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Re: st: Re: power analysis for panel data

From   Phil Schumm <[email protected]>
To   [email protected]
Subject   Re: st: Re: power analysis for panel data
Date   Fri, 10 Nov 2006 00:43:47 -0600

On Nov 7, 2006, at 12:57 PM, Christopher W. Ryan wrote:
The lack of any statistically significant beneficial effect of any of the interventions on BMI does not surprise me, given the generally intractable nature of obesity. The general futility of simple office-based exhortations to lose weight is part of my point.

But what is the power of this study? I don't know how to calculate that. Am I failing to see statistically significant beneficial effects on BMI because of inadequate power?

You're right to be concerned about power here (of course there are other issues too such as the apparent non-random assignment to treatment, but I'll assume you have already thought carefully about those). However, power per se is a frequentist concept that really only applies prior to collecting the data (or at least to analyzing them). Given that you have already done the analysis, and assuming you do not plan to collect any more data, I'd suggest recasting the issue in terms of precision. In other words, ask yourself: Do the confidence intervals include what would be considered substantively meaningful effects? If so, then one could argue that the study was underpowered. If however the confidence intervals are narrow enough to rule out substantively meaningful effects, then you can claim that your data actually provide evidence against the existence of such effects.

-- Phil

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