An Upbeat Mood May Boost Your Paper’s Publicity

April 17, 2018
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(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Gur Huberman points to this news article, An Upbeat Mood May Boost Your Flu Shot’s Effectiveness, which states:

A new study suggests that older people who are in a good mood when they get the shot have a better immune response.

British researchers followed 138 people ages 65 to 85 who got the 2014-15 vaccine. Using well-validated tests in the weeks before and after their shots, the scientists recorded mood, stress, negative thoughts, sleep patterns, diet and other measures of psychological and physical health. . . .

Greater levels of positive mood were associated with higher blood levels of antibodies to H1N1, a potentially dangerous flu strain, at both four and 16 weeks post-vaccination. No other factors measured were associated with improved immune response.

Abundant researcher degrees of freedom? Check.

Speculative hypothesis? Check.

Obvious latent-variable explanation? Check.

Difference between significant and non-significant taken as significant? Check.

The article continues:

The authors acknowledge they were not able to control for all possible variables, and that their observational study does not prove cause and effect.

The senior author, Kavita Vedhara, professor of health psychology at the University of Nottingham, said that many things could affect vaccine effectiveness, but most are not under a person’s control — age, coexisting illness or vaccine history, for example.

“It’s not there aren’t other influences,” she said, “but it looks like how you’re feeling on the day you’re vaccinated may be among the more important.”

First off, the confident statement at the end seems to contradict the caveats two paragraphs earlier. Second, I question the implication that one’s mood is “under a person’s control.” How does that work, exactly?

Beyond all this are the usual statistical problems of noise. From the research article:

One hundred and thirty-eight community-dwelling older adults aged 65–85 were recruited through 4 primary care practices in Nottingham, UK. A priori sample size calculations based on observed effects of stress on vaccine response in elderly caregivers (Vedhara et al., 1999) indicated a sample of 121 would give 80% power at 5% significance to detect a similar small-to-medium sized effect (r = 0.25) in individual regression models.

This the familiar “power = .06” disaster: take an overestimated effect size from a previous noisy study, then design a new study under these unrealistic assumptions. Bad news all around.

On the plus side, this is a study that would be easy enough to do a preregistered replication. I recommend the authors of the above-cited study start thinking up their alibis right now for the anticipated replication failure.

P.S. As usual, let me repeat that, yes, this effect could be real and replicable. And I’ll believe it once I see real evidence. Not before.

P.P.S. I learned about this paper on 25 Sep, right around when everyone’s getting their flu shots. But I posted it on a delay so it’s not appearing until mid-April.

Why delay my post on this timely topic?

Here’s why. If I keep quiet, this research might make people happy, which in turn will boost their flu shots’ effectiveness. But if I post, I’d be duty-bound to criticize this research as just another bit of noise-mining. This would make people sad, which in turn would decrease their flu shots’ effectiveness. Thus, by posting right away, I could be making people unhealthy, even maybe killing them! So, ethically speaking I have no choice but to delay my post until April, when flu season is over–and also, coincidentally, the next spot on the blog queue.

The post An Upbeat Mood May Boost Your Paper’s Publicity appeared first on Statistical Modeling, Causal Inference, and Social Science.



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