# Small sample in categorical explanatory variable vs overall sample size

In a statistical model e.g. regression, we have to ensure the sample size is sufficient to estimate a given number of parameters. Rules of thumb e.g. n=10 per parameter, or a power analysis, will give us the total sample size needed to fit the model. However, I was wondering how these sample size estimates apply to categorical variables which are usually coded as dummy variables, with a parameter estimated for 1 less than the number of groups (factor levels). Supposing we have a sample size which, on the average, has 30 data points for each parameter to be estimated (overall ratio = 30:1). However a categorical variable has one factor level with a very "small" number of occurances e.g. 4, giving a ratio = 4:1. This is much less than the overall ratio and even less than the rule of thumb of 10:1. I was wondering how to best proceed in such circumstances? Would it matter so much that the sample size / number parameters ratio is very small in one factor level if overall the ratio is sufficient?