What are the implications of p-values and confidence intervals in the context of patient management decisions?

Implications of p-values and confidence intervals in patient management decisions revolve around their roles in interpreting the reliability and clinical relevance of study findings to inform shared decision-making.

P-values indicate the probability that observed effects are due to chance, but relying solely on statistical significance (p-values) can be misleading for clinical decisions because they do not convey the magnitude or clinical importance of an effect ¹.

Confidence intervals (CIs), by contrast, provide a range within which the true effect size is likely to lie, offering insight into the precision and potential clinical impact of an intervention, which is crucial for evaluating benefit versus harm in individual patients ¹.

Integrating CIs with measures such as effect size and minimal clinically important difference (MCID) enhances interpretation by highlighting whether observed changes are meaningful to patients, beyond mere statistical significance (Fleischmann and Vaughan, 2019).

This approach aligns with NICE guidance promoting shared decision-making, where clinicians and patients consider the balance of risks and benefits informed by robust evidence, including the uncertainty expressed by CIs, rather than relying on p-values alone ¹,².

Therefore, in patient management, p-values should not be the sole determinant; instead, confidence intervals combined with clinical context and patient preferences guide safer, more effective, and personalised treatment decisions ¹,² (Shakespeare et al., 2001; Fleischmann and Vaughan, 2019).