Use and Misuse of Statistical Significance in Survival Analyses

In most preclinical disease models, survival analyses are the gold standard for measuring the efficacy of medical interventions such as therapeutics or vaccines. In these analyses, treatment regimens that promote the survival and/or reduce the morbidity of experimental subjects are tested for efficacy. Although these analyses appear to be relatively straightforward, there are associated caveats regarding interpretation of the results that we wish to discuss in this editorial. Of particular concern is overinterpretation of the biological significance of survival data based on statistical significance rather than durability of protection.

The intent of this article is to serve as a reminder that statistical and biological significance should never be used interchangeably in survival studies that attempt to predict protective efficacy. In our opinion, for acute infections that cause death in 7 to 10 days, a 3-day difference in survival is the minimum value that would warrant further development. On the other hand, a 3-day difference in survival would be biologically irrelevant for chronic infections ... in which desired differences would be weeks, months, or even years. 

We suggest that the biological outcome from the experiment be considered first and then statistics applied to determine if the results are likely to be due to chance. In this process, it should be remembered that a cutoff P value of 0.05 is relative; a P value of 0.1 indicates that a particular result would occur by chance 10% of the time. This could still reflect a biologically important effect.

Furuya, Y., Wijesundara, D. K., Neeman, T., & Metzger, D. W. (2014). Use and Misuse of Statistical Significance in Survival Analyses. mBio, 5(2), e00904-14.

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