How do you interpret a 95% confidence interval (CI) in clinical practice?

Understanding and Interpreting 95% Confidence Intervals

A 95% confidence interval represents a range of values within which we can be 95% confident that the true population parameter lies, based on the sample data collected. This statistical tool provides crucial information about both the precision and uncertainty of research findings.

What a 95% Confidence Interval Tells Us

A 95% confidence interval provides several key pieces of information:

• Range of plausible values: It shows the upper and lower boundaries within which the true population value is likely to exist
• Precision of estimate: The width of the interval indicates how precise the estimate is—narrower intervals suggest greater precision
• Statistical significance: If a 95% CI for a difference between groups does not include zero, the result is statistically significant (p<0.05)
• Clinical importance: The entire range of values within the CI should be considered when evaluating clinical relevance

Correct Interpretation

The technically correct interpretation of a 95% confidence interval is:

• If we were to repeat the same study many times and calculate a 95% CI for each sample, about 95% of these intervals would contain the true population parameter 1

This means that for any single confidence interval we calculate, we can be 95% confident that it contains the true population value 2.

Common Misinterpretations to Avoid

Several common misinterpretations should be avoided:

• NOT the range where 95% of individual values in the population fall
• NOT that there is a 95% probability that the true value lies within the interval (this is the Bayesian interpretation) 3, 4
• NOT simply a tool to determine statistical significance by checking if it includes the null value

Factors Affecting Confidence Interval Width

The width of a confidence interval is influenced by:

1. Sample size: Larger samples produce narrower (more precise) intervals 5
2. Variability in the data: Greater variability leads to wider intervals 6
3. Confidence level: Higher confidence levels (e.g., 99% vs. 95%) produce wider intervals 6

Clinical Application of Confidence Intervals

When applying confidence intervals in clinical practice:

• Evaluate the entire range: Consider whether all values within the interval would lead to the same clinical decision
• Consider precision: Wide intervals indicate greater uncertainty about the true effect
• Look beyond statistical significance: A statistically significant result may not be clinically meaningful if the effect size is small 6
• Compare to minimal clinically important difference: Determine if the entire CI exceeds the threshold for clinical importance

Advantages Over p-values

Confidence intervals offer several advantages over p-values alone:

• Provide information about the magnitude of effect (effect size)
• Show the precision of the estimate
• Allow assessment of clinical significance, not just statistical significance 2, 4
• Help interpret negative results by showing whether they truly rule out important effects

Example Interpretation

For example, if a study reports that a treatment reduces systolic blood pressure by 8 mmHg (95% CI: 5 to 11 mmHg):

• We can be 95% confident that the true effect lies between 5 and 11 mmHg
• The result is statistically significant (CI doesn't include zero)
• The precision is moderate (6 mmHg width)
• All values in the range may be clinically meaningful, suggesting a reliable treatment effect

By understanding and correctly interpreting confidence intervals, clinicians can make more informed decisions about applying research findings to patient care, focusing on both statistical significance and clinical importance.