The overall strategy was the following (the steps are described in more detail later): Develop a parameterized finite element (FE) model of a generic ONH such that a model with the desired combination of factor levels (a configuration) could be readily produced. A set of configurations used to sample the factors space was determined with a fractional factorial design of experiments (DOE) methodology. Each configuration model was then used to simulate an increase in IOP, and the response was characterized by the magnitudes of the strains and stresses within the LC and the prelaminar neural tissue (PLNT). Standard statistical techniques associated with the DOE method were then used to determine the relative strength of the contribution of each factor and of their interactions to the variances in the responses. Finally, the most influential factors were identified and the percentage of response variance accounted for by them and their interactions was computed.
Note the following terminology: I refer to the variables as factors, with levels, as is often done in statistics. Simulation analysts instead speak of inputs, input factors, or parameters with values. A set of factor levels determines a configuration, sometimes called a design point, a run, or a scenario. Models are built according to a configuration and used to simulate an increase in IOP. From a simulation are computed the responses, also referred to as outputs or outcome measures. I refer to the strength of the effects of factors on responses as factor main effects, direct effects, or the effects of factors independently, and of factors in conjunction, as factor interactions, or just interactions. Interactions are sometimes classified by order (e.g., zeroth, first, second) or by the number of factors (or “ways”) involved (e.g., two-factor/two-way, three-factor/three-way interactions). In this work, for simplicity, interactions refers to two-factor interactions. Higher order interactions were found to have much weaker effects and are therefore not presented or discussed. Note also that the DOE technique should not be confused with the colloquial use of “design of experiments” in the literal sense.