Complex Trait-Treatment-Interaction (CTTI) analysis is introduced as a new and highly efficient statistical approach for analysing individual differences in experimental designs. CTTI analysis enables the researcher to investigate the combined effects of several personality traits and several treatments on a dependent variable. Thus, the hypothesis can be tested that a specific relationship between some aspect of human behavior and some trait and/or treatment variable(s) is moderated by several other trait and/or treatment variables simultaneously. Unlike traditional approaches such as zone analysis, CTTI analysis treats trait variables as metric variables. Thus, the statistical power and, thereby, the sensitivity of the design to detect complex relationships is enhanced, requiring relatively small sample sizes. CTTI analysis consists of three main steps: (1) exploration of trait interactions within experimental groups by plotting regression surfaces; (2) designing a proper linear model with specified higher-order interactions; (3) testing the model using a standard general-linear-model algorithm. To demonstrate this, CTTI analysis was applied to data from a study on individual differences in responsiveness to alcohol and antidopaminergic treatment, in which the combined influence of two trait variables (anxiety and impulsiveness) and two treatment variables (ethanol and alpha-methyl-para-tyrosine) on CFF performance was investigated in eighty healthy male subjects. The results showed that, under specific pharmacological conditions, anxiety and impulsiveness as well as their mutual moderating effects are essential for the drug response observed. CTTI analysis proved to be a very powerful and highly sensitive statistical procedure for detecting complex higher-order interactions in this example of experimental personality research.