Multilevel analyses of on-demand medication data, with an application to the treatment of Female Sexual Interest/Arousal Disorder

Data from clinical trials investigating on-demand medication often consist of an intentionally varying number of measurements per patient. These measurements are often observations of discrete events of when the medication was taken, including for example data on symptom severity. In addition to the varying number of observations between patients, the data have another important feature: they are characterized by a hierarchical structure in which the events are nested within patients. Traditionally, the observed events of patients are aggregated into means and subsequently analyzed using, for example, a repeated measures ANOVA. This procedure has drawbacks. One drawback is that these patient means have different standard errors, first, because the variance of the underlying events differs between patients and second, because the number of events per patient differs. In this paper, we argue that such data should be analyzed by applying a multilevel analysis using the individual observed events as separate nested observations. Such a multilevel approach handles this drawback and it also enables the examination of varying drug effects across patients by estimating random effects. We show how multilevel analyses can be applied to on-demand medication data from a clinical trial investigating the efficacy of a drug for women with low sexual desire. We also explore linear and quadratic time effects that can only be performed when the individual events are considered as separate observations and we discuss several important statistical topics relevant for multilevel modeling. Taken together, the use of a multilevel approach considering events as nested observations in these types of data is advocated as it is more valid and provides more information than other (traditional) methods.