Statistical Analysis

Statistical Analysis

Rigorous statistical analysis guidance for interpreting and reporting research findings in cardiology content.

Triggers

• User needs to interpret trial statistics
• User is reporting study results
• User asks about statistical significance vs clinical significance
• User needs help with effect sizes, confidence intervals, or p-values
• User is evaluating the strength of evidence

Core Concepts

Test Selection Decision Tree

Comparing Two Groups:

• Continuous outcome, normal: Independent t-test
• Continuous outcome, non-normal: Mann-Whitney U
• Categorical outcome: Chi-square or Fisher's exact

Comparing 3+ Groups:

• Continuous, normal: ANOVA with post-hoc
• Continuous, non-normal: Kruskal-Wallis
• Categorical: Chi-square

Relationships:

• Two continuous: Pearson (normal) or Spearman (non-normal)
• Predict continuous: Linear regression
• Predict binary: Logistic regression
• Time-to-event: Cox proportional hazards

Effect Sizes (Always Report!)

Confidence Intervals

Critical: Always report 95% CIs alongside point estimates.

• CI crossing 1.0 (for ratios) = not statistically significant
• CI width indicates precision
• Narrow CI = more precise estimate
• Wide CI = less precise, often underpowered

P-values: What They Are and Aren't

P-value IS: Probability of observing data this extreme if null hypothesis true

P-value IS NOT:

• Probability hypothesis is true/false
• Measure of effect size
• Indicator of clinical importance

Reporting: p < 0.05 is arbitrary; report exact values (p = 0.03, not p < 0.05)

Clinical Trial Statistics

Key Metrics for Cardiology Trials

Example Interpretation

"DAPA-HF showed empagliflozin reduced the composite endpoint (HR 0.74, 95% CI 0.65-0.85, p<0.001). The ARR was 4.9%, yielding an NNT of 21 over 18 months."

This tells us:

• 26% relative risk reduction (1 - 0.74)
• Statistically significant (CI doesn't cross 1.0)
• Need to treat 21 patients to prevent one event
• Clinically meaningful benefit

Common Errors to Avoid

P-hacking Red Flags

• Multiple testing without correction
• Selective outcome reporting
• Subgroup fishing
• Stopping trials early for "significance"

Interpretation Errors

• Confusing statistical and clinical significance
• Ignoring confidence interval width
• Treating absence of evidence as evidence of absence
• Comparing p-values across studies

Reporting Errors

• Reporting only p-values without effect sizes
• Omitting confidence intervals
• Not specifying statistical tests used
• Rounding inappropriately (keep 2 decimal places for ratios)

APA-Style Statistical Reporting

# t-test
t(48) = 2.31, p = .025, d = 0.67, 95% CI [0.12, 1.22]

# ANOVA
F(2, 87) = 4.56, p = .013, η² = .095

# Correlation
r(58) = .42, p = .001, 95% CI [.18, .62]

# Chi-square
χ²(2, N = 120) = 8.45, p = .015, φ = .27

# Regression
β = 0.34, SE = 0.08, t = 4.25, p < .001

# Hazard ratio
HR = 0.74, 95% CI [0.65, 0.85], p < .001

Power Analysis Guidance

Before interpreting underpowered studies:

• Sample size adequate for expected effect?
• Was power analysis pre-specified?
• What effect size was study powered to detect?

A non-significant result in an underpowered study ≠ no effect

Checklist for Statistical Reporting

• Effect size with confidence interval
• Exact p-value (not just < or > threshold)
• Statistical test specified
• Assumptions verified
• Multiple comparison correction if needed
• Clinical significance discussed
• Limitations of analysis noted