Yet Another Excellent Explainer About P-Values in Randomized Trials
Apr 4, 2024
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Professor Erik Van Zwet from Leiden University discusses P-values in randomized trials, trial power, and trial replication. They explore the significance of P-values in interpreting study outcomes, the challenges of sample size, and understanding effect size. The conversation clarifies the unexpectedness of results and emphasizes the importance of historical data for comprehensive analysis in clinical settings.
Interpreting p-values below 5% signifies statistical significance in clinical trials, crucial for understanding trial results.
Underpowered trials increase the risk of missing true positive results or obtaining false positives, impacting the interpretation of trial outcomes.
Deep dives
Understanding P-Values and Statistical Interpretation in Clinical Trials
P-values play a crucial role in clinical trials, indicating the significance of trial results. In the podcast episode, Dr. Eric Benswett elucidates that a key aspect is whether a p-value is below or above 5%, with values below signifying significance. He compares interpreting p-values to assessing growth charts for children, with extreme deviations highlighting concern. Dr. Benswett’s analogy aids in grasping the importance of statistical significance in medical research.
Power Levels and Trial Interpretation
Dr. Benswett delves into the power levels of clinical trials, revealing that a substantial number of trials are underpowered, contrary to the desired 80% power level. This suggests a higher likelihood of missing true positive results or obtaining false positives due to chance. Understanding the power levels of trials is crucial for accurate interpretation of trial outcomes and the potential impact of statistical significance.
Overestimation in P-values and Trial Results
The episode highlights a common issue of overestimation in p-values, particularly in trials with marginal statistical significance. Dr. Benswett notes that trials with p-values hovering near the significance threshold of 0.05 may have overestimated effects due to low power. This caution calls for a more critical assessment of trials that barely achieve statistical significance to avoid misinterpretation and reliance on potentially inflated results.
Implications for Clinical Practice and Trial Evaluation
Dr. Benswett's insights underscore the importance of meta-analyses and cautious interpretation of trial results, especially in the context of underpowered trials. Encouraging the publication of all trials, even small ones, and subsequent meta-analyses can provide a comprehensive view of treatment effects. By recognizing the limitations of underpowered trials and the potential for overestimation, clinicians can make informed decisions based on a more nuanced understanding of trial outcomes.
Gosh was I lucky to speak with Professor Erik Van Zwet from Leiden University in the Netherlands. He is the first author on a recent NEJM Evidence paper looking at more than 23,000 trials in the Cochrane Database. (I linked to an URL that should get by the paywall.)
There are technical aspects of this paper. We hit on some (not a lot) of them. The gist of it though is really important when we look at evidence. Erik did an excellent job of explaining P-values, trial power, and, at the end, we discuss how this work might inform the ability of trials to replicate.
This discussion also pairs well with one I had with computer scientist Ben Recht.
I hope you enjoy the conversation.
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