The Pros and Cons of Bayesian and Frequentist Statistics in Biomedical Research

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We’ve previously talked about how important it is to choose the right statistical test. But did you know, you also have a choice in the overall way you approach statistical analysis for your study data? Today, we’re going to explore the pros and cons of two fundamental statistical approaches in clinical research: Bayesian and Frequentist statistics. 

Frequentist Statistics: The Traditional Path

Picture this: You’re conducting a clinical trial to evaluate the effectiveness of a new drug to treat a rare disease. With Frequentist statistics, you’ll be following the classic, well-trodden path that most researchers have been walking on for years, using tests such as ANOVA, t-tests, etc. 

Pros of Frequentist Statistics: 

a. Objectivity: Frequentist methods are considered objective since they do not involve prior beliefs or subjective judgments. The results are purely based on the data collected during the study. 

b. P-values: Ah, the famous P-value! Frequentist statistics rely on this little number to test hypotheses. It helps you determine whether the observed effect is statistically significant or simply due to chance. 

c. Confidence Intervals: Frequentist statistics provide confidence intervals, which show the range of values within which the true population parameter is likely to lie. This helps researchers understand the precision of their estimates. 

d. Long-standing Tradition: Frequentist statistics have been the cornerstone of clinical research for a long time. Many peer reviewers, journals and regulatory agencies are familiar with these methods, making it easier to communicate your findings. 

Cons of Frequentist Statistics: 

a. Limited Use of Prior Information: Frequentist methods ignore prior information, which might be valuable in certain cases. By neglecting prior knowledge, you might miss out on important insights. 

b. P-value Misinterpretation: Relying solely on P-values can lead to misunderstandings and misinterpretations of results. Remember, statistical significance doesn’t necessarily mean the results are clinically or practically meaningful! 

Bayesian Statistics: The Path Less Traveled 

Now, let’s tread the less-beaten path of Bayesian statistics. It might seem daunting at first, but trust me, it has its own charm and advantages! 

Pros of Bayesian Statistics: 

a. Prior Knowledge: Unlike Frequentist methods, Bayesian statistics allow us to incorporate prior knowledge and beliefs about the parameters in our analysis. This can be invaluable in situations where historical data or expert opinions are available. 

b. Probability Statements: Bayesian statistics provide probability distributions for parameters, giving us a more intuitive understanding of uncertainty. Instead of just declaring “statistical significance,” we get to know the probability that a parameter falls within a certain range. 

c. Smaller Sample Sizes: Bayesian methods can be more efficient, especially when dealing with limited data. They often require smaller sample sizes to achieve comparable results to Frequentist approaches. 

d. Iterative Learning: With Bayesian statistics, we can update our beliefs as we gather more data. This iterative learning process allows us to continuously refine our understanding of the problem at hand. 

Cons of Bayesian Statistics: 

a. Subjectivity: Bayesian methods involve incorporating prior beliefs, which introduces some level of subjectivity into the analysis. This can be a double-edged sword, as it might lead to biased results if the prior is poorly specified. 

b. Complexity: Bayesian statistics can be more challenging to implement and require a good understanding of probability theory and computational methods. 

c. Limited Popularity: Because of their complexity, journals may find it difficult to source peer reviewers who are comfortable with manuscripts where Bayesian statistics are used. Further, relatively few reporting guidelines (aside from the SAMPL guidelines) cover Bayesian statistics.  

Embrace the Hybrid Approach!  

As biomedical researchers, it’s crucial to recognize that there’s no one-size-fits-all approach to statistics. Instead of choosing one side over the other, consider adopting a hybrid approach. Utilize the strengths of both Bayesian and Frequentist methods depending on your research question, available data, and prior knowledge. 

For instance, in exploratory studies, where prior information is scarce, you might lean more towards Frequentist methods. On the other hand, if you have substantial historical data or expert opinions on a treatment’s efficacy, Bayesian methods can complement your analysis. 

Remember, statistics is not about following rigid rules but embracing uncertainty and making informed decisions. Stay curious, keep learning, and don’t shy away from exploring the road less traveled! 

Get advice from expert biostatisticians on the right statistical approach and tests to run for your study. Check out Editage’s Statistical Analysis & Review Services.