1. Effect Size: The Foundation of Clinical Interpretation

Effect size (ES) is the magnitude of difference or association between groups, exposures, treatments, or predictors. It is the central component of all DEPTh areas (diagnosis, etiology, prognosis, therapeutic, methodologic).

“Always interpret effect size + 95% CI, not p-values alone.”

Common Effect Size Metrics by Research Type

Effect size is therefore not just a number—it is the quantitative backbone of clinical research.

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2. Why Effect Size Alone Is Not Enough: The Role of Confidence Intervals

CECS guidance requires 95% CIs with every effect size.

CI answers:

• How precise is the effect?
• Does it cross a “clinically meaningful” threshold?

Precision (CI width) determines whether your sample is adequate.

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3. MCID & CID: Translating Effect Size Into Clinical Meaning

Effect size shows “how big.”MCID/CID show whether the effect actually matters.

MCID – Minimal Clinically Important Difference

• Smallest difference patients consider meaningful
• Preferred method: Anchor-based (CECS causal metrics guide)

CID – Clinically Important Difference

• Difference clinicians/guidelines consider meaningful
• Often used in policy or guideline decisions

Role in Interpretation

• If effect size < MCID → clinically trivial
• If CI crosses MCID → uncertain clinical benefit
• If effect size > MCID → meaningful improvement

Matching effect size with MCID is essential to determine real-world impact, not just statistical significance.

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4. Why MCID/CID Must Drive Sample Size

CECS design logic instructs:

“Use clinically meaningful target differences (e.g., MCID) for powering studies.”

This prevents:

• Underpowered studies that miss meaningful effects
• Overpowered studies that detect trivial ones
• Trials that are statistically positive but clinically hollow

Key Relationship

Thus, MCID = Target Effect Size in power calculation.

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5. Standardized Effect Size (for continuous outcomes)

When outcomes vary in scale:

• Cohen’s d (effect / SD)
• Hedges g

Used when the variability affects detectability of MCID.

If MCID = 1 and SD = 2: d = 1/2 = 0.5 → moderate effect → guides sample-size estimation.

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6. Effect Size + MCID + CI → Determines Trial Success

A high-quality study meets all three conditions:

1. Estimated effect size exceeds MCID/CID
2. CI does not cross MCID
3. Sample size is adequate to ensure precision

This is the CECS standard for clinical interpretability and methodological validity.

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7. Putting It Together (Continuous Outcome Example)

Inputs

• MCID = 1
• SD = 2
• Target effect size (Cohen’s d) = 1/2 = 0.5
• Power = 80%, α = 0.05

Interpretation

• d = 0.5 means the effect is clinically meaningful
• This d becomes the input for sample size calculation

Hence, without MCID, sample size is clinically blind. Without effect size, MCID cannot be mapped.Without CI, we cannot judge precision.

All three are inseparable.

The BRAVE Rule of Thumb for Sample Size Estimation

• B: Beta (Type II error): This is related to the statistical power of the study (Power = 1 - Beta). Conventionally, power is set at 80–90% to avoid false negatives (Type II errors),.
• R: Ratio: This refers to the allocation ratio of sample sizes between the comparison groups (e.g., n2/n1),.
• A: Alpha (Type I error): This is the pre-set critical value of significance, typically set at 0.05 or 0.01. A lower alpha reduces the chance of false positive results.
• V: Variability: This represents the variation or sampling error (e.g., standard deviation) within the data. Higher variation generally requires a larger sample size.
• E: Effect size: This is the magnitude of the clinically significant difference the researcher aims to detect. A larger effect size typically requires a smaller sample size to detect,.

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8. Final Summary Table