Correlation and causation for cardiothoracic surgeons: part 4-distinguishing relationships in data.

Abstract

Correlation indicates a relationship between variables without causation, while causation implies one variable directly influences the other in clinical research. Through various statistical approaches, including Pearson and Spearman correlation coefficients, we can explore the strength of linear and non-linear relationships. Phi coefficient and the point-biserial correlation are other alternative techniques. Scatter plots are used to illustrate correlations in real-world data, guiding surgeons in understanding how variables like experience impact complication rates. Emphasis is placed on recognizing confounding variables, applying appropriate statistical methods, and interpreting results accurately to inform clinical decisions. This paper highlights the importance of evidence-based, data-driven practices in enhancing surgical outcomes.