p=0.24: “Modest improvements” if you want to believe it, a null finding if you don’t.

February 5, 2018
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(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

David Allison sends along this juxtaposition:

Press Release: “A large-scale effort to reduce childhood obesity in two low-income Massachusetts communities resulted in some modest improvements among schoolchildren over a relatively short period of time…”

Study: “Overall, we did not observe a significant decrease in the percent of students with obesity from baseline to post intervention in either community in comparison with controls…”

Allison continues:

In the paper, the body of the text states:

Overall, we did not observe a significant decrease in the percent of students with obesity from baseline to post intervention in either community in comparison with controls (Community 1: −0.77% change per year, 95% confidence interval [CI] = −2.06 to 0.52, P = 0.240; Community 2: −0.17% change per year, 95% CI = −1.45 to 1.11, P = 0.795).

Yet, the abstract concludes “This multisector intervention was associated with a modest reduction in obesity prevalence among seventh-graders in one community compared to controls . . .”

The publicity also seems to exaggerate the findings, stating, “A large-scale effort to reduce childhood obesity in two low-income Massachusetts communities resulted in some modest improvements among schoolchildren over a relatively short period of time, suggesting that such a comprehensive approach holds promise for the future, according to a new study from Harvard T.H. Chan School of Public Health.”

I have mixed feelings about this one.

On one hand, we shouldn’t be using “p = 0.05” as a cutoff. Just cos a 95% conf interval excludes 0, it doesn’t mean a pattern in data reproduces in the general population; and just cos an interval includes 0, it doesn’t mean that nothing’s going on. So, with that in mind, sure, there’s nothing wrong with saying that the intervention “was associated with a modest reduction,” as long as you make clear that there’s uncertainty here, and the data are also consistent with a zero effect or even a modest increase in obesity.

On the other hand, there is a problem here with degrees of freedom available for researchers and publicists. The 95\% interval was [-2.1, 0.5], and this was reported as “a modest reduction” that was “holding promise for the future.” Suppose the 95% confidence interval had gone the other way and had been [-0.5, 2.1]. Would they have reported it as “a modest increase in obesity . . . holding danger for the future”? I doubt it. Rather, I expect they would’ve reported this outcome as a null (the p-value is 0.24, after all!) and gone searching for the positive results.

So there is a problem here, not so much with the reporting of this claim in isolation but with the larger way in which a study produces a big-ass pile of numbers which can then be mined to tell whatever story you want.

P.S. Just as a side note: above, I used the awkward but careful phrase “a pattern in data reproduces in the general population” rather than the convenient but vague formulation “the effect is real.”

P.P.S. I sent this to John Carlin, who replied:

That’s interesting and an area that I’ve had a bit to do with – basically there are zillions of attempts at interventions like this and none of them seem to work, so my prior distribution would be deflating this effect even more. The other point that occurred to me is that the discussion seems to have focussed entirely on the “time-confounded” before-after effect in the intervention group rather than the randomised(??) comparison with the control group – which looks even weaker.

John wanted to emphasize, though, that he’s not looked at the paper. So his comment represents a general impression, not a specific comment on what was done in this particular research project.

The post p=0.24: “Modest improvements” if you want to believe it, a null finding if you don’t. appeared first on Statistical Modeling, Causal Inference, and Social Science.



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