Profiling Placebo Responders by Self-Consistent Partitioning of Functional Data

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Identification of placebo responders among subjects treated with active drug has significant clinical and research implications. In clinical practice, when a patient treated with medication improves, this improvement may be attributed to the chemical component of the drug itself, a "placebo effect," or some combination of these. Determining the proper subsequent treatment and maintenance of the patient may be greatly aided by understanding the mechanism of patient improvement. In a research context, classification of patient response has bearing on how efficacy and effectiveness clinical trials are designed and conducted. This article presents a framework for studying placebo response in diverse areas of medicine. To identify placebo responders among drug-treated patients, a profile of the clinical status over time (outcome profile) is estimated for each subject. Self-consistent partitioning techniques are used to group subjects based on the amount of curvature in the profile as well as the overall trend in the profile. The resulting partitions determine representative profiles for subjects in the drug group that subsequently can be used to classify patients. The proposed method is applied to data from a clinical trial for treatment of depression involving placebo and the active drug phenelzine. Data from the placebo arm of the study is used to help validate the procedure, because the drug-treated and placebo-treated subjects should share common profiles.



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