Genetic markers anticipate response to citalopram in a majority of patients.

OBJECTIVE: Scientists have concluded that genetic profiles cannot predict a large percentage of variation in response to citalopram, a common antidepressant. Using the same data, we examined if a different conclusion can be arrived at when the results are personalized to fit specific patients. METHODS: We used data available through the Sequenced Treatment Alternatives to Relieve Depression database. We created three boosted Classification and Regression Trees to identify 16 subgroups of patients, among whom anticipation of positive or negative response to citalopram was significantly different from 0.5 (P≤0.1). RESULTS: In a 10-fold cross-validation, this ensemble of trees made no predictions in 33% of cases. In the remaining 67% of cases, it accurately classified response to citalopram in 78% of cases. CONCLUSION: For the majority of the patients, genetic markers can be used to guide selection of citalopram. The rules identified in this study can help personalize prescription of antidepressants.

Improved Statistical Methods are Needed to Advance Personalized Medicine.

Common methods of statistical analysis, e.g. Analysis of Variance and Discriminant Analysis, are not necessarily optimal in selecting therapy for an individual patient. These methods rely on group differences to identify markers for disease or successful interventions and ignore sub-group differences when the number of sub-groups is large. In these circumstances, they provide the same advice to an individual as the average patient. Personalized medicine needs new statistical methods that allow treatment efficacy to be tailored to a specific patient, based on a large number of patient characteristics. One such approach is the sequential k-nearest neighbor analysis (patients-like-me algorithm). In this approach, the k most similar patients are examined sequentially until a statistically significant conclusion about the efficacy of treatment for the patient-at-hand can be arrived at. For some patients, the algorithm stops before the entire set of data is examined and provides beneficial advice that may contradict recommendations made to the average patient. Many problems remain in creating statistical tools that can help individual patients but this is an important area in which progress in statistical thinking is helpful.