Practical guide to clinical data management free download

Any data management group, large or small, long established, or just emerging, will have to perform these tasks or be closely involved with them.

Even companies that do not perform data management in house, but rather send all their data management work to contract research organizations, will need to beware of these elements and oversee them as they are carried out.

For the most part, they all did a good job of this and produced datasets with accurate data that reflected values provided by the investigator sites. However, even with good study files, some data management groups found they could not always quickly find an answer when an auditor asked a specific question about the conduct of a past study.

So, several years ago, some companies began to address this problem by creating a document whose purpose was to record all the most important information on how data management was carried out for a study.

They quickly found that creating this kind of document at the start of a study provides added value — beyond its function as a reference — by forcing study planning. The documents are also more accurate when written at the start of a study rather than as a summary or report at the end of the study. A DMP that is written at the beginning of a study provides a focus for identifying the work to be performed, who will perform that work, and what is to be produced as documentation of the work.

Plans can vary in length depending on the size of the data management group and how standard study activities are. In this chapter, we will discuss what goes into these plans and how to use them efficiently. What goes into a plan? A DMP should touch on all the elements of the data management process for the study in question. The elements are those that form the structure of Part I of this book and are summarized in Figure 1. Who is responsible for the work? Which SOPs or guidelines will apply?

What documentation or output will be collected or produced? By including the final point in each section, that of documentation or output, the DMP also becomes an approximate table of contents for the study file and can be used as a source document for internal quality assurance audits. There is a lot of consistency in the content of DMPs from company to company. DMPs across companies touch on roughly the same topics, even if the exact names of the headers or the ways the topics are grouped into sections vary.

However, experienced data managers can still differ quite a lot in their opinions as to how much detail to include in a particular section of a DMP. At some companies, the DMP is very detailed and includes text copied from other documents so that the plan is a complete record of the study. More commonly, the DMP documents key information not included elsewhere and refers to appendices or other independent documents for details.

Both of these approaches are valid and meet the need of consolidating data management process information. Topics to Cover in a Data Management Plan 1. This outline can be used for nearly all kinds of studies at all kinds of companies. Revising the DMP It is very likely that during the course of an average study, some critical data management process or a key computer application will change. Even in a short-term study, some detail of a data handling procedure or a number of cleaning checks will change as the group acquires experience with the data specific to that trial.

Even though the DMP is a plan, that is, it is the way you expect to conduct the study, it must be revised whenever there is a significant change because it will then show how you expect to conduct the study from that point forward.

The DMP must be kept current, and that is harder to manage than one might expect. Companies constantly wrestle with finding the best or most practical way to record updates to the data management process in a DMP.

Is normal change control sufficient or does it warrant attention in the plan? In whatever way it is accomplished, after study lock, the DMP should reflect all important changes to the data management process and computer systems that took place during the study. In fact, many CROs have more comprehensive plans than those found at sponsors because they hold themselves to a higher level of documentation for their process.

In biopharmaceutical firms, QA is closely tied to regulatory compliance because good practice must be closely tied to following regulations. Regulatory compliance and quality assurance are critical even in emerging companies too small to have a separate QA group. A key requirement of most quality methods is the creation of a plan; a key requirement of Good Clinical Practice GCP is the documentation of what has happened during a study.

The DMP helps fulfill both of these requirements by creating the plan and detailing what documents will record the conduct of the study. The DMP and the output documents it requires can be used as the starting point when conducting internal QA audits of the data management process.

As noted above, it is also used by external auditors. SOPs for DMPs and study files Every data management group should have a process for documenting how a study was conducted. The process must be described formally in either an SOP or a department guideline. For most companies, this means that they will require a DMP for each study. Some companies will address this need by creating a detailed SOP governing the contents of the study file.

The procedure should be clear at which point the plan must be in place. For the DMP to be a plan — rather than a report at the end of the study — a draft or an initial version typically needs to be in place before any substantial data management work is performed for the study. A section on the requirements for revision control of the document should clearly state under what circumstances the DMP must be revised and how the revision is to be documented.

Along with the procedure for creating and maintaining a DMP, there should be a blank template document or an outline for the plan to assure consistency.

Each section in the template should have instructions on what kind of information and what level of detail is expected. An example of a completed DMP is also a good way to show the level of detail expected by a given company.

DMPs specify what output documents are to be created during the course of the study. These must be filed somewhere.

In either case, the two must be kept in synchronization. If the SOP for the DMP states that the final database design will be filed in the study file, there must be a folder or tab in the study file for that document.

If the SOP states that the study file must have a particular folder or tab, it must actually be there — with something in it — during an audit.

Companies that use only study files, not a DMP document, will have only a study file SOP but will still collect the same information. All information required to document the conduct of the study should be listed in the study file SOP. The same issues noted above apply here as well: be careful what you require, and if you require it, make sure it is actually in the study file when it is supposed to be filed.

Finally, for both approaches, consider how to file documents for CRO studies. Some documents normally produced during a study conducted by the sponsor may be produced by the CRO as well but are then kept internally by the CRO rather than transferred to the sponsor. One example is a CRF annotated for the database design. If the sponsor will receive transfer datasets at the end of study rather than a database transfer of some sort , the sponsor has no need of this design document for the database.

This is particularly useful for long-term studies and growing data management groups. To avoid overwhelming staff with documentation requirements, managers of data management groups should encourage the use of templates and the use of previous plans.

The first few plans will require some work; after that the burden should be considerably reduced as each new plan builds on the experience of the previous ones.

The key is to keep DMP requirements both focused and practical. At some companies, the first draft of the CRF is created by a clinical research associate; at others, data managers prepare the initial version. Even if data managers are not responsible for the creation of the CRF, they should be closely involved in its review and approval as part of a cross-functional team. A cross-functional team is the only way to design a CRF that will collect the necessary data in a way that is clear and easy for the investigator, efficient for data management processing, and appropriate for analysis.

The team must balance company standards with the needs of the individual study and take into account preferences of the team members and investigators. For the most part, the issues and concepts that follow apply to both paper CRFs and entry screens that are part of electronic data capture applications. Later in the chapter we will also look at handling revisions to CRFs and general quality considerations. Therefore, working on a CRF design that reduces the need for checks on fields that are administrative only that is, they do not actually add to the quality of the data has a lot of value to data management.

Asking the investigator to provide duplicate or repeat information in different locations is one such source of low-value queries. Other low-value queries will be generated if the CRF designer does not make adequate provision for blank or unavailable responses or permits ambiguous responses. Duplicate data CRFs frequently include questions that are intentionally used as cross-checks to other fields. This can be a very good policy when the data is actually different.

Or, the weight of a patient can be checked against a weight-dependent dosage to assure proper compliance with the protocol. The fields are different, but related. Problems do arise, however, when cleaning programs cross-check values that are actually duplicates of the same data.

Discrepancies and confusion are bound to be generated when the investigator asks for both values and they do not agree.

The values will have to be checked against each other and mismatches will, without a doubt, occur. A form of indirect duplication of values in the CRF is asking the investigator to calculate values. If the protocol asks the investigator to measure blood pressure three times and then to calculate the mean, the value of the mean may be inappropriately duplicating the information in the three measurements.

Sometimes a protocol does require the investigator to perform calculations as part of determining the correct course of treatment.

In this case, the trial sponsor should consider providing space and methods for the calculation on a worksheet or in an instruction area that is not entered as part of the CRF data. A monitor would double-check the calculation as part of the normal source document verification.

It is worth noting, having warned against it, at least one example of a case when duplication does provide value in data cleaning. Header information, such as patient identifiers and initials, are such an example of useful duplication. In studies where pages can come in singly or become separated from the booklet, a cross-check of patient ID with initials for the data on every page against enrollment information has proven invaluable in identifying mislabeled pages.

Missing and ambiguous responses Missing and ambiguous responses can occur at the level of the individual field, at the level of a module or grouping of questions on the CRF, or at the entire page level. When considering CRF layout, the design team should consider appropriate actions at each level. Data management might annoy the investigator sites by double-checking via a query.

For CRF fields that are not associated with check boxes, data management, working together with clinical and biostatistics team members, should decide whether or not to instruct the sites to write something in a field if there is no data or reply available. The team must decide if only one or a selection of these texts is to be deemed an acceptable response. In some data management groups, the text is actually entered into the database for that field or into a related text field; see Chapter 3 for more on this , in which case no discrepancy is raised.

A Practical Guide for Informationists: Supporting Research and Clinical Practice guides new informationists to a successful career, giving them a pathway to this savvier, more technically advanced, domain-focused role in modern day information centers and libraries. The book's broad scope serves as an invaluable toolkit for healthcare professionals, researchers and. A single trial is complex, with numerous regulations, administrative processes, medical procedures, deadlines and specific protocol instructions to follow.

And yet, there has existed no single-volume, comprehensive clinical research reference manual for investigators, medical institutions, and national and international research personnel to keep on the shelf as a ready reference. Extensively revised and updated, with the addition of new chapters and authors, this long-awaited second edition covers all aspects of clinical data management.

Giving details of the efficient clinical data management procedures required to satisfy both corporate objectives and quality audits by regulatory authorities, this text is timely and an. A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data.

The subjects that the practitioner must be aware of are not only technological and scientific, but also organizational and managerial. Therefore, the author offers case studies based on real life. Libraries organize information and data is information, so it is natural that librarians should help people who need to find, organize, use, or store data.

Organizations need evidence for decision making; data provides that evidence. Inventors and creators build upon data collected by others. All around us, people need data. Grady, N. Newman Eds. This Third Edition retains the concise, easy-to-read approach of the earlier editions, is full of updated examples, and adds new developments in the methods of clinical and translational research. The research question Study subjects McFadden E. Management of Data in Clinical Trials.

Practical Guide to Clinical Data Management. In the third edition of Introduction to Health Research Methods, every chapter from the second edition has been The new edition also features several new chapters and subsections that provide expanded coverage of the clinical and Author : Susanne Prokscha Publisher: CRC Press ISBN: Category: Computers Page: View: Read Now » The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment.

Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, the third edition of Practical Guide to Clinical Data Management includes important updates to all chapters to reflect the current industry approach to using electronic data capture EDC for most studies. It also details the context of regulations that guide how those systems are used and how those regulations are applied to their installation and maintenance.

Keeping the coverage practical rather than academic, the author hones in on the most critical information that impacts clinical trial conduct, providing a full end-to-end overview or introduction for clinical data managers. It emphasizes generic methods of medical documentation applicable to such diverse tasks as the electronic patient record, maintaining a clinical trials database, and building a tumor registry.

The book also guides professionals in the design and use of clinical information systems in various health care settings. It is an invaluable resource for all health care professionals involved in designing, assessing, adapting, or using clinical data management systems in hospitals, outpatient clinics, study centers, health plans, etc. The book combines a consistent theoretical foundation of medical documentation methods outlining their practical applicability in real clinical data management systems.

Two new chapters detail hospital information systems and clinical trials. There is a focus on the international classification of diseases ICD-9 and systems, as well as a discussion on the difference between the two codes. All chapters feature exercises, bullet points, and a summary to provide the reader with essential points to remember. New to the Third Edition is a comprehensive section comprised of a combined Thesaurus and Glossary which aims to clarify the unclear and sometimes inconsistent terminology surrounding the topic.

This extensively updated fifth edition reflects the current knowledge in Health Informatics and provides learning objectives, key points, case studies and references. Available as a printed copy and E-book. Author : J.