Hi MozzTo explain in simple terms, the Rat study is simply a...

Hi Mozz

To explain in simple terms, the Rat study is simply a series of hypothesis tests. Two hypotheses that were being tested were:

1. Does iPPS improve diastolic function
2. Does iPPS reduce the amount of versican fragments (which is indicative of ADAMTS-4/5 inhibition

Before P-values can be reported, the statistical power of the study needs to be determined. Usually this is done before studies begin, but for pre-clinical studies they can also be calcuated afterwards. For clinical trials the statistical power is always determined before the trial starts recruiting patients, with the power of the study used to determine the number of patients that need to be recruited to achieve a certain significance level or alpha. In the rat study, the power estimates suggested only a p-value of p < 0.1 was needed to achieve statistical significance.

So what exactly is statistical significance?

When you test a hypothesis you do so with the intention of either accepting or rejecting the null hypothesis. The p-value e.g. p< 0.1 determines whether the result could have been due to chance alone and not iPPS. If significance is reached then you simply reject the null hypothesis. If it is not reached, then you accept the null hypothesis i.e. that the difference was due to chance. So this result simply means PAR are justified to look into HFpEF further and should look to perform an additional animal study.

For those who are still struggling to understand p values, just think of it as being part of a lengthy process with multiple hurdles where the end goal is trying to prove how a treatment works and for whom it works. Now there is never any instance where a treatment is proven to work with 100% certainty, but every successful study and trial is just another hurdle that is overcome which justifies moving on to the next hurdle. A p-value of p <0.05 is generally accepted, but ultimately it depends on the power of the study.