Developing “EXSAS” an integrated system for statistical analysis of non-clinical experimental data using animals

We have been developing “EXSAS”, which combines SAS with Excel to provide accurate and user friendly statistical analysis system for investigators in preclinical department. EXSAS was systematically constructed in order to make it easy to manipulate and to understand the result of statistical analysis even for experimenters with limited knowledge of statistics. After preparing the data in the specified form of Excel sheet, EXSAS directly imports Excel sheet to SAS dataset and experimenters choose a statistical method from the menu. The results of statistical analysis are converted to an Excel sheet through output delivery system (ODS). The following statistical methods are available in the most resent version of EXSAS 6.10; preparatory analysis, basic parametric methods. several types of ANOVA, parametric multiple comparisons, tree-type algorithms, nonparametric multiple comparisons, dose response relationship analysis, analysis of contingency table, estimation of ED50 and LD50, survival analysis, analysis of bioequivalence study, sample size design and random allocation. Introduction The biostatistical evaluation of toxicity studies and pharmacological experiments using animals should be supported the decision whether a findings is positive or negative and to evaluate the magnitude of the biological effects quantitatively. It is obvious that biostatistics plays a basic role in adequate pharmaceutical assessment. Statistical analysis in animal experiments possesses both a confirmative and an explorative character simultaneously. We have been discussing statistical analysis of nonclinical studies and many statistical methods for these studies have already been proposed for the last two decades. But we have not reached a consensus between experimenters and statisticians yet. Statisticians have a tendency to make the characteristic of data too simple and to assume an ideal situation, normal distribution or homogeneity of variance, which is not plausible for actual data. They also prefer to invent a new statistical method. In contrast, experimenters require the analytical methods for more complex situations with an outlier or heterogeneity of variance and their main interest is how to choose an appropriate statistical method for real experimental data from a lot of well-established methods, depending on the characteristic of data and the aim of the experiment. However it is very difficult for experimenters, who usually do not understand the detail of mathematical aspect of biostatistics, to choose a proper analytical method. Experimenters required a supporting tool to select an appropriate statistical method from a lot of proposed methods. This is our strong motive for developing “EXSAS”. What is “EXSAS”? We have been developing “EXSAS” since 1994, which combines SAS with Excel to provide accurate and user friendly statistical analysis system for investigators in preclinical department. After arranging and classifying the determined statistical methods for basic animal experiment, the EXSAS was systematically constructed in order to make it easy to understand and to manipulate even for experimenters with limited knowledge of statistics. The basic data platform of EXSAS is Excel, which is the commercially most available spreadsheet and fundamental tabulation software. After preparing the data in the specified form of Excel sheet, EXSAS directly imports an Excel sheet to a SAS dataset and experimenters can select a statistical method from the menu. A menu of EXSAS was shown in Figure 1. The results of statistical analysis are converted to an Excel sheet or a HTML file through output delivery system (ODS). It is expected that the EXSAS will enhance the reliability of reporting of experimental results by presenting a common standard of statistical analysis. The legitimacy of decision making in the process of pharmaceutical development depends also on the validity of the data and the software for management and statistical analysis. Standardization of statistical procedure is essential for creating a new drug. Characteristics of EXSAS The following statistical methods are available in the most resent version of EXSAS 6.10. Fundamental statistics, preparatory analysis, basic parametric methods, several types of ANOVA, parametric multiple comparisons, treetype algorithms, nonparametric multiple comparisons, dose response relationship analysis, analysis of contingency table, estimation of ED50 and LD50, survival analysis, analysis of bioequivalence study, sample size design and random allocation. The list of statistical methods available in EXSAS Version 6.10 was shown in Table 1. EXSAS has several unique characteristics. Some standard statistical methods for non-clinical experiments are not available in native SAS procedures, particularly nonparametric tests. Therefore, we produced a couple of SAS macros for parametric multiple comparisons or nonparametric (jointranking and separate-ranking) multiple comparisons. These macros include the following methods, Williams test, Shirley test, Shirley-Williams test, Steel test and Steel-Dwass test. Six (O, M, H, F, J and K) tree type algorithms for statistical analysis can be applied in EXSAS. These are convenient for selecting statistical methods, depending on data type (normal or not, homogeneity or heterogeneity). As an example, type O, type M and type H algorithms are shown in Figure 2. EXSAS also provides useful methods for design of biological experiment, sample size design and random allocation of animals to experimental group. EXSAS requires BASE/SAS, SAS/STAT, SAS/GRAPH and SAS/INSIGHT in Windows8.2. It is available on both Microsoft EXCEL 97 and EXCEL2000. In this presentation, we will demonstrate a brief overview of EXSAS using actual experimental data. Application of EXSAS The typical 9 biochemical measurements data from a repeated dose toxicity study using rats were analyzed by EXSAS. The data consisted of four groups (control (1) low (2), middle (3) and high dose (4)). The sample size was 10 except control group (N=8) The type H algorithm (Hamada et al. 1998) was applied to these data. The content of this algorithm was demonstrated in Figure 2. The result of the H type algorithm was summarized in Table 2. This algorithm emphasizes the importance of visual exploratory analysis. Figure 3 showed multiple scatter plots using SAS/INSIGHT. They are very useful for understanding the tendency of data. For example, we can see an outlier on the scatter plot of CPK (ID number 34 in group 4). SAS/INSIGHT is very convenient, because clicking on this outlier using a mouse highlights the observation with symbol ‘ ’ on nine scatter plots simultaneously; therefore we can recognize the other measurements of this animal at a first glance. Even though CPK is extremely high, the other measurements do not show any abnormalities. Any changes in the data can also be recognized easily. For example, with GOT and GPT, both the mean and the SD increases, with increasing dosage. On the other hand, there is almost a linear dose-response relationship with TC. EXSAS can directly access SAS/INSIGHT. Therefore, it is very easy to draw a scatter plot or a boxplot. There were five cases which were significant using the Bartlett test for raw data at the 0.01 level. The results of the Bartlett test for log-transformed data were also demonstrated in Table 1. Figure 2 exhibited that increasing variability, with increasing mean, resulted in heterogeneity of variance with GOT and GPT. In contrast, the outlier in the fourth group caused heterogeneity of variance with CPK and TBI. A large variance of the control group in GLU made the results of the Bartlett test significant. After log-transformation, the Bartlett test ceased to be significant with GOT GPT CPK at the 0.05 level. The variable transformation was useful for these items to improve heterogeneity of variance. Then, it was decided to analyze the transformed data hereafter. While logtransformation made the data characteristic of TBI and GLU better, the Bartlett test was, however, still significant at the 0.05 level, therefore raw data for these two items were analyzed. There were two cases when the absolute value of studentized residual (SR) was greater than 4, the fourth group of CPK (620) and the fourth group of TBI (0.13). After detecting statistical outliers, the biological significance and its impact on the result of statistical analysis should be evaluated. There were seven items in which at least one dosed group was significant for the Dunnett test at the two-tailed 0.05 level. With GOT, GPT, TC and TBI, a significant change was observed from the third group. With ALP, TG and GLU, there was a significant change only in the fourth group. As a result of the linear regression method, there were six significant items at the two-tailed 0.01 level. The linearity is assessed using MF (Model Fitness) in the type H algorithm. This statistics is defined as the ratio of the sum of the square for the regression method to the between sum of the square for one-way ANOVA. The possible range of model fitness is between 0 and 100%. When the model fitness is 100%, the mean values locate in a completely linear configuration. Compared to the result of the Dunnett test, GLU did not show a significant dose-dependency and MF was not considerably high (73.6%). The dose-response relationship was different from a linear one. In contrast, MF of TC is almost 100%; therefore the dose-response shape of TC was completely linear. On the other hand, MF was 80-90% with GOT GPT and TBI. The cause of lack of fit corresponded to no change in the second group. TG demonstrated a large change only in the fourth group and MF was less than 80%. ALP had the smallest MF value, 43.8% and the dose-response relationship was far from linear. ALP had a threshold type dose-response relationship. There was no change below the fourth group, The output of EXSAS for GPT was shown in Table 3. The