity = 91%

• AUROC = 0.94

⚠️ Predictive values vary by prevalence. Always report PPV/NPV with context.

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🔗 Aligning Measures to Study Logic

Use this master table to pair your X-Y type with the right metric:

X Type	Y Type	Metric Options
Binary	Binary	RR, OR, RD, NNT
Binary	Continuous	Mean difference, ratio
Binary	Time-to-event	Hazard ratio, rate ratio
Continuous	Binary	Regression coefficient, OR
Continuous	Time-to-event	Cox proportional hazard model

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✅ Key Takeaways

• Always start from the DEPTh classification—it defines everything.

• Use the Design Triad to structure your study: object, method, analysis.

• Match your metrics to both data type and research goal (causal vs predictive).

• Don’t interpret significance without effect size + 95% CI.

• Use MCID and clinical impact framing to go beyond p-values.