Optimization of Clinical Data Analysis

Transcription

1 Optimization of Clinical Data Analysis Hugues Gerard, Philippe Richardot, Robin Deransy, Marc-Antoine de Rotrou AR.R.I.S. Applications et Realisations de la Recherche Informatique et Statistique Keywords medical, pharmaceutical clinical trials, data analysis Abstract Facing a sharp increase concerning the variety and volume of data manipulated within a corporation, AR.R.I.S. has developped an overall strategy aimed to increase productivity while complying with the following objectives - creating a set of processing tools independent from specific data - insuring data integrity - obtaining results of the utmost quality In order to realize these objectives, four major principles have been used - a unique universe (SYSTEM SAS ) - the data dictionary (defining each item) - a specificity for each task (macro-language) - the logical integration of these tasks The concept developed by A.R.R.I.S., AS.S.E.T., is currently used within clinical trial analysis and has produced significant results in terms of productivity and workload scheduling. It will be explained and illustrated in detail, with examples extracted from clinical trial analysis, using the SYSTEM SAS The database management system is more storage than analysis driven. This means that once the data has, been stored into the database, it is seldom possible to compute a full statistical analysis. The use of full fledged satatistical packages is possible using data transfer techniques (data transfer is rarely a science... ). Duplicating data can itself be a very misleading operation when editing occurs on the wrong version. The second feature is that DBMS's lack data integrity control. This fact is of the utmost importance, as the value of data is directly related to the amount of control that is operated. Quality assurance should cover every area of data processing: data collection, entry, editing, and modification. Methods can be univariate, such as preventing out-of-range data at entry, or multivariate, such as preventing illogical relationship, e.g. "pregnant males". Data editing is a multi-step procedure which uses univariate and multivariate tests [NANS 1] to assure data consistency and integrity. All would be fme if data structure did not change all the time. The sharp increase today, not only concerns the amount of data but even more the variety. Data structures. are different for each analysis, as for example m market research or in the clinical trials. Therefore it is essential to create data structures or use and transform previous data structures. All this should be done by the end-user, who has the direct relation to data. According to the ANSI/X3/SPARC committee proposals [JARD 1], database description should be separated into the following three levels: internal, conceptual and external schema. These will be detailled later in this paper., I: " i Introduction As mass storage prices fall and computers proliferate, as much in quantity as in power, organizations are confronted with vast amounts of valuable data. The latter must be collected, entered, controlled, stored retrieved, computed and finally reported. ' The concept of database has emerged as to bring a solution to these needs. Database management systems take pieces of information and transform them into organized files. They have been recognized as the most efficient storage and management for data organization. The relational database model introduced by Codd in 1970 [CODD 1] and late; extended [CODD 2] has acted as a foundation for present DBMS's. But three major drawbacks appear which leed to further concepts. ' It is important to define the goals and specificities of clinical trials. This will be done by explaining the process of a clinical trial. Mter, three major points will be emphasized : - storage, management and analysis of data - data dictionary as external schema - quality control at all levels ASSET will then be described in all it's functionalities. 183

2 Clinical trials. The objectives of a clinical trial are to evaluate drug efficacy and safety. This represents a major task: in the preparation of a new drug application dossier for the health authorities. Clinical data are highly specific, and this is due to multiple reasons : - data are collected at different periods of time, from different sources and have to be brought together in a single dossier - medical and legal aspects are closely related as safety is a widespread concern. The usual process is described in figure I and is commonly spread in the pharmaceutical industry. Storage, Management and Analysis Each clinical study will generate an amount of data which needs an efficient storage system. Nevertheless, clinical databases are seldom very large. A common clinical database may have about 500 patients, and 800 items of information for each patient A given Pharmaceutical Laboratory may carry out as much as 200 clinical studies in a year. It is clear that it is more important to lay emphasis on data management than on data storage. Concern has grown on data management techniques, such as different DataBase Management System (DBMS) types (hierarchical, network, relationnal) or interface approaches, such as natural language. But the view would be incomplete if not taken into account the end-users main aim, data analysis. Data entry I ~ Quality control Statistical analysis Medical Doctors Case Report Forms filled in for each patient - Medical Figure 1 report Storage, management and data analysis are three parts of a common process: they need a common language. A given software must integrate these 3 parts arid tend to optimize each one individualy. SAS software, together with all the modules (SAS/FSP, SAS/GRAPH. SAS/SHARE) complies to the needs previously defmed. Figure 2 explains the multi-level system architecture development with A.S.S.E.T. Data Dictionary According to the ANSJ/X3/SPARC commitee proposals, database description should be separated into three levels: - the internal schema describes how information is stored and managed by the data management and operating system - the conceptual schema describes how the information contents can be modelled in the database, independently from both stomge and specific user needs - the external schema describes how data can be viewed, accessed and modified by end-users and procedures The multi-level system architecture ensures a complete physical and logical data independence. j G Case Report Forms (CRF) are filled in by Medical Doctors and collected at the end of the study by the Pharmaceutical. Laboratory. Data are checked, possibily recoded and entered, via a data entry program, into a data management system. After a second checking, the quality control, which aim is to ensure that data in the computer match the CRF, analysis may begin. The results of the latter will be viewed by MD's in order to produce a medical report concerning the drug's efficacy and/or safety. The structure of a given database must be defmed. Each and every item must be defined, with long and short labels, formats, limits and data types (numeric or character). This task is usually fastiduous, and is a source of errors. What is more, many databases may have a structure very similar. The external schema is represented by the data dictionary, which provides the necessary information to create the data sets and the format library. 184

3 During data entry or analysis, the dictionary will distinguish the data processing (numeric or character), statistical (continuous or discontinuous) and medical (secondary effects, concommitant medication) aspects of data. The AS.S.E.T. data dictionary is a S.AS. dataset, in which each observation corresponds to the description of a database item. This data dictionary may be viewed or modified using only S.AS, on the mainframe or the micro-computer. And the data dictionary checking programs are written in S.AS. The data dictionary may be entered on mainframe or micro-computer. Quality control Quality control concerns both meta-data and data. Meta~data (dictionary) control occurs during data dictionary entry. For data, control occurs in different ways - for standard items (as blood pressure or WBC) a limits database contains the values of lower and upper bounds for the item; these bounds are given according to sex and age - for all items, univariate analysis are executed By these means, outliers are immediately detected. Multivariate controls are applied to check protocol compliance as well as date lags. Multi-level system architecture A.S.S.E.T. AS.S.E.T. is a software designed to operate a systematic statistical analysis of clinical trial data. The whole ASSET process is written using the SAS/Macro Language: this ensures the common language condition remaining in a unique universe: SAS. The Macro language provides an open system, as each module may be adapted Based on SAS/AF panels, ASSET is divided into ten major steps, which will be explained in detail. Each step may be only be operated if the previous has terminated correctly. INITIALIZATION This step prepares the computer environement for the study by allocating files (macros, SAS programs, libraries). The user is then prompted for the origin of data (sequential file, SAS datasets or data to be entered directly with FSEDIT). DATA DICTIONARY ENTRY The data dictionary is entered with FSEDIT,on mainframe of micro-computer, using formatted screens; this will define the study structure. At this step, a user may create a completely new data dictionary or call back a previously created one to use it. This is important in case of a meta-analysis, when data of multiple studies are pooled. The data dictionary is a SAS dataset, in which each observation is the definition of a study item. The definition will include long and short labels, formats, limits and data types (numeric or character). Items seen at different periods can easilly be repeated, using simple SAS statements. Checking will occur after data dictionary entry. Colums overlapping, bad labels, the number of codes for discontinuous variables...,all errors will be edited to be corrected. The data dictionary will be printed by alphabetic order of items and position order. This document will be the common discussion basis between all actors of the clinical trial analysis: MD's, statisticians, DP personnel. CREATING THE CRFDATA SET D.B. Database D.O. Data dictionary This step will be executed only if data are to be read from a sequential file or te be entered with FSEDIT screens. Data will be transfered into a SAS dataset. This ensures that in all possible cases, the process is evolving in the unique universe: SAS. Figure 2 Errors of data type will be detected as well as duplicate patient numbers. 185

4 STEP 4 FORMATS AND LABELS STATISTICAL ANALYSIS Now the formats and labels are created. This is done only now, as this step is independent from the data itself. This step may be repeated if the data dictionary is modified, without re-reading raw data. A common case is when a study is to be re-analyzed, only changing the labels from English to German, for example. The different formats will be printed, so they can be checked. OUALITY CONTROL The following controls will be operated : - a reference limits database will ensure that standard items (ie: blood pressure) are within defined bounds; outliers will be printed - univariate analysis will be performed for all items - for all items, each missing patient number will be printed Statistical computations are operated on SAS datasets.there are several macros, which principles will be explained. The end-user may define the items, patients, periods or centers to keep or drop. Four aggregate variables can be chosen: center, stratification, period and treatement. The correct statistical test (T,F, Newmann- Keuls,multiple comparisons, cm2) is applied corresponding to the number of treatement groups and the type of the variable (continuous or discontinuous). This statistical test (degrees of freedom, value of the test) is printed together with the tabulate (proc tabulate) for the item (mean, minimum,... ). This gives a better overview of the behaviour of treatement for this item. GRAPmCS SAS/AF panels guide the user in choosing the appropriate graphic representations of data. Graphic enhancements, title, footnotes can be chosen. - for each patient and each period, a ratio of non missing items is provided The CRF is printed by patient. STEP 10 GENERAL MANAGEMENT PROTOCOL AND DELAY The user is asked for conditions of protocol compliance, conditions under the form of SAS statements. The latter will be submitted by ASSET. The date lag checking are perfomed in comparison to pre-defined values. All violations are flagged and printed. This step is devoted to the study supervisor, in order to provide information about the process sequencing: the number of times the whole process has been executed, elapsed time of the study, the names of created datasets. The study can be stored on a magnetic tape, guided by SAS/ AF panels. TOOL DATASETS The final datasets, used for statistical purposes, are created at this step from the CRF dataset. Items are segmented into different datasets according to their medical type (fixed, seen at different periods, secondary effects,... ). Necessary information is found in the data dictionary. For datasets of items seen at different periods of time, for each item, the value is computed against the value of this item at a reference period. The reference period is defined and entered at step 2. These computations are sent in a special dataset. ASSET is a modular process composed at the present time, of ten main modules. Each and every module may be modified and adapted by the end-user. Additional modules may be implemented and directly integrated into the whole process, without need for major transformations. ASSET is completly written with SAS version 5.l6 and takes full advantage of new features, in particular, new functionalities of the macro-language. Values of computations may be printed. 186

5 Conclusion I The field of clinical trial analysis is very specific, due to the diversity of people involved, the complexity of data and the medico-legal aspects. ASSET has been designed keeping in mind the problems which have been explained in this paper. ASSET is a software, but also a concept. The concept of integrating data processing, statistical and medical factors together with all the people involved in clinical trials. Future developments will be held in two major directions. New statistical features will be implemented, such as the integration of factor analysis. Also, Case Report Forms will be designed directly with ASSET, in relation with the data dictionary using formatted FSEDIT screens. References [CODD 1] Codd EF : A relational model of data for large shared data banks. CommA.CM.1970; 13; [CODD 2] Codd EF : Extending the database relational model to capture more meaning CommA.C.M. 1979; 4; [JARD 1] Jardine DA : The ANSI SPARC DBMS model North Holland, Amsterdam, 1978 [NANS 1] Nans J : Data quality control and editing Marcel Decker Inc., New York, 1975.[SAS] SAS,SAS/AF, SAS/FSP, SAS/GRAPH, are registered trademarks of SAS Institute Inc., Cary N.C., U.S.A SAS/SHARE is a trademark of SAS Institute Icn., Cary N.C., U.S.A. [ASSET] A.S.S.E.T. (Analyse Statistique Systematique de l'essai Therapeutique) is a trademark of Clinica & Statistica Reprint address Marc Antoine de Rotrou A.R.R.I.S. 25, Avenue de l'europe F-9231O SEVRES FRANCE (1)