We propose a method for assessing variable importance in matched case-control investigations and other highly-stratified studies characterized by high dimensional data (p >> n). In simulated and real datasets, we show that the proposed algorithm performs better than a conventional univariate method (condi- tional logistic regression) and a popular multivariable algorithm (Random Forests) that does not take the matching into account. The methods are applicable to wide ranging, high impact clinical studies including metabolomic, proteomic studies and neuroimaging analyses, such as those assessing stroke and Alzheimer’s disease. The methods proposed in this paper have been implemented in a freely available R library (http://cran.r-project.org/web/packages/RPCLR/index.html).