Effect Size, MCID/CID, and Sample Size Relevance
- Mayta
- 2 days ago
- 3 min read
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
DEPTh Type | Effect Size Metrics |
Therapeutic | Risk Ratio, Risk Difference, Mean Difference, Hazard Ratio |
Etiologic | RR, OR, HR, IRR |
Prognostic | HR, OR, Absolute Risk, AUROC |
Diagnostic | Sensitivity, Specificity, LR+, LR–, AUROC |
Effect size is therefore not just a number—it is the quantitative backbone of clinical research.
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.
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.
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
Component | Purpose |
Effect Size | What difference exists |
MCID/CID | What difference matters |
Sample Size | How many subjects needed to detect that meaningful difference with precision |
Thus, MCID = Target Effect Size in power calculation.
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.
6. Effect Size + MCID + CI → Determines Trial Success
A high-quality study meets all three conditions:
Estimated effect size exceeds MCID/CID
CI does not cross MCID
Sample size is adequate to ensure precision
This is the CECS standard for clinical interpretability and methodological validity.
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,.
8. Final Summary Table
Concept | What it Means | Why It Matters |
Effect Size | Magnitude of effect | Tells “how big” |
CI | Precision of ES | Determines certainty |
MCID | Minimum patient-important difference | Determines whether ES is clinically meaningful |
CID | Clinically/guideline-important difference | Determines relevance for practice |
Sample Size | N needed to detect MCID with required precision | Defines power; ensures valid inference |
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