An Introduction to Analysis (and Repository) Databases (ARDs)

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1 An Introduction to Analysis (and Repository) TM Databases (ARDs) Russell W. Helms, Ph.D. Rho, Inc. Chapel Hill, NC Presented to DIA-CDM: Philadelphia, PA, 1 April 2003

2 Introduction to Analysis Databases Outline: Lite Introduction to Analysis Databases Differences among types of Clinical Trials (CT) databases Types of Analysis Databases Conclusion Copyright 2003 Rho, Inc. 2

3 1. Lite Introduction: What is an Analysis Database? The answer to this question depends upon whom you ask. To a programmer-user or statistician-user an analysis database is: a collection of integrated [SAS] datasets, with excellent documentation and metadata, so that most requests can be handled by simple one- PROC programs. (The mantra is, Answers are one PROC away. ) One PROC, method, function, Copyright 2003 Rho, Inc. 3

4 1. Lite Introduction: What is an Analysis Database? To a programmer-user or statistician-user: A collection of integrated datasets For example, one dataset has one record (SAS observation) per subject, and contains all the data that appear once per subject. Data includes derived variables e.g., change from baseline to endpoint. Data includes statistics e.g., mean baseline score, averaged over multiple baseline visits. There are datasets with other structures, too. Copyright 2003 Rho, Inc. 4

5 1. Lite Introduction: What is an Analysis Database? To a pharma executive, project manager, or similar: A compound analysis database ( CAD, explained later) is a quick source of easily accessible information about a drug development program. Efficacy data and analyses from each study, and integrated across studies. Safety data and analyses from each study, and integrated across studies. Software is required to provide easy access to an analysis database (more on this later, or ask afterwards!) Copyright 2003 Rho, Inc. 5

6 1. Lite Introduction: What is an Analysis Database? To a statistical programmer producing data displays (TFLs: tables, figures, listings) an analysis database is: Simplicity: All the difficult, annoying data problems have been resolved and/or documented and All the difficult, annoying data manipulations have been done. Most programs consist of one PROC to create output (ODS), then formatting output. Copyright 2003 Rho, Inc. 6

7 1. Lite Introduction: What is an Analysis Database? To an FDA reviewer or pharma reviewer, an analysis database will be: A godsend: All the difficult, annoying data problems have been resolved and/or documented and All the difficult, annoying data manipulations have been done. One can easily check whether data resolutions and manipulations were performed correctly. Anticipated reaction: I can focus on interpreting the data! Copyright 2003 Rho, Inc. 7

8 1. Lite Introduction: What is an Analysis Database? To an FDA reviewer: A number of FDA statistical reviewers have said they want to see the code that generated certain data displays. Without an analysis database the code is usually too complicated involving dataset manipulations (merges, etc.). With a one PROC away analysis database, the code that generates the results is simple. Code to produce pleasing appearance may be longer. Copyright 2003 Rho, Inc. 8

9 1. Lite Introduction: What is an Analysis Database? To the team of biostatisticians, clinical scientists, programmers, and others who must produce analysis databases from already-completed studies, an analysis database is: One of the most challenging scientific tasks in drug development. A very large amount of work, performed under tight deadlines. It is much more efficient to do it right from the beginning than make a heroic rescue at the end. Copyright 2003 Rho, Inc. 9

10 2. Differences among Types of Clinical Trials Databases What are the differences among: An analysis database, An operational (CRF, domain ) database (CT-DBMS), and A data warehouse? The primary difference is in ease of use for analysis. An analogy can help clarify this difference. (We re still in lite mode.) Copyright 2003 Rho, Inc. 10

11 2. Differences among Types of Clinical Trials Databases There are more technical descriptions of differences among various types of CT databases. The architecture of each type of database is organized to make database operations efficient. An analysis database, a CT-DBMS and a data warehouse have different objectives and functionality. Thus, The structures are very different. The ways in which they are prepared are very different. Copyright 2003 Rho, Inc. 11

12 2. Differences among Types of Clinical Trials Databases: 2.2 Comparison of OBJECTIVES The primary objectives of a CT-DBMS are: Data computerization i.e., transcription of data into a computer file, using any of several processes (e.g., data keying, scan/ocr, electronic data capture). Data cleansing i.e., challenge questionable data values (generate a query ), obtain a corrected value or a verification of correctness, update the database accordingly. Data import e.g., from a central laboratory. Data coding, e.g., coding of adverse events or concomitant medications. Data export e.g., to SAS for statistical computations and reporting. Copyright 2003 Rho, Inc. 12

13 2. Differences among Types of Clinical Trials Databases: 2.2 Comparison of OBJECTIVES The primary objectives of an Analysis Database are: To facilitate rapid scientific analysis without additional data manipulation. Goal: data files that are one PROC away from scientifically valid answers. One PROC away files can be analyzed directly, without data manipulations. One PROC away and scientific validity imply that much thought has gone into the design of the database and many preparatory processing steps have been performed. Copyright 2003 Rho, Inc. 13

14 2. Differences among Types of Clinical Trials Databases: 2.2 Comparison of OBJECTIVES Primary Objectives of a data warehouse are: Serve as a repository for data files from a variety of sources, including, e.g.: Multiple data files from a clinical trial, Data files from multiple clinical trials. To serve as a source of data files for future integrated analyses. Generally, the integration is performed as a part of the analysis project, not at the time data are inserted into the warehouse. Typically, the data files are not one PROC away and a substantial number of data processing steps are required before actual statistical analyses can be performed. Copyright 2003 Rho, Inc. 14

15 2. Differences among Types of Clinical Trials Databases: 2.3 Comparison of STRUCTURES The structure of a database is designed to make database operations efficient. Thus: A CT-DBMS database is designed to facilitate data capture, error detection and correction, etc. The database organization incorporates the organization of a study s case report form (CRF). The data is usually normalized so that each value only occurs once. The database changes as the data are cleaned, and changes only need to be made in one place. A data warehouse typically contains: Primarily copies of original data files from individual studies. A variable often has different names, formats, and other characteristics in different data files. Data structures typically vary from study to study. A typical CT-DBMS or data warehouse contains minimal metadata. Copyright 2003 Rho, Inc. 15

16 2. Differences among Types of Clinical Trials Databases: 2.3 Comparison of STRUCTURES An Analysis/Repository Database is designed to facilitate rapid scientific analysis. Each dataset is structured to facilitate a different set of analyses (per-patient, per-visit, AE s, ) Each record contains data integrated from many different CRF pages. Each record contains data derived or computed from many different CRF pages. Many values exist in multiple formats and locations to facilitate analysis. An analysis database contains extensive documentation and metadata to guide unfamiliar users. Copyright 2003 Rho, Inc. 16

17 2. Differences among Types of Clinical Trials Databases: 2.4 Comparison of PREPARATION Because of these differences in objectives and structures, the ways in which the databases are prepared are very different. CT-DBMS database: The structure of a database for a study is initially set up by data processors, using the study protocol and CRF, to create tools for data entry (or other data capture), error detection and correction, etc. The content is created by data processors who enter data, comments, corrections, etc., during data management processes. Copyright 2003 Rho, Inc. 17

18 2. Differences among Types of Clinical Trials Databases: 2.4 Comparison of PREPARATION Data warehouse: Structure is primarily inherited from individual source datasets. A few integrated data files have more structure. Content is created by programmers or data processors who populate the data warehouse by: Creating new directories for files from new studies, Copying the files into the directories, and Updating documentation of the contents of the files. Creating integrated data files upon request. Copyright 2003 Rho, Inc. 18

19 2. Differences among Types of Clinical Trials Databases: 2.4 Comparison of PREPARATION Analysis or Repository Database: The structure is designed by a combination of clinical scientists, biostatisticians, and epidemiologists, and is based on anticipated queries and statistical analyses. the content is created by: Importing data from the randomization system, the CT-DBMS, or other sources. Computing values of derived variables (e.g., change-frombaseline). Adding metadata information about the data. Other processes, based on scientific or administrative needs. Copyright 2003 Rho, Inc. 19

20 3. Types of Analysis Databases A Study Analysis Database (SAD) contains data, metadata, documents, etc. from one study. A Compound Analysis Database (CAD) contains data, metadata, documents, etc., from all the clinical studies of the specific compound. We build a CAD by adding one SAD at a time. A Product Database contains all of the SADs, and the latest version of the CAD. Previous versions of the CAD are retained. Copyright 2003 Rho, Inc. 20

21 2. Types of Analysis Databases: 2.4 Study Analysis Database CT-DBMS ( e.g., Oracle Clinical) for One Study Export to SAS Collection of SAS datasets Inc/Exc Criteria Create Study Analysis Database Study Analysis Database Patient- Specific Data Visit-Specific Data Data Organized in Other Ways Study Operational Database Set-up DB, data entry, clean data, lock DB, etc Demography Adverse Events Etc. Metadata: Information on Variables, Datasets EXTENSIVE DOCUMENTATION! Create analysis database: Create metadata. Reorganize data ( one PROC away ). Prepare derived variables. Discover, solve, and document Quirks TM of clinical data. Test, test, test. Copyright 2003 Rho, Inc. 21

22 4. Conclusion There are many steps in the creation of analysis databases SADs, CADs, Product Databases that require acute scientific judgment. Some decisions are no-brainers that do not require judgment. As in other areas of science, many really important analysis database decisions require extensive training, experience, deep insight, and other characteristics of some humans, aided by computers. This intensive scientific process leads to a database that is (all together now) One PROC away! Copyright 2003 Rho, Inc. 22

One-PROC-Away: The Essence of an Analysis Database Russell W. Helms, Ph.D. Rho, Inc.

One-PROC-Away: The Essence of an Analysis Database Russell W. Helms, Ph.D. Rho, Inc. One-PROC-Away: The Essence of an Analysis Database Russell W. Helms, Ph.D. Rho, Inc. Chapel Hill, NC RHelms@RhoWorld.com www.rhoworld.com Presented to ASA/JSM: San Francisco, August 2003 One-PROC-Away

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