Single case designs (SCDs) are short time series that assess intervention effects by measuring units repeatedly over time both in the presence and absence of treatment. For a variety of reasons, interest in the statistical analysis and meta-analysis of these designs has been growing in recent years. This paper proposes modeling SCD data with Generalized Additive Models (GAMs), a semi-parametric method from which it is possible to estimate the functional form of trend directly from the data, arguably capturing the true functional form better than ordinary least squares regression methods in which the researcher must decide which functional form to impose on the data. Generalized Additive Models provide a flexible way to model SCD data, allowing the data to inform the researcher both as to whether significant trend or trend treatment interaction exists, as well as which of those terms need nonlinear representations and which can remain linear. Tables and figures are appended.