Conducting a clinical trial? 3 Kinds of statistical analysis you need to generate high-quality evidence

Statistical analysis plays a crucial role in determining whether new treatments or therapies are effective and safe. However, conducting a proper statistical analysis requires expertise and a thorough understanding of the underlying principles. In this blog post, we will discuss three kinds of statistical analysis that play a critical role in ensuring a robust and reliable clinical trial: power analysis, testing of assumptions, and post-hoc comparisons. 

Power analysis

Power analysis is an essential statistical tool that allows you to determine the sample size needed for your study. It helps you to calculate the statistical power of your trial: the ability of your study to identify effects of substantial interest. A well-designed clinical trial with an appropriate sample size ensures that the study has a high chance of detecting a clinically meaningful difference or relationship among the variables you are studying. In other words, power analysis helps you to avoid an underpowered study that cannot produce meaningful results or an overpowered study that wastes time and resources. In long-term clinical trials, interim power analysis can prevent premature stopping or unnecessary prolongation of the study.  

Testing of assumptions

Before conducting any statistical analysis, you need to test the assumptions underlying your study design. Testing assumptions is essential to ensure that your statistical analysis is valid and reliable. There are several assumptions that you need to test, such as normality, homogeneity of variance, and independence, and failure to test for these is a common error in biomedical research.i Testing assumptions allows you to identify any potential violations and adjust your analysis accordingly. Failure to test assumptions properly can lead to erroneous results and conclusions. 

Post-hoc comparisons

Post-hoc comparisons refer to the analysis of data after the completion of a clinical trial. Its main purpose is to identify exactly which groups differ from each other, especially if you are running an ANOVA or any other similar analysis. Post-hoc comparisons can help you to identify subgroups that may respond differently to your intervention and can inform the development of personalized treatment plans. However, Griffith and Feynman (2021) noted that excessive post-hoc comparisons can increase the risk of false positives, especially in fields like surgery where sample sizes are often smallii, and therefore, such tests should be conducted and their results interpreted with caution. 

In conclusion, conducting a proper statistical analysis is crucial in ensuring the validity and reliability of your clinical trial results. Power analysis, testing of assumptions, and post-hoc comparisons are useful for conducting a robust and meaningful analysis. By applying these tools, you can design and execute clinical trials that yield high-quality and clinically meaningful evidence. 

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