PHARMACOKINETIC STATISTICAL ANALYSIS SYSTEM - - A SAS/AF AND SAS/FSP APPLICATION

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1 PHARMACOKINETIC STATISTICAL ANALYSIS SYSTEM - - A SAS/AF AND SAS/FSP APPLICATION Sharon M. Passe, Hoffmann-La Roche Inc. Andrea L Contino, Hoffmann-La Roche Inc. ABSTRACT The statistician responsible for the design of pharmacokinetic studies, the subsequent analysis ofthe data and the report generation, typically repeated the same time-consuming tasks for each new study. A menu-driven system was needed automating these routine functions. The Pharmacokinetic Statistical Analysis System (PSAS) is a SAS based system created to expedite design tasks, data processing, statistical analysis and report generation for the pharmacokinetics area. Base SAS, SAS/FSp, SAS/AF, SAS/STAT and SAS/GRAPH (Version 6.07) software are used in this workstation based system. Modifications and the incorporation of additional options are easily made because of the flexibility of SAS/ AF, SAS/FSp and Screen Control Language. INTRODUCTION The statistician collaborates with the pharmacokineticist to design human clinical trials to study the absorption, distribution, metabolism and elimination of a particular drug under differing conditions. These studies are characterized by the collection of plasma/urine samples at a sequence of times following drug administration and the assay of these samples for drug concentration. Pharmacokinetic variables are derived from the concentration time curve and include, for example, variables such as C max (peak concentration) and AUC (area under the concentration time cune). The statistician is responsible for the subsequent analysis of the pharmacokinetic variables and the final statistical report. The most frequently used designs are open-label randomized 2-,3-. and 4-way crossover or parallel group designs with studies conducted at one site. For each study design the statistical methodology as well as the format of the summary tables remain the same. Because of the well-defined characteris- tics of these pharmacokinetic studies the development of a user-friendly menu-based computer system to handle the different statistical functions in an expedient manner seemed appropriate. HISTORY OF PSAS Prior to the creation of the Pharmacokinetic Statistical Analysis System (PSAS), several very time-consuming steps were required by the statistician for the analysis of a study: Determination of the appropriate methodology to use for the analysis of crossover designs. Creation and validation of SAS code for the analysis. Transcription of PROC GLM results to summary analysis of variance tables for the statistical report. Checking for transcription and typographical errors. Repeating the entire analysis and table preparation if any of the original data was found to be in error. PSAS was developed to meet all of these limitations and in addition could free up additional time for the statistician in that personnel unfamiliar with the statistical aspects could complete an analysis under the statistician's direction. Aside from its time-saving features other statistical functions such as sample size estimation and generation of randomization schedules could be conveniently accessed via PSAS. The original version of PSAS was developed on a mainframe under SAS (VerSion 5.18) using Base SAS, SAS/AF, SAS/FSp and SAS/ STAT which metthe initial requirements of amenu-based system. However, due to the limitations of SAS (Version 5.18) of SAS/AF and SAS/FSp, PSAS lacked flexibility. PSAS was then developed as a PC application under SAS (Version 6.04). Moving PSAS to the' PC environment allowed the use of Screen Con- 741

2 trol Language which enabled the use of the following major features: Cross-field validation within a screen. Checking user-entered information for existence in the SAS dataset. Generation of customized error messages. The processing time on the PC was not a concern because the studies which are analyzed typically have sample sizes of no more than 24. To incorporate the many enhanced capabilities of SAS (Version 6.07), PSAS was moved to a workstation. The following features were then also available: Increased processing speed. Only one copy of PSAS is needed because multiple users may use the workstation. High resolution graphics. Daily back-up of all files automatically done by the corporate information systems group. Due to the ever-changing hardware/software environments, having written PSAS with SAS software has allowed the flexibility of easily moving from one platform to another. PSAS REQUiREMENTS PSAS is written to run on Digital's DECstation 5000 model 200 workstation under the RISC/UL TRIX operating system. Base SAS, SAS/AF, SAS/FSP, SAS/STAT and SAS/GRAPH (Version 6.07) are required. Other necessary hardware include a mouse and either a laserjet and/or a high-speed printer. PSAS creates a permanent SAS dataset from a predefined ASCII data file requiring the user only to enter the treatment description corresponding to each treatment code. Optional variable labels may be added. The data file need only meet minimal specifications. An example of such a data file for a 2-way crossover study is shown in Figure 1. The first record of the file contains the protocol description. The second record contains the identification variables (subject, interval or period of the study, and treatment received) and the variable names of up to 11 variables to be analyzed. All variable names must follow SAS naming conventions. The remaining records of the file contain the data in the order specified on the second record. PSAS FEATURES A schematic representation of the functional capabilities of PSAS is shown in Figure 2. The user is guided through the performance of particular tasks by a series of program screens. As an example Figure 3 shows the first menu screen presented. The specific capabilities of PSAS along with their corresponding options are discussed below: Estimation of sample size Sample sizes may be requested for parallel group, 2-, 3- and 4-way crossover designs for a Single-center study using the two one-sided tests procedure (Ref. 1). This statistical test is used to assess the equivalence of two treatments. The randomization schedule for a design The treatment or sequence of treatment codes for each subject entering the study is automatically printed out. Logarithmically transforming selected variables The results are saved in the permanent SAS dataset. Calculating descriptive statistics and performing analyses of variance Summary descriptive statistics, graphical displays of the data and analyses of variance using PROC GLM under the required models for the design of the study are provided. These analyses require the prior creation of the permanent SAS dataset. An example of the screen requesting user input is shown in Figure 4. As an option data may be subsetted by Subject and/or Interval. Obtaining a report-ready Listing of Individual Responses This listing contains the data analyzed. A sort order of the data based on the identification variables is provided as an option, eg, sorting by Subject and Interval. Obtaining report-ready summary analysis of variance tables 742

3 For these tailor-made tables, PSAS reads the summary statistics directly from the PROC GLM output. User input consists of the desired title, variabies and labels, order of the variables on the tables and the number of decimal places to be displayed for each variable. The screen requesting this information is shown in Figure 5 with an example of the output for a twoway crossover study in Figure 6. Performing the two one-sided tests procedure PSAS reads the summary statistics directly from the PROC GLM output or summary statistics may be entered by the user. Options to analyze logarithmically transformed data or untransformed data and to specify the equivalence criterion are available. In general, whenever queried for the variables to be analyzed, an option is available for a pop-up window overlaying the screen to show the variables in the SAS data set. Selection of the variables is then made from this list as is illustrated in Figure 4. The user has the flexibility within PSAS to view the SAS Log window or to issue commands at the RISC/ULTRIX level without exiting from PSAS. When ending the session the contents of the SAS Log may also be written to a permanent file. Aside from the SAS dataset created from the predefined data file, PSAS automatically generates a minimal number of permanent files which may be further edited if desired. These consist ofthe listings of the data, the results of the statistical analyses and the report-ready analysis of variance tables. FUTURE ENHANCEMENTS PSAS will continue to undergo enhancements as new features become available. Additionally, other options are planned to further expedite the analysis of pharmacokinetic data. Some of these features are: Incorporating analyses of studies with blocking factors, eg, center. I ncorporating analyses of other types of pharmacokinetic studies such as dose- proportionality and stable isotope studies. Adding a feature to browse/edit the SAS dataset or input data file. Allowing for data entry of the input data file. Adding additional diagnostics for detecting outliers and assessing the appropriateness of distributional assumptions. Incorporating non-parametric methods when distributional assumptions are not met. Investigating a possible interface with SAS/INSIGHT. Providing HELP capabilities. CONCLUSIONS PSAS is an ideal application for the use of SAS/ AF, SAS/FSP and Screen Control Language because of the similar study designs and types of analyses frequently required in the pharmacokinetics area. The use of PSAS results in a substantial decrease in the statistician's time. it is portable and modifications and enhancements can be easily made. Although developed for use in the pharmacokinetics area PSAS has' generalizability to any application with the same designs and required statistical methodologies. ACKNOWLEDGEMENT We would like to thank Mr. Michael McGranaghan for contributing the SAS code for the creation of the report-ready ANOVA tables. REFERENCE 1. Schuirmann OJ: A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J Pharmacokinet Biopharm. 15(6): (1987). SAS, SAS/FSp, SAS/AF, SAS/STAT, SAS/ GRAPH and SAS/INSIGHT are registered trademarks of the SAS Institute, Cary, North Carolina. RISC/ULTRIX is a registered trademark of Digital Equipment Corporation, Maynard, Massachusetts. 743

4 Figure 1. An example of an input data file for a two-way crossover study. Protocol M9999 Subject Interval Rx Cmax Tmax 1 1 A B B A A B A B Auc_ Auc Beta Figure 2. A schematic representation of PSAS. ESTIMATION OF SAMPLE SIZE.., ( \.. GENERATION OF ) RANDOMIZATION SCHEME STORED DATA FILE CREATION AND MODIFICATION OF SA DATASET F "- ~ STATISTICAL ANALYSIS AND REPORT GENERATION ).I USER INPUT OF DATA l REPORT GENERATION REPORT GENERATION 744

5 Figure 3. The first menu screen presented to the user. SAS:AF PSAS WELCOME TO THE PHARMACOKINETIC STATISTICAL ANALYSIS SYSTEM ;ltl.~i~i~iiiiii!~!~~! II~n~i~QPifli~~gliliig~l lil~! Statistical Analysis and Report Generation: ';I~I~! tii~iil~~li~iilg~ Version 2.00 Figure 4. An example of a screen requesting information for an analysis. SAS:AF PSAS Enter Protocol Number and Protocol Title: p!iiiii-----~ Variables Make up to 99 selections. Data set: MYDATA.FM9999 Either type in variables to be analyzed us or type a? and Press Enter for a variable. Do you require a subset of observations? Name Type Len Label *QM~I@NIIM ;mlenri i TMAX NUM 8 AUC 24 NUM 8 AUC- NUM 8 BETA NUM 8 745

6 Figure 5. An example of a screen requesting information for generating report-ready summary analysis of variance tables. Variable Title for Variable Number of Decimals: Order: 1iMMi Line 1 Report Title Line 2 Line 3 Line 4 Figure 6. An example of a report-ready summary analysis of variance table for a two-way crossover study. TABLE 1 Protocol M Analysis of Variance Results Mean Square Source , I"... -, of Variation df. Cmax. Imax AUC24 AUC Beta _1-...! _,_... _1_... _,_ _1_... _, I Sequences : Subjects within Sequences: 10: ,--, " ,-... -,-... -, , I Periods I Treatments 6n , Error : '-.. J... _1_... _1_ , Statistical significance (F-tests): + p<0.10; * p<o.05; ** p<o