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I was wondering what the difference between including a covariate in a regression model and doing a separate subgroup analysis based on that covariate? (I'm a stats noob, please forgive me). For example, I am planning a regression model that will include age (child or adult) as a covariate, but I also wanted to do a separate analysis looking at outcomes in children and then in adults. Just wondering if doing the subgroup analysis would be redundant?

Thanks for any help in advance.

Roaring
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  • You can include that age category as a variable (typically called a factor when it is categorical), and by having the regression go something like $y = \beta_0 + \beta_1 x + \beta_2 \text{age} + \beta_3 x*\text{age}$, you save yourself some parameter estimation and increase your precision. In other words, you can accomplish everything by doing one regression equation. – Dave Apr 21 '21 at 15:10
  • Okay, so I'm just wondering then, why do people do sub group analyses then if you can just include the covariates in one regression equation? – Roaring Apr 21 '21 at 15:17

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As Dave writes, you can often just use one larger regression model, using interaction terms if necessary. This will usually come with better precision on your parameter estimates.

However, note that this presupposes homoskedasticity. If your error variance differs between children and adults, you would need to account for this in your larger model. Or, well, run subgroup analyses.

And of course, separate models are far easier to understand than interactions. This is indeed often valuable.

Finally, discretizing a numerical covariate (age) to form groups is often bad practice. Better to use age as-is, possibly with a spline transformation to account for any nonlinearity. See here.

Stephan Kolassa
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