Data Management of Clinical Studies

Transcription

1 Data Management of Clinical Studies Magdolna Kiss, Dr. Artila B. Kovac. Chinoin Pharmaceutical and Chemical Works Ltd. Co. Hungary 1 : 1 " 'f, f: > ', }. :'J- ",< " i ;; ' "., F:", " ':.!1 " f }; l' l:.: ' B l l! ' E " :.f 1. Introduction It was a few years ago when in the pharmaceutical industry it became evident that if we wish to conduct clinical studies according to the current standards of Good Clinical Practice (GCP) we have to face the challenge of being able to meet GCP requirements also in the field of clinical data management. The first and most essential step to comply with this challenge was to choose an internationally accepted software package OD which the complete management of clinical study data can be based. As SAS System was well known of its powerful statistical analysing capabilities, we decided to chose it also for the purpose of data management. In our concept clinical data management starts with case report fonn (CRF) design and lasts until data are ready for statistical analysis. The whole process can be divided into the preparatory and pracical parts. As an integrated tool SAS is used from the preparatory tasks including dataset definition, data entry screen design and setting up vahdation requirements, through each practical steps of the two data entries and data validity control until data processing. We have been using SAS System for 2 and a half years, during this period of time the whole data manageme'nt activity became gradually covered by the SAS system. In the following it is described how we proceeded to find out the appropriate solutions from the beginning until now.,, \, 959

2 2. Rationalization To enable verified data entry producing good quality data suitable for statistical analysis, both the preparatory and the practical part of data management activity needed to be rationalized Preparatory Standardization Standardized panels have been created, which include sample CRFs, predefined database structures together with the relevant customized data entry screens, views and program files. :. The panels include samples for - demographic data, - general anamnesis, - vital signs and physical examination. - general neurological examination, - hematology, - blood chemistry. - urinanalysis, -ECG, - X-ray, - adverse event reports, concomitant medication, - drug accountability. Besides speeding up the preparatory process standardization enabled the consistent collection and reporting of non-study-specific data items. If needed the standardized panels can be adjusted to the particular study requirements. \ ", 960

3 ... -;- 'Descriptive statistics' block creates some statistical infonntion on the variables defined as primary efficacy parameters in the study.specific panels. These tables are shown in the output window and can be used when compiling the statistical analysis. Figure 1 Menu system 961

4 Data entry screen design Data entry screens were designed on the basis of CRFs. The fields location on the screen were adjusted to the overall outlook of CRFs. Each screen contains the minimal but enough text which enables the operator to identify the appropriate fields to be filled in. As a header the study number, visit number, patient's number were shown on each screen. The applied measurement units were always recorded. According to the above multiple screens consisting of 9-14 parts were setup for each visit. The order of screens were determined by the order of CRFs. Following field identification special attributes were assigned to certain fields and other data verifying functions were built in by SeL programs to reduce the possibility of entering invalid data. Menu system For the purpose of creating a user-friendly environment block menu was used. The primary nleou was set up according to the ain tasks the application was aimed at. (Figure 1) 'Entry l' block calls for the data entry screens by visits. The screens appear in 'add' mode offering immediately the entry of a new record. 'Entry 2' block calls for the same screens by visits, nevertheless the entered data get into the doubled datasets with the same structure. 'Comparing' block runs a compare procedure for the dataset pairs resulting from the two data entries. The output window shows a detailed description of discrepancies which represents an important document of data validation. 'Modifying' block calls for the entry screens by visit without offering the entry of a new record. The existing records of both entries can be modified. As the datasets resulting from the second entry are considered to be the correct, this block is used mainly for 'modifying those ones. 'Listing' block enables making lists by visits and panels. Lists app:ar in the output window with the relevant title, showing the data according to patient's number. They can be printed directly or saved and used for documentation. 'Browsing' block makes possible to have a look at the study data by panels in fsview windows. 962

5 _ ' 'r, 2.2.Practlcal Study-speeitic application development Although SAS provides us a complete control over our data. there was a need for setting-up an environment suitable for end-users e.g. Clinical Research Associates responsible for a particular study Aim We developed applications for certain studies to enable perfonning basic everyday tasks, such as data entry, comparison, listing according to visits or panels, data modification, browsing the data by panels and calculating some basic descriptive statistics of main therapeutic parameters. I:',: f -' Means SASIFSP and SASI AF with sel features have been used Method Dataset structure definition Datasets were defined on the basis of the characteristics and structure of infonnation collected on the specially designed CRFs. The basic requirement was to involve all data recorded on the main part of the CRFs so that the unanimous identification of the patient a'nd the time of observation should be possible. When defining the datasets by panels. the available standardized panels have been used for non-study-specific data. The study-specific panels consisted of the variables representing the efficacy and safety parameters to be collected throughout the study progress according to the protocol. Then the datasets of specific and non-specific panels were merged to fonn one dataset per visit serving the aim of data entry. Since we intended to meet the requirement of data verification by entering the data twice, the defined dataset structures of each visit were doubled. 963 >:. : -. ':.::..-:: -=-:::..:--

6 Double Data Entry As we reviewed our data management process, the weak point proved to be data validation. Using OUT study-specific applications the comparison of the two data entries was perfonned after the second entry had been completed and it was followed by the modification. Although both the comparison and the modification can be well documented, there was an undoubtable need for a more complex double data entry system enabling on-line verification. This fact forced us to begin an application development cooperation with the Hungarian ISYS Computing Ltd. with the aim of ensuring double data entry with on-line checking while drawing attention to the discrepancies. documenting modifications with the reasons and persons doing so. So, our data validation system became complex with the Double Data Entry application which provides a complete audit trailing facility: 3. Current Development Diredion Our current plans are directed towards the special need for Adverse Event Surveillance. OUT aim was to develop an application for keeping records on the unexpected - serious and non-serious - adverse event reports arriving to the Clinical DevopmeDt Group from clinical studies performed with Chinoin drugs in Hungary and abroad. The application is based on the 'Suspect Adverse Event' CIOMS. form widely used in Europe including information on the event, suspect drug(s). concomitant drug(s) and history, etc. As primary function periodic report generation is provided by suspect dmg(s) and by WHO code numbers of the event according to the 'International Classification of the Diseases' SA System, SASIFSp and SASIAP are registered trademarks Q/SAS Institute Inc., Cary. NC, USA. 964