Lies, damned lies and statistics: Clinical importance versus statistical significance in research.
Correctly performed and interpreted statistics play a crucial role for both those who 'produce' clinical research, and for those who 'consume' this research. Unfortunately, however, there are many misunderstandings and misinterpretations of statistics by both groups. In particular, there is a widespread lack of appreciation for the severe limitations with p values. This is a particular problem with small sample sizes and low event rates - common features of many published clinical trials. These issues have resulted in increasing numbers of false positive clinical trials (false 'discoveries'), and the well-publicised inability to replicate many of the findings. While chance clearly plays a role in these errors, many more are due to either poorly performed or badly misinterpreted statistics. Consequently, it is essential that whenever p values appear, these need be accompanied by both 95% confidence limits and effect sizes. These will enable readers to immediately assess the plausible range of results, and whether or not the effect is clinically meaningful.
Emeritus Professor of Medicine, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia. Electronic address: craig.mellis@sydney.edu.au.
Craig
C
Mellis
D035843:Biomedical Research / Q000379:methods; D000068598:Data Accuracy; D003625:Data Collection; D003627:Data Interpretation, Statistical; D006801:Humans; D018401:Sample Size
95% Confidence Intervals (95% CI); Bayes theory; Clinical relevance; Effect size (Absolute vs Ratios); P values; Statistical significance