The article discusses the statistical analysis of controlled trials that measure continuous variables at baseline and follow-up. It highlights the importance of baseline measurements in trials for chronic conditions, such as pain, anxiety, and hypertension. The authors compare different methods of statistical comparison, including follow-up scores and change scores, and explain how the choice of method can affect the interpretation of results. They emphasize that the method of analysis should be specified in the trial protocol and should not be chosen based on the statistical significance of the findings. The article also introduces the use of Analysis of Covariance (ANCOVA) as a more robust method to adjust for baseline imbalances and regression to the mean. ANCOVA is described as a regression method that relates the outcome score to the baseline score and the treatment group, providing a more accurate estimate of the treatment effect. The authors illustrate this with an example from a study on acupuncture for shoulder pain, where ANCOVA provided a more reliable estimate of the treatment effect compared to other methods. Additionally, the article discusses the advantages of ANCOVA, including its greater statistical power and ability to handle baseline imbalances. It concludes by emphasizing that ANCOVA is the preferred general approach for analyzing continuous data in controlled trials.The article discusses the statistical analysis of controlled trials that measure continuous variables at baseline and follow-up. It highlights the importance of baseline measurements in trials for chronic conditions, such as pain, anxiety, and hypertension. The authors compare different methods of statistical comparison, including follow-up scores and change scores, and explain how the choice of method can affect the interpretation of results. They emphasize that the method of analysis should be specified in the trial protocol and should not be chosen based on the statistical significance of the findings. The article also introduces the use of Analysis of Covariance (ANCOVA) as a more robust method to adjust for baseline imbalances and regression to the mean. ANCOVA is described as a regression method that relates the outcome score to the baseline score and the treatment group, providing a more accurate estimate of the treatment effect. The authors illustrate this with an example from a study on acupuncture for shoulder pain, where ANCOVA provided a more reliable estimate of the treatment effect compared to other methods. Additionally, the article discusses the advantages of ANCOVA, including its greater statistical power and ability to handle baseline imbalances. It concludes by emphasizing that ANCOVA is the preferred general approach for analyzing continuous data in controlled trials.