Suppose that at any stage of a statistical experiment a control variable $X$ that affects the distribution of the observed data $Y$ can be used. The distribution of $Y$ depends on some unknown parameter $θ$, and we consider the classical problem of testing a simple hypothesis $H_0: θ=θ_0$ against a simple alternative $H_1: θ=θ_1$ allowing the data to be controlled by $X$, in the following sequential context. The experiment starts with assigning a value $X_1$ to the control variable and observing $Y_1$ as a response. After some analysis, we choose another value $X_2$ for the control variable, and observe $Y_2$ as a response, etc. It is supposed that the experiment eventually stops, and at that moment a final decision in favour of $H_0$ or $H_1$ is to be taken. In this article, our aim is to characterize the structure of optimal sequential procedures, based on this type of data, for testing a simple hypothesis against a simple alternative.