Lev Sverdlov, Ph.D.; John F. Noble, Ph.D.; Gabriela Nicolau, Ph.D. Innapharma, Inc., Upper Saddle River, NJ

Transcription

1 THE RESULTS OF CLUSTER ANALYSIS OF CLINICAL DATA USING THE FASTCLUS PROCEDURE Lev Sverdlov, Ph.D.; John F. Noble, Ph.D.; Gabriela Nicolau, Ph.D. Innapharma, Inc., Upper Saddle River, NJ ABSTRACT The objective of this presentation is to describe an example of disjoint clustering for clinical data as part of statistical evaluation for drug development in the CNS area. The method, based on the FASTCLUS procedure, provides evidence that cluster structure reflects differences in response to drug treatment according to achieved plasma therapeutic concentration. The main focuses are on the analysis of SAS output and the technical issues regarding selection of the independent variables for cluster analysis and of the optimal number of clusters. INTRODUCTION SAS 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. In addition, SAS has a very powerful approach in using multivariate statistics for better interpretation of the results of clinical trials. For example, in SAS/STAT [1] there are a number of procedures to separate a heterogeneous study population into relatively homogenous subgroups. FASTCLUS procedure is one of them. It virtually guarantees no overlap between subgroups after separation and is very effective in terms of computer resources. SAS is a registered trademark of the SAS Institute Inc., Cary, North Carolina, USA. Statistical evaluation with FASTCLUS procedure for drug development with a new antidepressant was previously presented [2]. Serotonin uptake in platelets as a biochemical marker of treatment effect was used for FASTCLUS procedure in replication manner [3]. The most important technical aspects (list of variables, optimal number of clusters, interpretation of results) with the FASTCLUS procedure are evaluated in this presentation. STUDY POPULATION Fifty-two subjects with major depression (26 placebo and 26 treated with a new antidepressant drug [4]) were included in a single center study (Phase 2) based on the protocol inclusion and exclusion criteria. Forty-nine subjects (24 placebo and 25 treated with drug) were evaluable for statistical analysis of efficacy. There were no statistically significant demographic differences between treatment groups (age, sex, ethnic origin, weight). Efficacy was evaluated by treatment group comparisons of individual and mean scores on the Hamilton Depression Rating Scale - 21 items (HAMD), Montgomery-Asberg Depression Rating Scale (MADR), Carroll Self-Rating Scale (CSRS), Clinical Global Impression measuring severity of illness (CGIS), Visual Analog Scale (VAS) for mood, anxiety, and mental clarity collected during treatment, post-treatment, and N:\WP\PROP\PPIN9453\621286\PAPERS\1PAPER.DOC sc 6/18/99 9:32 AM Page: 1

2 follow-up periods. As HAMD is used extensively in clinical research and is recognized as a standard clinical depression rating scale predictive of the efficacy of antidepressant drugs, the primary efficacy measurement used in this study was the percent change from baseline. Venous blood samples for pharmacokinetic analysis were drawn, and the concentration of drug in human plasma was determined by liquid chromatography/mass spectrometry (LC/MS). LIST OF VARIABLES The clinical data were converted from BBN/Clintrial to SAS and all subjects were sorted in ascending order by subject numbers assigned by randomization. Because of the limited sample size, all subjects were included in a training group and there was no replication group of subjects. The list of variables for the cluster analysis was selected from the efficacy parameters investigated in the study (i.e., percent change from baseline for Day 7 and Day 14 in HAMD, MADR, CSRS, CGIS). The Day 7 and Day 14 timepoints were selected because Day 7 represents the end of treatment measured after 2 days following the end of the 5-day dosing and Day 14 represents the peak effect of antidepressant action of the drug as measured by all variables. All four psychometric assessments for Day 14 were included in the list of variables for cluster analysis in order to use the physiological background, to explain the effect of separation. For Day 7, only those psychometric assessments that resulted in a correlation matrix (PRINCOMP procedure [1]) with a value less than.9 were included in the list of variables for cluster analysis. Consequently, MADR 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. Consequently, the optimal list of variables selected for cluster analysis included seven variables. NUMBER OF CLUSTERS Using the seven efficacy variables, three 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 (i.e., minimum chi-square probability between individual clusters and treatment groups) between clusters over the following treatment groups: placebo, drug group with plasma drug concentrations at 1 hour after dosing within >5 ng/ml and below <5 ng/ml. The concentration of 5 ng/ml is the minimum projected therapeutic concentration (MPTC). Table 1 presents the statistical evaluation for the different number of clusters. Three is the minimum number of clusters because there were three treatment groups. Six clusters is the statistical limit, according to the sample size. No. of s Chi-Square P-Value of Separation TABLE 1 Minimum No. of Subjects Per Maximum No. of Subjects Per BBN/Clintrial is a registered trademark of Bolt Beranek and Newman Inc. N:\WP\PROP\PPIN9453\621286\PAPERS\1PAPER.DOC sc 6/18/99 9:32 AM Page: 2

3 According to Table 1, only three or four clusters can be considered a reasonable solution because for five and six clusters, one cluster represents only the effect of outliers. The four cluster solution was selected as optimal because it has a better statistical effect of separation (minimum P-value for chi-square test). RESULTS FOR OPTIMAL CLUSTER SOLUTION The input dataset (Table 2) contains subject number, type of treatment, plasma group (above or below the MPTC) and psychometric assessments for the variables used for cluster analysis. The SAS macro for cluster analysis with the FASTCLUS procedure is presented in Figure 1. SAS Output 1 is the result of the FASTCLUS procedure. SAS Output 2 is a cluster definition and distance for all subjects. Table 3 is a summary of optimal cluster solution selected (4 clusters). No. Mean % Change for 7 Variables TABLE 3 % Subjects from Treatment Groups Above Below Placebo MPTC MPTC % 1.% 2.% 25.% 2 5.% 2.% 2.% 25.% % 7.% 13.3% 25.% 4 7.3%.% 46.7% 25.% The analysis of cluster structure shows that the mean percent change from baseline for the seven clinical assessments was at least 5% for s 2 and 3. These clusters were classified as clusters with responders to treatment. The magnitude of response of these two clusters, however, was dissimilar. 2 had a mean percent change from baseline in all seven clinical assessments of 5.%. 3, on the other hand, showed a larger response to treatment with a mean percent change from baseline of 77.8% in all seven clinical assessments. The sensitivity of CGIS for all clusters was a little less than the sensitivity of all other efficacy parameters. The majority of subjects (7 out of 1, or 7%) with drug plasma concentrations above the MPTC were located in 3, while only 2% (2 out of 1) of subjects with drug plasma concentrations within the MPTC were located in 2. s 1 and 4 were classified as clusters with non-responders to treatment. Mean percent change from baseline for the seven variables for s 1 and 4 were only 31.9% and 7.3%, respectively. The majority of subjects in these clusters (46.7% in 4 and 2% in 1) had drug plasma concentration below the MPTC. Only 1 out of 1, or 1% of subjects in 1, and no subjects in 4 had drug plasma concentrations within the MPTC. Placebo subjects contributed equally (25%) to all four clusters. Figure 2 presents the mean percent change versus baseline by timepoints for the treatment and follow-up periods by clusters. There were proportional improvements in all efficacy parameters in each of the four clusters from Days 3 to 7, indicating a cumulative effect of treatment. Responders (s 2 and 3) demonstrated a significant mean percent change from baseline at each timepoint, compared to non-responders (s 1 and 4). However, 3 demonstrated the greatest percent change from baseline in psychometric test scores from Day 3 to Day 7. The difference between clusters also increased proportionally on each treatment day, with the maximum difference between clusters occurring on N:\WP\PROP\PPIN9453\621286\PAPERS\1PAPER.DOC sc 6/18/99 9:32 AM Page: 3

4 Day 7. All changes in each cluster obtained by Day 7 were observed throughout the entire follow-up period (four weeks). The distribution of males and females between clusters was similar, and there were no differences between clusters in race. Demographic features of age were not significantly related to cluster membership. The population with the highest mean age was found in s 2 and 3. Similarly, baseline HAMD scores were comparatively higher for s 2 and 3 than for s 1 and 4. Improved therapeutic effect appears to be correlated with more advanced age, and higher baseline HAMD scores. SUMMARY SAS is a powerful and flexible tool for multivariate statistical analysis of clinical data. The FASTCLUS procedure effectively separated a heterogeneous study population of patients diagnosed with major depression into relatively homogeneous subgroups. The division of the study population into four clusters, two of which were classified as clusters with responders to treatment and two that were considered as clusters with non-responders to treatment, as well as the equal distribution of placebo patients in all four clusters, provides substantial evidence of the value of cluster analysis with FASTCLUS procedure. BIBLIOGRAPHY 1. SAS/STAT, User s Guide, Version 6, Fourth Edition, 53-11, Lev Sverdlov and John F. Noble. FASTCLUS Procedure to Identify Subtypes Within a Study Population Following Treatment With a New Antidepressant Drug. NESUG 98. Pittsburgh, Pennsylvania, Lev Sverdlov, John F. Noble and Gabriela Nicolau. Analysis (FASTCLUS Procedure) to Replicate Platelet Serotonin Uptake Rates as a Biochemical Marker of Treatment Effect for a New Antidepressant Drug. PharmaSUG 99. New Orleans, Louisiana, J. P. Feighner, R. H. Ehrensing, A. J. Kastin, J. F. Noble, L. Sverdlov, H. Abajian, G. Nicolau. A doubleblind, placebo-controlled efficacy safety, and pharmacokinetic study of INN 835, a novel antidepressant peptide in the treatment of major depression. J. Affect. Dis. (submitted). For Contact: Lev Sverdlov, Ph.D. Innapharma, Inc. 1 Mountainview Road, Suite 31 Upper Saddle River, NJ 7458 Phone: , Ext. 646 Fax: LSVERDLOV@AOL.COM N:\WP\PROP\PPIN9453\621286\PAPERS\1PAPER.DOC sc 6/18/99 9:32 AM Page: 4

5 TABLE 2. INPUT DATASET SUBJID TREAT GRP PHAMD_7 PHAMD_W 1 PMADS_W 1 PCSRS_7 PCSRS_W1 PCGIS_7 PCGIS_W1 1 Placebo 2 Placebo 3 INN835 4 INN835 6 Placebo 7 Placebo 8 INN835 9 Placebo 1 INN Placebo 12 Placebo 13 Placebo 14 INN INN INN Placebo 18 Placebo 19 INN835 2 INN Placebo 22 Placebo 23 INN Placebo 25 INN Placebo 27 Placebo 28 INN INN INN Placebo 33 INN INN Placebo 36 INN INN INN835 4 Placebo 41 INN Placebo 43 INN INN Placebo 46 Placebo 47 INN Placebo 49 Placebo 5 INN INN Placebo placebo placebo con< con< placebo placebo con< placebo con>= placebo placebo placebo con< con< con< placebo placebo con< con< placebo placebo con>= placebo con< placebo placebo con>= con>= con< placebo con>= con>= placebo con< con< con< placebo con< placebo con>= con< placebo placebo con>= placebo placebo con>= con>= placebo N:\WP\PROP\PPIN9453\621286\PAPERS\1PAPER.DOC sc 6/18/99 9:32 AM Page: 5

6 /* MACRO for Analysis, Training Group */ %macro clus_tr(fl_tr,max_cl); title 'Training Group'; proc fastclus data=&fl_tr maxc=&max_cl out=clus_tr maxiter=2; var phamd_7 phamd_w1 pmads_w1 pcsrs_7 pcsrs_w1 pcgis_7 pcgis_w1; proc sort data=clus_tr; by cluster; proc print data=clus_tr; var subjid subjinit grp cluster distance; proc freq data=clus_tr; table cluster * grp / chisq exact; proc means data=clus_tr; by cluster; var phamd_7 phamd_w1 pmads_w1 pcsrs_7 pcsrs_w1 pcgis_7 pcgis_w1; output out=replic mean=phamd_7 phamd_w1 pmads_w1 pcsrs_7 pcsrs_w1 pcgis_7 pcgis_w1; /* MACRO for Analysis, Replication Group */ %macro clus_rp(fl_rp,max_cl); title 'Replication Group'; proc fastclus data=&fl_rp seed=replic replace=none maxiter= maxc=&max_cl out=clus_rp; var phamd_7 phamd_w1 pmads_w1 pcsrs_7 pcsrs_w1 pcgis_7 pcgis_w1; proc sort data=clus_rp; by cluster; proc print data=clus_rp; var subjid subjinit grp cluster distance; proc freq data=clus_rp; table cluster * grp / chisq exact; %mend clus_rp; proc candisc data=clus_tr anova out=can; class cluster; var phamd_7 phamd_w1 pmads_w1 pcsrs_7 pcsrs_w1 pcgis_7 pcgis_w1; proc plot data=can; plot can1*can2=cluster / haxis=-4 to 4 by 1 vaxis=-6 to 6 by 1; %mend clus_tr; FIGURE 1. MACRO FOR CLUSTER ANALYSIS Definition of Terms in Figure 1 and Table 2 fl_tr - SAS input data set for training group of subjects. fl_rp - SAS input data set for replication group of subjects. max_cl - Number of clusters. clus_tr - SAS output data set for training group of subjects. clus_rp - SAS output data set for replication group of subjects. grp - Treatment groups. phamd_7 - Percent change from baseline for HAMD (Hamilton 21 item Depression Rating Scale) for Day 7. phamd_w1 - Percent change from baseline for HAMD for Day 14 (Week 1 for follow-up period). pmads_w1 - Percent change from baseline for MADS (Montgomery-Asberg Depression Scale) for Day 14. pcsc_7 - Percent change from baseline for CSRS (Carroll Self-Rating Scale for Depression) for Day 7. pcsc_14 - Percent change from baseline for CSRS for Day 14. pcgis_7 - Percent change from baseline for CGI (Clinical Global Impression - Severity of Illness) for Day 7. pcgis_w1 - Percent change from baseline for CGI for Day 14. N:\WP\PROP\PPIN9453\621286\PAPERS\1PAPER.DOC sc 6/18/99 9:32 AM Page: 6

7 OUTPUT 1. RESULTS OF FASTCLUS PROCEDURE FASTCLUS Procedure: Replace=FULL Radius= Maxclusters=4 Maxiter=2 Converge=.2 Initial Seeds PHAMD_7 PHAMD_W1 PMADS_W1 PCSRS_7 PCSRS_W1 PCGIS_7 PCGIS_W Minimum Distance Between Initial Seeds = Relative Change in Seeds Iteration Criterion Convergence criterion is satisfied. Criterion Based on Final Seeds = Summary Maximum Frequency RMS Std Deviation Distance from Seed to Observation Nearest Distance Between Centroids Statistics for Variables Variable Total STD Within STD R-Squared RSQ/(1-RSQ) PHAMD_ PHAMD_W PMADS_W PCSRS_ PCSRS_W PCGIS_ PCGIS_W OVER-ALL Pseudo F Statistic = Approximate Expected Over-All R-Squared = Cubic ing Criterion = WARNING: The two above values are invalid for correlated variables. Means PHAMD_7 PHAMD_W1 PMADS_W1 PCSRS_7 PCSRS_W1 PCGIS_7 PCGIS_W Standard Deviations PHAMD_7 PHAMD_W1 PMADS_W1 PCSRS_7 PCSRS_W1 PCGIS_7 PCGIS_W N:\WP\PROP\PPIN9453\621286\PAPERS\1PAPER.DOC sc 6/18/99 9:32 AM Page: 7

8 OUTPUT 2. CLUSTER DEFINITION FOR ALL SUBJECTS OBS SUBJID TREAT GRP CLUSTER DISTANCE 1 1 Placebo 2 3 INN INN Placebo 5 18 Placebo 6 21 Placebo 7 35 Placebo 8 39 INN Placebo 1 47 INN Placebo 12 9 Placebo 13 1 INN Placebo Placebo INN Placebo INN INN INN Placebo 22 6 Placebo Placebo INN Placebo INN Placebo INN INN INN INN INN INN Placebo Placebo 36 5 INN Placebo 38 8 INN Placebo 4 15 INN INN INN INN Placebo INN Placebo INN Placebo Placebo placebo con< con< placebo placebo placebo placebo con< placebo con>= placebo placebo con>= placebo placebo con< placebo con< con< con>= placebo placebo placebo con< placebo con>= placebo con>= con>= con>= con>= con< con>= placebo placebo con>= placebo con< placebo con< con< con< con< placebo con< placebo con< placebo placebo N:\WP\PROP\PPIN9453\621286\PAPERS\1PAPER.DOC sc 6/18/99 9:32 AM Page: 8

9 2 Mean % Change vs Baseline Day 3 2 Mean % change vs Baseline Week % Change in HAMD % Change in MADR % Change in CSRS % Change in CGIS Day 5 2 Week Day 7 2 Week FIGURE 2. MEAN PERCENT CHANGE VS. BASELINE FOR FOUR CLUSTER SOLUTION - FROM DAY 3 TO WEEK 3 BY CLUSTERS N:\WP\PROP\PPIN9453\621286\PAPERS\1PAPER.DOC sc 6/18/99 9:32 AM Page: 9