Greetings, fellow data aficionados! Once again, your favorite numerical narrator is spinning a yarn of statistical intrigue. This time our stage is set at none other than Harvard Medical School, where we're elbow-deep in posterior probability scores and bonding problems associated with Peripartum Depression (PPD). Strap in, because this adventure is as compelling as it gets!
PPD is a mood disorder that can occur during pregnancy or after childbirth, characterized by feelings of extreme sadness, anxiety, and exhaustion. Our project focused on a potential biological marker for bonding issues within PPD, known as the Posterior Probability (PrP) scores. This intriguing metric comprises HRR (heart rate response), SCR (skin conductance response), and F-EMG (frontalis electromyogram). As for the biology of PPD, there's an intriguing unified theory that you can read up on here: [link](https://rdcu.be/dgiol).
But alas! Our statistical expedition was not without its trials and tribulations. Picture this: a limited sample size of 16 women with PPD. And yours truly, the eager statistician, tasked with making sense of this intricate puzzle. The chosen weapon of choice? Network Analysis.
Network Analysis, for those unacquainted with the term, is a method used to examine the relationships between different variables. In our context, it's like hosting a tea party and observing how all our guests – PrP scores, bonding behaviors, psychopathology, and risk factors – interact with each other.
Our network party unveiled some fascinating dynamics. The PrP scores, our guest of honor, were central to the conversation, mingling seamlessly with bonding behaviors (like the amount of skin-to-skin contact), psychopathology (childbearing post-traumatic stress disorders), and risk factors (like unplanned c-sections). It boasted the highest betweenness (indicating it interacts with the most guests), strength (meaning it has strong relationships with other guests), and expected influence (suggesting it influences other variables significantly).
But how do we prove that PrP is the best predictor in this complex matrix? Instead of playing the conventional p-value game, I turned to the mighty Pratt's index – an effect size measure that quantifies the importance of a predictor variable in a regression model by looking at the proportion of explained variance it contributes to. After some calculations, we discovered that while PrP is an important predictor, it's not the strongest one.
Our foray into this world of small sample sizes and complex variables was a stark reminder of the need to allow the data to narrate its own story, rather than imposing our preconceived notions upon it. It also highlighted the perils of drawing firm conclusions from tiny sample sizes. Just as you wouldn’t judge a cake from a single crumb, careful we must be in our numerical adventures, reminding ourselves and others to take small samples with a grain of... well, statistical salt!
So, until our next numerical adventure, keep crunching those numbers and remember: data has the funniest way of revealing the truth, especially when you least expect it!
Yours truly,
The Statistician
Comments