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Re: st: Subgroup analysis

From   David Bai <>
Subject   Re: st: Subgroup analysis
Date   Thu, 08 Jul 2010 10:23:44 -0400

Thank you, Clnde. I have looked at the coefficients in the white and African American subgroups, and found that they are not very close. So there is no evidence of a lack of statistical power, based on what you've suggested. Regarding the second suggestion, I have good reasons to believe that some interactions could be significant based on the subgroup analysis.

Maybe I didn't make it clear in my last inquiry, so let me clarify here: My concern is not about whether the interaction effect is significant, but how to interpret the UNEXPECTED differential predictors' effects on the outcome across race/ethnicity if the interaction effect is significant. Assuming my analysis is correct, and found differential effects across race/ethnicity, but there is NO good theory to explain this difference, how can we explain it from statistical perspective? Lack of statistical power, with 28 predictors for a sample size of 600? Or African Americans are very homogeneous in the distributions of these predictors, and therefore it is hard for the analysis to distinguish any variations in the effects and therefore find non-significance in the results? or other interpretations?

Looking forward to more wisdom/insight to be shared with me. Thank you,


-----Original Message-----
From: Clyde Schechter <>
Sent: Thu, Jul 8, 2010 9:33 am
Subject: Re: Re: st: Subgroup analysis

Comparing the statistical significance of effects in two sub-populations
is rather perilous.

I have two suggestions.  First, since you have already done the
race-specific analyses, just look at the coefficients in the White and
African American subgroups, disregarding standard errors and p-values.
Are the coefficients similar?  If so, you may well be simply finding a
lack of statistical power to detect in a subgroup of 600 subtle effects
that achieve statistical significance in your larger combined sample.

Second, and more formally, before even running the subgroup analyses, I
would have added race X predictor interaction terms to the model and then
tested the significance of those interaction terms.  If _they_ are not
significant, then the conclusion would be that your data do not provide
evidence of difference across races (which is not the same as evidence of no difference across races). If the interaction terms _are_ significant, then the coefficients of those interaction terms give you estimates of the
cross-race differences in effects.

Hope this helps.

Clyde Schechter, MA MD
Associate Professor of Family & Social Medicine

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