Random Effects Assessed as Dependent Adverse Effects
In this chapter examples are given, where a dependent adverse effect is random. In such studies, for example, a random “treatment by study subset” effect may be a dependent adverse effect of the treatment modalities on the outcome.
In clinical trials it is common to assume a fixed effects research model. This means that the patients selected for a specific treatment are homogeneous, and have the same true quantitative effect, and that the differences observed are residual, meaning that they are caused by inherent variability in biological processes, rather than some hidden subgroup property. If, however, we have reasons to believe that certain patients due to co-morbidity, co-medication, age or other factors will respond differently from others, then the spread in the data is caused not only by the residual effect but also by between patient differences due to some unexpected property.
Random effects models are a very interesting class of models, but even a partial understanding of it is fairly difficult to achieve. In this chapter we will demonstrate that it is helpful for the purpose to assess those random factors in the form of adverse effects of the dependent type. Random factor analysis implies that the treatment effect is not tested against the residual effect but rather against a random effect.