Carroll Self-Rating Scale(CSRC), Clinical Global. Impression (CGI) measuring severity of illness, investigator assessment of subject improvement, and subjects ...
FASTCLUS PROCEDURE TO IDENTIFY SUBTYPES WITHIN A STUDY POPULATION FOLLOWING TREATMENT WITH A NEW ANTIDEPRESSANT DRUG Lev Sverdlov, Ph.D.; John F. Noble, Ph.D Innapharma, Inc., Upper Saddle River, NJ
Introduction SAS®1 is the predominant statistical tool used in the pharmaceutical industry to analyze clinical data and to prepare integrated medical and statistical reports for submission to the FDA. The majority of statistical analyses of clinical trial data in the field of clinical psychiatry have been based on two-dimensional models in which the active drug group is simply compared with the placebo group. The objective of this paper is to describe a multivariate statistical method used to analyze thr effect of treatment with a new antidepressant drug that enabled the identification of subtypes within a population. This method, based on the FASTCLUS procedure for cluster analysis, provided evidence that response to treatment was related to the cluster structure and was very much influenced by plasma drug concentrations above and below a minimum effective concentration. Study Population Fifty two subjects with major depression (26 placebo and 26 treated with a new antidepressant drug) were included in a single center study based on the protocol inclusion and exclusion criteria. There were no statistically significant differences between treatment groups in either age, sex, ethnic origin, or any baseline measure of safety. Efficacy was evaluated by treatment group comparisons of individual and mean scores on the Hamilton Depression Rating Scale (HAMD), Montgomery-Asberg Depression Scale (MADS), Carroll Self-Rating Scale(CSRC), Clinical Global Impression (CGI) measuring severity of illness, investigator assessment of subject improvement, and subjects self-assessment of improvement, Visual Analog Scale (VAS) for mood, anxiety, and mental clarity collected during treatment, posttreatment, and follow-up periods. As HAMD is used extensively in clinical research and is recognized as the standard clinical depression rating scale predictive of the efficacy of antidepressant drugs, the primary efficacy measurement in the study was the percent responders and the percent change from
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baseline to HAMD. All other efficacy parameters were used for secondary efficacy measurements. Venous blood samples for pharmacokinetic analysis were drawn, and the concentration of drug in human plasma was determined using a validated bioanalytical LC/MS method. Statistical Method The clustering method is a multivariate statistical procedure used to create homogenous groups of subjects as suggested by the data, but not defined prior to analysis. Subjects in a given cluster tend to have similar characteristics, while subjects belonging to different clusters tend to be dissimilar. Cluster analysis categorizes all subjects and divides them into groups based solely on the inherent traits of the population. This structure is usually defined by computation of a distance matrix which quantifies the distance of every member of the group to the center of each cluster using corresponding measurements [1]. Cluster analysis has a proven track record in the field of neurometrics [2] in which groups of psychiatric subjects are subtyped based on quantitative analysis of electroencephalogram and event-related potentials [3,4]. An extensive number of clustering methods have been developed, such as disjoint clusters, hierarchical clusters, overlapping clusters, and fuzzy clusters [5] to name few. In this study, subgroups were sought using the FASTCLUS SAS algorithm [5]. FASTCLUS is designed for disjoint clustering and measures the Euclidean Distance from each subject to the center of each cluster. This clustering method was selected because of its efficiency in creating a cluster structure for a large database using a minimum amount of computer resources, and it virtually guarantees no overlap between clusters. Subjects included in the sample were iteratively allocated to their respective clusters as determined by the minimum Euclidean Distance from each subject to each cluster. As it is well known that the FASTCLUS procedure is highly sensitive to the order of the observations in the dataset, subjects were sorted in ascending order by subject numbers
according to a randomization table. In addition, because of the limited sample size, all subjects2 were included in the training group (there was no replication group of subjects). The list of variables for the cluster analysis were selected from the efficacy parameters investigated in the study (ie, percent change from baseline for Day 7 and Day 14 in HAMD, MADS, CSRS, CGI). The Day 7 and Day 14 timepoints were selected because these represent the onset of antidepressant action of the drug (peak effect). In order to insure the use of independent variables for analysis, a correlation procedure was performed on all efficacy variables. Those variables that resulted in a correlation coefficient >0.9 were eliminated from the independent variables for analysis. Consequently, MADS Day 7 was excluded because of its high correlation with HAMD, Day 7. In addition, VAS was omitted because of the subjectivity and high variability of this psychometric test. CGI for Investigator’s Global Improvement and Subject Global Impression/Improvement tests were also eliminated because baseline values are not collected for these two parameters and percent changes from baseline therefore, can’t be calculated. Consequently, the optimal list of variables selected for cluster analysis included seven variables. Though the VAS was not used as a variable for determination of the cluster structure for the training group, as validation of this procedure, total VAS scores for Day 7 and Day 14 were used as replication variables. Using the seven efficacy variables, two to six clusters were modeled in order to obtain the optimal number of clusters. The criteria for selection of the clusters included a proportional contribution of subjects per cluster, and the maximum difference (ie, minimum chi-square probability between individual clusters and treatment groups) between clusters over the following treatment groups: placebo, drug group with plasma concentrations greater than the minimum effective therapeutic concentration (MEC), and less than the MEC.
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The Phase 2 study investigated 52 subjects. Three subjects were excluded from the efficacy analysis due to protocol violations.
Results and Discussion Cluster analysis using seven psychometric test variables identified four optimal clusters. Convergence criterion was satisfied following three iterations. Results of statistical tests for the null hypothesis showed a strong, statistically signficant (p
