Heterogeneity in patient response in depression: The relevance of functional data analysis

M any misconceptions exist about the behaviour of ’typical’ patients in clinical trials in depression. Physicians, for example, are taught that it takes 2-4 weeks for the effect of antidepressants to manifest, which has been shown to be untrue. On the other hand, average curves show a time course which is also not representative of many patients, since considerable heterogeneity exists. In this work the focus is on the analysis of the heterogeneity between patients, rather than on the mean behaviour, using methodology from functional data analysis. Data from five double-blind, randomised, placebo-controlled, clinical studies were used in which the HAMD was measured as efficacy endpoint. All analyses were performed in the language and environment for statistical computing, R. The package pcaMethods, which includes various methods to deal with censored data, was used to carry out the principal component analysis. The results of the functional data analysis showed that most variation (∼65%) was present in a vertical shift of the curve, as was also evident from previous analyses using a linear mixed model. The main principal components of the HAM-D17 were constant over studies and also the same for responders and non-responders. The main principal components were also identified in the HAM-D7 subscale. Our analysis enables identification of individual response patterns over time. It also shows that the principal components explaining heterogeneity are constant across clinical studies and in responders versus non-responders, although the mean curves do differ between these subpopulations. This finding indicates that responders and non-responders do not constitute two different populations. It is also shown that individual differences in response can be characterised by the use of a subscale, even though it contains only 7 items, as compared to the full HAM-D17. This strengthens the relevance of subscales in clinical research.