20: Data management
- Page ID
- 13269
\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \) \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)\(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\) \(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\)\(\newcommand{\AA}{\unicode[.8,0]{x212B}}\)
- 20.1: Introduction to data management
- All intervention trials involve the collection and management of data, often in large quantities. In order to get the most out of study data, it is important to have worked through plans for the collection, management, and use of the data early in the planning stages of a trial. Previous editions of the Toolbox discussed the role and choice of computers in the management of trial data,
- 20.2: Before starting to collect data
- All trials need appropriate resources to collect data and information, to check the consistency and quality of the data, and to organize the data into a suitable form for analysis. It is important that all the steps of the trial and the associated data flow are planned before starting the trial, and the resources needed at each step are defined.
- 20.3: Planning the data flow
- There are many advantages to collecting and storing research data electronically. Electronic storage of data facilitates easy retrieval, simpler generation of study reports, easy exportation to statistical packages, and rapid data sharing. The benefits of electronic storage of data can only be fully realized if the database storing the data is well designed. A poorly designed database leads to poor performance, inefficient data queries, inaccurate and unreliable data, and redundant data that are
- 20.4: Data collection systems
- In this section, we review some of the ways in which data can be collected from the participants and put into an electronic database.
- 20.5: Managing data
- Data management is a major task in most intervention trials. The main stages of the data management process are: entering the data into a computer system checking the data for errors and inconsistencies organizing the data into an appropriate form for analysis archiving the data.
- 20.6: Archiving
- New data are brought into a data management centre daily, and many different data changes and decisions are made. It is important that these are recorded and documented. If an accident happens (for example, a fire in the data centre), these changes and decisions could be lost and may be difficult to re-create, with potentially serious consequences for the integrity of the trial. This section advises on some of the ways to backup and keep the data, both for short-term protection and long-term use
- 20.7: Preparing data for analysis
- The ‘raw materials’ for data analysis are the data files created by the data management process. However, the variables, as recorded in the questionnaire and entered into the database as raw data, are not always the ones directly suitable for data analysis. Recoding and creating of new variables is likely to be necessary. It is generally also necessary to combine information from different data files.