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Use of databases for clinical research

PMID: 24489362
DOI: 10.1136/archdischild-2013-304466

Databases are electronic filing systems that have been set up to capture patient data in a variety of clinical and administrative settings. While randomised controlled trials are the gold standard for the evaluation of healthcare interventions, electronic databases are valuable research options for studies of aetiology and prognosis, or where trials are too expensive/not logistically feasible. However, databases exist in many different settings and formats (often developed for administrative or financial reimbursement purposes rather than clinical research), and researchers need to put careful thought into identifying and acquiring relevant data sets. Accuracy of records and validation of diagnoses are key issues when planning a database study. High-quality databases can readily capture outcome data (as part of routine clinical care) without the costs and burden of additional trial-related follow-up, and there are promising hybrid models which combine the benefits of randomisation with the efficiency of outcome ascertainment using existing databases.

Keywords: Evidence Based Medicine; Health Service; Information Technology; Outcomes Research.

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Key Characteristics of Database Studies on Drug Effectiveness in the Postmarketing Stage: A Systematic Review

Systematic Review
Published: 01 November 2021
Volume 35 , pages 327–338, ( 2021 )

Cite this article

Chihaya Shiragasawa 1 &
Mamoru Narukawa 1

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In recent years, real-world data (RWD) have been actively used in the field of pharmaceutical research. Database (DB) study, one of the observational studies using RWD, is a comprehensive, continuous, and rapid research method that plays an important role in the postmarketing stage of drugs, although the interpretation of the results may be limited. DB studies are often focused on drug safety, and previous research reviewing DB studies on drug effectiveness across different disease areas have been limited.

The objective of this review was to reveal the current status of DB studies on drug effectiveness in various therapeutic areas and to provide information that allows researchers to consider conducting appropriate DB studies on drug effectiveness in the postmarketing stage.

For this systematic review, we searched Embase and MEDLINE for DB studies on drug effectiveness published between 1 January 2018 and 31 December 2019. We reviewed the title, abstract, and methods to identify studies on drug effectiveness using medical information DBs, and excluded non-medical studies, studies on non-drug, and studies on drug safety, actual use, or cost outcomes that did not include any effectiveness outcomes. The name and type of the DB (administrative claims DB, clinical DB, pharmacy DB, and DB linkage), study design, comparison group, type of outcome, and presence or absence of reference to the outcome definition were extracted and summarized according to disease areas.

We obtained 225 articles on DB studies on drug effectiveness using DBs that integrate large-scale medical data for secondary use across different disease areas. Among the DB classifications, administrative claims DBs (70%, 158/225) were most commonly used, while pharmacy DBs were used in only three studies. The largest number of reported studies were associated with cardiovascular, respiratory, and infectious diseases. Outcomes were often inpatient diagnosis, and some ideas included defining effectiveness based on drug use. While various outcomes were uniformly used in studies for the treatment of infectious diseases and respiratory organs, death (overall survival [OS]) and drug continuation (progression-free survival [PFS]) in patients with cancer, laboratory values in the endocrine system (mainly diabetes) were used as the main outcomes. Outcome validation within the article was limited. New user design (32%, 73/225), propensity score analysis (58%, 131/225), and sensitivity analysis (40%, 90/225) were used as measures to reduce bias in these studies. Sixty-eight studies (30%, 68/225) were supported by pharmaceutical companies.

Conclusions

This systematic review summarized the status of cross-disease research articles on DB studies on drug effectiveness. While considering the strengths and limitations of DB studies, we hope that our comprehensive results would help to promote appropriate DB studies on drug effectiveness in the postmarketing stage.

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Shiragasawa, C., Narukawa, M. Key Characteristics of Database Studies on Drug Effectiveness in the Postmarketing Stage: A Systematic Review. Pharm Med 35 , 327–338 (2021). https://doi.org/10.1007/s40290-021-00406-8

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DOI : https://doi.org/10.1007/s40290-021-00406-8

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Finding Qualitative Studies

Qualitative Research Resources: Finding Qualitative Studies

Created by health science librarians.

What is Qualitative Research?
Qualitative Research Basics
Special Topics
Training Opportunities: UNC & Beyond
Help at UNC
Qualitative Software for Coding/Analysis
Software for Audio, Video, Online Surveys

About this Page

Ready-built sets of search terms, database-specific search strategies, general qual search strategies, web resources.

Assessing Qualitative Research
Writing Up Your Research
Integrating Qualitative Research into Systematic Reviews
Publishing Qualitative Research
Presenting Qualitative Research
Qualitative & Libraries: a few gems
Data Repositories

Why is this information important?

Electronic databases for health science literature, such as PubMed or CINAHL, often do not index qualitative health studies very clearly.
Authors also do not always identify their methods using the word "qualitative" in their titles or abstracts; in some cases they may use terminology for a specific qualitative method instead.
Often, that means that it is hard to find qualitative studies in common health science databases like PubMed

On this page you'll find:

articles that describe and evaluate search strategies for finding qualitative research
articles that provide search strategies for specific databases
web resources on search filters and finding qualitative articles in databases
links to sets of search terms to use when searching for qualitative research
Hedges: Evidence Based Health Informatics, McMaster University contains qualitative hedges for Medline, PsycInfo, and Embase
ISSG Search Filters Resource: Qualitative Research Filters The InterTASC Information Specialists' Sub-Group Search Filter Resource is a collaborative venture to identify, assess and test search filters designed to retrieve research by study design or focus. The Search Filters Resource aims to provide easy access to published and unpublished search filters. It also provides information and guidance on how to critically appraise search filters, study design filters in progress and information on the development and use of search filters. Inclusion of a search filter is not an endorsement of its validity or a recommendation.
PubMed Health Services Research Queries Using Research Methodology Filters

A Few Articles on Search Strategies for Specific Databases

Wilczynski NL, Marks S, Haynes RB.2007. Search strategies for identifying qualitative studies in CINAHL. Qualitative Health Research 17(5):705-10.

Walters LA, Wilczynski NL, Haynes RB; Hedges Team. 2006. Developing optimal search strategies for retrieving clinically relevant qualitative studies in EMBASE. Qualitative Health Research 16(1):162-8.

Wong SS, Wilczynski NL, Haynes RB, Hedges Team. 2004. Developing optimal search strategies for detecting clinically relevant qualitative studies in MEDLINE. Medinfo 11: 311-316.

McKibbon KA, Wilczynski NL, Haynes RB. 2006. Developing optimal search strategies for retrieving qualitative studies in PsycINFO. Evaluation and the Health Professions 29: 440-454.

CINAHL & PsycINFO :

Rosumeck S, Wagner M, Wallraf S, Euler U. A validation study revealed differences in design and performance of search filters for qualitative research in PsycINFO and CINAHL . J Clin Epidemiol. 2020 Dec;128:101-108. doi: 10.1016/j.jclinepi.2020.09.031. Epub 2020 Sep 26. PMID: 32987157.

MEDLINE, CINAHL, Social Science Citation Index (SSCI) :

DeJean D, Giacomini M, Simeonov D, Smith A. Finding Qualitative Research Evidence for Health Technology Assessment . Qual Health Res. 2016 Aug;26(10):1307-17. doi: 10.1177/1049732316644429. Epub 2016 Apr 26. PMID: 27117960.

MEDLINE, EMBASE, CINAHL, PsycINFO :

A Few Articles on General Search Strategies for Qualitative Literature

Booth, A. (2016). Searching for qualitative research for inclusion in systematic reviews: A structured methodological review . Systematic Reviews, 5 doi:http://dx.doi.org.libproxy.lib.unc.edu/10.1186/s13643-016-0249-x

Cook, A., D. Smith, and A. Booth. 2012. Beyond PICO: the SPIDER tool for qualitative evidence synthesis. Qualitative Health Research 10: 1435-1443.

Evans, D. 2002. Database searches for qualitative research . Journal of the Medical Libraries Association , 90(3): 290-293.

Flemming K, Briggs M. 2007. Electronic searching to locate qualitative research: evaluation of three strategies. J Adv Nurs . 57(1):95-100

Gorecki CA, Brown JM, Briggs M, Nixon J. 2010. Evaluation of five search strategies in retrieving qualitative patient-reported electronic data on the impact of pressure ulcers on quality of life . J Adv Nurs . 66(3):645-52

Grant MJ. 2004 How does your searching grow? A survey of search preferences and the use of optimal search strategies in the identification of qualitative research. Health Info Libr J . 21(1):21-32

Littleton, D, S Marsalis, D Z Bliss. 2004. Searching the literature by design . Western Journal of Nursing Research 26(8): 891-908.

Methley, A.M., S. Campbell, C. Chew-Graham, R. McNally, and S. Cheraghi-Sohi. 2014. PICO, PICOS, and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews . BMC Health Serv Res 14: 579.

Pearson, M., Moxham, T., & Ashton, K. 2011. Effectiveness of Search Strategies for Qualitative Research About Barriers and Facilitators of Program Delivery . Evaluation & the Health Professions , 34(3), 297–308. https://doi.org/10.1177/0163278710388029

Petticrew, Mark and Helen Roberts. 2008. Systematic Reviews in the Social Sciences: A Practical Guide. Chapter 4: How to Find the Studies: The Literature Search . Blackwell Publishing: Oxford, UK.

Shaw RL, Booth A, Sutton AJ, Miller T, Smith JA, Young B, et al. 2004. Finding qualitative research: an evaluation of search strategies . BMC Med Res Methodol 4:5

Campbell Collaboration Information Retrieval Guide Campbell Collaboration is an organization that guides and publishes systematic reviews. This resource is their publication on searching strategies and finding articles; it is not specific to qualitative studies but offers useful hints.
NYU Libraries: Locating Qualitative Research Includes a good starting list of qualitative keywords for a general keyword based search strategy that can be cut and pasted into any database plus database specific strategies for CINAHL, Medline (including PubMed), and PsycINFO (with APA Index Terms). Note that PsycINFO via OVID strategies would need to be translated for UNC's Ebsco version.
University of Washington LibGuide: Finding Qualitative Research Articles This guide gives some basic general search strategies when looking for qualitative literature, as well as specific search strategies for specific databases (CINAHL, PubMed, PsycInfo), books, and grey literature.
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In brief: what types of studies are there.

Last Update: September 8, 2016 ; Next update: 2024.

There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked.

When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following questions may be asked:

What is the cause of the condition?
What is the natural course of the disease if left untreated?
What will change because of the treatment?
How many other people have the same condition?
How do other people cope with it?

Each of these questions can best be answered by a different type of study.

In order to get reliable results, a study has to be carefully planned right from the start. One thing that is especially important to consider is which type of study is best suited to the research question. A study protocol should be written and complete documentation of the study's process should also be done. This is vital in order for other scientists to be able to reproduce and check the results afterwards.

The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies.

Randomized controlled trials

If you want to know how effective a treatment or diagnostic test is, randomized trials provide the most reliable answers. Because the effect of the treatment is often compared with "no treatment" (or a different treatment), they can also show what happens if you opt to not have the treatment or diagnostic test.

When planning this type of study, a research question is stipulated first. This involves deciding what exactly should be tested and in what group of people. In order to be able to reliably assess how effective the treatment is, the following things also need to be determined before the study is started:

How long the study should last
How many participants are needed
How the effect of the treatment should be measured

For instance, a medication used to treat menopause symptoms needs to be tested on a different group of people than a flu medicine. And a study on treatment for a stuffy nose may be much shorter than a study on a drug taken to prevent strokes .

“Randomized” means divided into groups by chance. In RCTs participants are randomly assigned to one of two or more groups. Then one group receives the new drug A, for example, while the other group receives the conventional drug B or a placebo (dummy drug). Things like the appearance and taste of the drug and the placebo should be as similar as possible. Ideally, the assignment to the various groups is done "double blinded," meaning that neither the participants nor their doctors know who is in which group.

The assignment to groups has to be random in order to make sure that only the effects of the medications are compared, and no other factors influence the results. If doctors decided themselves which patients should receive which treatment, they might – for instance – give the more promising drug to patients who have better chances of recovery. This would distort the results. Random allocation ensures that differences between the results of the two groups at the end of the study are actually due to the treatment and not something else.

Randomized controlled trials provide the best results when trying to find out if there is a cause-and-effect relationship. RCTs can answer questions such as these:

Is the new drug A better than the standard treatment for medical condition X?
Does regular physical activity speed up recovery after a slipped disk when compared to passive waiting?
Cohort studies

A cohort is a group of people who are observed frequently over a period of many years – for instance, to determine how often a certain disease occurs. In a cohort study, two (or more) groups that are exposed to different things are compared with each other: For example, one group might smoke while the other doesn't. Or one group may be exposed to a hazardous substance at work, while the comparison group isn't. The researchers then observe how the health of the people in both groups develops over the course of several years, whether they become ill, and how many of them pass away. Cohort studies often include people who are healthy at the start of the study. Cohort studies can have a prospective (forward-looking) design or a retrospective (backward-looking) design. In a prospective study, the result that the researchers are interested in (such as a specific illness) has not yet occurred by the time the study starts. But the outcomes that they want to measure and other possible influential factors can be precisely defined beforehand. In a retrospective study, the result (the illness) has already occurred before the study starts, and the researchers look at the patient's history to find risk factors.

Cohort studies are especially useful if you want to find out how common a medical condition is and which factors increase the risk of developing it. They can answer questions such as:

How does high blood pressure affect heart health?
Does smoking increase your risk of lung cancer?

For example, one famous long-term cohort study observed a group of 40,000 British doctors, many of whom smoked. It tracked how many doctors died over the years, and what they died of. The study showed that smoking caused a lot of deaths, and that people who smoked more were more likely to get ill and die.

Case-control studies

Case-control studies compare people who have a certain medical condition with people who do not have the medical condition, but who are otherwise as similar as possible, for example in terms of their sex and age. Then the two groups are interviewed, or their medical files are analyzed, to find anything that might be risk factors for the disease. So case-control studies are generally retrospective.

Case-control studies are one way to gain knowledge about rare diseases. They are also not as expensive or time-consuming as RCTs or cohort studies. But it is often difficult to tell which people are the most similar to each other and should therefore be compared with each other. Because the researchers usually ask about past events, they are dependent on the participants’ memories. But the people they interview might no longer remember whether they were, for instance, exposed to certain risk factors in the past.

Still, case-control studies can help to investigate the causes of a specific disease, and answer questions like these:

Do HPV infections increase the risk of cervical cancer ?
Is the risk of sudden infant death syndrome (“cot death”) increased by parents smoking at home?

Cohort studies and case-control studies are types of "observational studies."

Cross-sectional studies

Many people will be familiar with this kind of study. The classic type of cross-sectional study is the survey: A representative group of people – usually a random sample – are interviewed or examined in order to find out their opinions or facts. Because this data is collected only once, cross-sectional studies are relatively quick and inexpensive. They can provide information on things like the prevalence of a particular disease (how common it is). But they can't tell us anything about the cause of a disease or what the best treatment might be.

Cross-sectional studies can answer questions such as these:

How tall are German men and women at age 20?
How many people have cancer screening?
Qualitative studies

This type of study helps us understand, for instance, what it is like for people to live with a certain disease. Unlike other kinds of research, qualitative research does not rely on numbers and data. Instead, it is based on information collected by talking to people who have a particular medical condition and people close to them. Written documents and observations are used too. The information that is obtained is then analyzed and interpreted using a number of methods.

Qualitative studies can answer questions such as these:

How do women experience a Cesarean section?
What aspects of treatment are especially important to men who have prostate cancer ?
How reliable are the different types of studies?

Each type of study has its advantages and disadvantages. It is always important to find out the following: Did the researchers select a study type that will actually allow them to find the answers they are looking for? You can’t use a survey to find out what is causing a particular disease, for instance.

It is really only possible to draw reliable conclusions about cause and effect by using randomized controlled trials. Other types of studies usually only allow us to establish correlations (relationships where it isn’t clear whether one thing is causing the other). For instance, data from a cohort study may show that people who eat more red meat develop bowel cancer more often than people who don't. This might suggest that eating red meat can increase your risk of getting bowel cancer. But people who eat a lot of red meat might also smoke more, drink more alcohol, or tend to be overweight. The influence of these and other possible risk factors can only be determined by comparing two equal-sized groups made up of randomly assigned participants.

That is why randomized controlled trials are usually the only suitable way to find out how effective a treatment is. Systematic reviews, which summarize multiple RCTs , are even better. In order to be good-quality, though, all studies and systematic reviews need to be designed properly and eliminate as many potential sources of error as possible.

German Network for Evidence-based Medicine. Glossar: Qualitative Forschung. Berlin: DNEbM; 2011.
Greenhalgh T. Einführung in die Evidence-based Medicine: kritische Beurteilung klinischer Studien als Basis einer rationalen Medizin. Bern: Huber; 2003.
Institute for Quality and Efficiency in Health Care (IQWiG, Germany). General methods . Version 5.0. Cologne: IQWiG; 2017.
Klug SJ, Bender R, Blettner M, Lange S. Wichtige epidemiologische Studientypen. Dtsch Med Wochenschr 2007; 132:e45-e47. [ PubMed : 17530597 ]
Schäfer T. Kritische Bewertung von Studien zur Ätiologie. In: Kunz R, Ollenschläger G, Raspe H, Jonitz G, Donner-Banzhoff N (eds.). Lehrbuch evidenzbasierte Medizin in Klinik und Praxis. Cologne: Deutscher Ärzte-Verlag; 2007.

IQWiG health information is written with the aim of helping people understand the advantages and disadvantages of the main treatment options and health care services.

Because IQWiG is a German institute, some of the information provided here is specific to the German health care system. The suitability of any of the described options in an individual case can be determined by talking to a doctor. informedhealth.org can provide support for talks with doctors and other medical professionals, but cannot replace them. We do not offer individual consultations.

Our information is based on the results of good-quality studies. It is written by a team of health care professionals, scientists and editors, and reviewed by external experts. You can find a detailed description of how our health information is produced and updated in our methods.

Cite this Page InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG); 2006-. In brief: What types of studies are there? [Updated 2016 Sep 8].

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Qualitative data analysis methods should flow from, or align with, the methodological paradigm chosen for your study, whether that paradigm is interpretivist, critical, positivist, or participative in nature (or a combination of these). Some established methods include Content Analysis, Critical Analysis, Discourse Analysis, Gestalt Analysis, Grounded Theory Analysis, Interpretive Analysis, Narrative Analysis, Normative Analysis, Phenomenological Analysis, Rhetorical Analysis, and Semiotic Analysis, among others. The following resources should help you navigate your methodological options and put into practice methods for coding, themeing, interpreting, and presenting your data.

Users can browse content by topic, discipline, or format type (reference works, book chapters, definitions, etc.). SRM offers several research tools as well: a methods map, user-created reading lists, a project planner, and advice on choosing statistical tests.
Abductive Coding: Theory Building and Qualitative (Re)Analysis by Vila-Henninger, et al. The authors recommend an abductive approach to guide qualitative researchers who are oriented towards theory-building. They outline a set of tactics for abductive analysis, including the generation of an abductive codebook, abductive data reduction through code equations, and in-depth abductive qualitative analysis.
Analyzing and Interpreting Qualitative Research: After the Interview by Charles F. Vanover, Paul A. Mihas, and Johnny Saldana (Editors) Providing insight into the wide range of approaches available to the qualitative researcher and covering all steps in the research process, the authors utilize a consistent chapter structure that provides novice and seasoned researchers with pragmatic, "how-to" strategies. Each chapter author introduces the method, uses one of their own research projects as a case study of the method described, shows how the specific analytic method can be used in other types of studies, and concludes with three questions/activities to prompt class discussion or personal study.
"Analyzing Qualitative Data." Theory Into Practice 39, no. 3 (2000): 146-54 by Margaret D. LeCompte This article walks readers though rules for unbiased data analysis and provides guidance for getting organized, finding items, creating stable sets of items, creating patterns, assembling structures, and conducting data validity checks.
"Coding is Not a Dirty Word" in Chapter 1 (pp. 1–30) of Enhancing Qualitative and Mixed Methods Research with Technology by Shalin Hai-Jew (Editor) Current discourses in qualitative research, especially those situated in postmodernism, represent coding and the technology that assists with coding as reductive, lacking complexity, and detached from theory. In this chapter, the author presents a counter-narrative to this dominant discourse in qualitative research. The author argues that coding is not necessarily devoid of theory, nor does the use of software for data management and analysis automatically render scholarship theoretically lightweight or barren. A lack of deep analytical insight is a consequence not of software but of epistemology. Using examples informed by interpretive and critical approaches, the author demonstrates how NVivo can provide an effective tool for data management and analysis. The author also highlights ideas for critical and deconstructive approaches in qualitative inquiry while using NVivo. By troubling the positivist discourse of coding, the author seeks to create dialogic spaces that integrate theory with technology-driven data management and analysis, while maintaining the depth and rigor of qualitative research.
The Coding Manual for Qualitative Researchers by Johnny Saldana An in-depth guide to the multiple approaches available for coding qualitative data. Clear, practical and authoritative, the book profiles 32 coding methods that can be applied to a range of research genres from grounded theory to phenomenology to narrative inquiry. For each approach, Saldaña discusses the methods, origins, a description of the method, practical applications, and a clearly illustrated example with analytic follow-up. Essential reading across the social sciences.
Flexible Coding of In-depth Interviews: A Twenty-first-century Approach by Nicole M. Deterding and Mary C. Waters The authors suggest steps in data organization and analysis to better utilize qualitative data analysis technologies and support rigorous, transparent, and flexible analysis of in-depth interview data.
From the Editors: What Grounded Theory is Not by Roy Suddaby Walks readers through common misconceptions that hinder grounded theory studies, reinforcing the two key concepts of the grounded theory approach: (1) constant comparison of data gathered throughout the data collection process and (2) the determination of which kinds of data to sample in succession based on emergent themes (i.e., "theoretical sampling").
“Good enough” methods for life-story analysis, by Wendy Luttrell. In Quinn N. (Ed.), Finding culture in talk (pp. 243–268). Demonstrates for researchers of culture and consciousness who use narrative how to concretely document reflexive processes in terms of where, how and why particular decisions are made at particular stages of the research process.
The Ethnographic Interview by James P. Spradley “Spradley wrote this book for the professional and student who have never done ethnographic fieldwork (p. 231) and for the professional ethnographer who is interested in adapting the author’s procedures (p. iv) ... Steps 6 and 8 explain lucidly how to construct a domain and a taxonomic analysis” (excerpted from book review by James D. Sexton, 1980). See also: Presentation slides on coding and themeing your data, derived from Saldana, Spradley, and LeCompte Click to request access.
Qualitative Data Analysis by Matthew B. Miles; A. Michael Huberman A practical sourcebook for researchers who make use of qualitative data, presenting the current state of the craft in the design, testing, and use of qualitative analysis methods. Strong emphasis is placed on data displays matrices and networks that go beyond ordinary narrative text. Each method of data display and analysis is described and illustrated.
"A Survey of Qualitative Data Analytic Methods" in Chapter 4 (pp. 89–138) of Fundamentals of Qualitative Research by Johnny Saldana Provides an in-depth introduction to coding as a heuristic, particularly focusing on process coding, in vivo coding, descriptive coding, values coding, dramaturgical coding, and versus coding. Includes advice on writing analytic memos, developing categories, and themeing data.
"Thematic Networks: An Analytic Tool for Qualitative Research." Qualitative Research : QR, 1(3), 385–405 by Jennifer Attride-Stirling Details a technique for conducting thematic analysis of qualitative material, presenting a step-by-step guide of the analytic process, with the aid of an empirical example. The analytic method presented employs established, well-known techniques; the article proposes that thematic analyses can be usefully aided by and presented as thematic networks.
Using Thematic Analysis in Psychology by Virginia Braun and Victoria Clark Walks readers through the process of reflexive thematic analysis, step by step. The method may be adapted in fields outside of psychology as relevant. Pair this with One Size Fits All? What Counts as Quality Practice in Reflexive Thematic Analysis? by Virginia Braun and Victoria Clark

Data visualization can be employed formatively, to aid your data analysis, or summatively, to present your findings. Many qualitative data analysis (QDA) software platforms, such as NVivo , feature search functionality and data visualization options within them to aid data analysis during the formative stages of your project.

For expert assistance creating data visualizations to present your research, Harvard Library offers Visualization Support . Get help and training with data visualization design and tools—such as Tableau—for the Harvard community. Workshops and one-on-one consultations are also available.

The quality of your data analysis depends on how you situate what you learn within a wider body of knowledge. Consider the following advice:

A good literature review has many obvious virtues. It enables the investigator to define problems and assess data. It provides the concepts on which percepts depend. But the literature review has a special importance for the qualitative researcher. This consists of its ability to sharpen his or her capacity for surprise (Lazarsfeld, 1972b). The investigator who is well versed in the literature now has a set of expectations the data can defy. Counterexpectational data are conspicuous, readable, and highly provocative data. They signal the existence of unfulfilled theoretical assumptions, and these are, as Kuhn (1962) has noted, the very origins of intellectual innovation. A thorough review of the literature is, to this extent, a way to manufacture distance. It is a way to let the data of one's research project take issue with the theory of one's field.

- McCracken, G. (1988), The Long Interview, Sage: Newbury Park, CA, p. 31

Once you have coalesced around a theory, realize that a theory should reveal rather than color your discoveries. Allow your data to guide you to what's most suitable. Grounded theory researchers may develop their own theory where current theories fail to provide insight. This guide on Theoretical Models from Alfaisal University Library provides a helpful overview on using theory.

If you'd like to supplement what you learned about relevant theories through your coursework and literature review, try these sources:

Annual Reviews Review articles sum up the latest research in many fields, including social sciences, biomedicine, life sciences, and physical sciences. These are timely collections of critical reviews written by leading scientists.
HOLLIS - search for resources on theories in your field Modify this example search by entering the name of your field in place of "your discipline," then hit search.
Oxford Bibliographies Written and reviewed by academic experts, every article in this database is an authoritative guide to the current scholarship in a variety of fields, containing original commentary and annotations.
ProQuest Dissertations & Theses (PQDT) Indexes dissertations and masters' theses from most North American graduate schools as well as some European universities. Provides full text for most indexed dissertations from 1990-present.
Very Short Introductions Launched by Oxford University Press in 1995, Very Short Introductions offer concise introductions to a diverse range of subjects from Climate to Consciousness, Game Theory to Ancient Warfare, Privacy to Islamic History, Economics to Literary Theory.
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Department of Health & Social Care

Management information on recruitment to clinical research studies

Published 12 September 2024

This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] .

Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned.

This publication is available at https://www.gov.uk/government/publications/management-information-on-recruitment-to-clinical-research-studies/management-information-on-recruitment-to-clinical-research-studies

Data on the recruitment to clinical research studies, reported to the Department of Health and Social Care ( DHSC ). This release is published as management information, in accordance with the Code of Practice for Statistics , to improve transparency and support publication of the Darzi review on 12 September 2024. The Darzi review is the independent investigation of the National Health Service in England, led by Lord Darzi.

Clinical research studies are important for advancing medical knowledge and enhancing patient care. These investigations are designed to evaluate the safety, efficacy and effectiveness of new treatments, medications, devices, or interventions. Clinical research includes various types of studies, such as clinical trials and observational studies.

Clinical trials, also known as interventional studies, test new medical approaches, including drugs, vaccines and surgical procedures. They are carried out in phases to assess safety, dosing and effectiveness. Observational studies monitor and analyse the effects of specific variables on health outcomes without intervening.

By systematically investigating in this way, clinical research plays a vital role in discovering new treatments and improving healthcare practices.

Monitoring clinical research delivery

One of the ways clinical research delivery in the UK is monitored is through the UK Clinical Research Delivery Key Performance Indicators Report . This report, previously known as the Research Status Report, brings together data from the National Institute for Health and Care Research ( NIHR ) and the Medicines and Healthcare products Regulatory Agency. It monitors progress on delivering parts of the government’s vision for UK clinical research delivery. The performance indicators measure trends in:

the speed and predictability of regulatory and study set-up timelines
the delivery of research to time and target
overall recruitment levels

The report is published monthly by DHSC and is being used to support the development of policies to encourage clinical research delivery.

This publication focuses specifically on clinical research recruitment data from the UK Clinical Research Delivery Key Performance Indicators Report from April 2014 onwards. It is a supplementary management information release, showing detailed tables behind the recruitment charts published in June 2024’s UK Clinical Research Delivery Key Performance Indicators Report.

Definition of a research study

Research is defined in the UK Policy Framework for Health and Social Care Research as the attempt to derive generalisable or transferable new knowledge to answer or refine relevant questions with scientifically sound methods. This excludes: audit, needs assessments, quality improvement and other local service evaluations. It also excludes routine banking of biological samples or data except where this activity is integral to a self-contained research project designed to test a clear hypothesis.

This definition applies to all studies for which NIHR Clinical Research Network support is sought regardless of the study type or research funder.

Table 1 shows the number of participants recruited into studies since April 2014, held on the NIHR Clinical Research Network’s central portfolio management system.

The central portfolio management system consists of all studies held on the Clinical Research Network portfolio, as well as some studies held on the network portfolios of Northern Ireland, Scotland and Wales.

The data includes recruitment to both interventional and observational studies. The data represents the total number of participants recruited for a given month, based on the month and year the recruitment notification was received.

Table 1: recruitment to clinical research studies

Recruitment month and year	Number of participants
April 2014	53,127
May 2014	81,491
June 2014	60,363
July 2014	58,498
August 2014	52,321
September 2014	56,211
October 2014	62,436
November 2014	55,059
December 2014	46,203
January 2015	58,324
February 2015	63,841
March 2015	79,245
April 2015	55,841
May 2015	54,132
June 2015	63,624
July 2015	61,753
August 2015	73,360
September 2015	63,296
October 2015	61,527
November 2015	66,477
December 2015	43,906
January 2016	51,217
February 2016	54,898
March 2016	56,727
April 2016	55,838
May 2016	61,268
June 2016	70,329
July 2016	54,405
August 2016	57,022
September 2016	59,833
October 2016	56,395
November 2016	64,126
December 2016	50,328
January 2017	61,254
February 2017	60,311
March 2017	110,535
April 2017	57,058
May 2017	63,939
June 2017	74,999
July 2017	65,602
August 2017	63,852
September 2017	66,551
October 2017	74,006
November 2017	76,660
December 2017	50,561
January 2018	66,972
February 2018	77,654
March 2018	70,763
April 2018	71,401
May 2018	79,562
June 2018	101,583
July 2018	78,111
August 2018	69,173
September 2018	79,669
October 2018	102,418
November 2018	80,351
December 2018	52,956
January 2019	77,046
February 2019	85,854
March 2019	95,115
April 2019	76,893
May 2019	75,525
June 2019	69,448
July 2019	70,086
August 2019	57,268
September 2019	63,686
October 2019	81,183
November 2019	71,557
December 2019	54,106
January 2020	74,426
February 2020	71,698
March 2020	69,371
April 2020	118,865
May 2020	164,358
June 2020	235,733
July 2020	166,467
August 2020	152,595
September 2020	187,815
October 2020	193,757
November 2020	219,025
December 2020	219,465
January 2021	235,100
February 2021	198,865
March 2021	200,579
April 2021	174,143
May 2021	162,090
June 2021	171,082
July 2021	173,561
August 2021	104,394
September 2021	116,278
October 2021	86,524
November 2021	93,404
December 2021	92,956
January 2022	95,821
February 2022	91,643
March 2022	108,758
April 2022	103,638
May 2022	83,983
June 2022	94,282
July 2022	78,845
August 2022	81,659
September 2022	86,075
October 2022	89,064
November 2022	112,070
December 2022	77,792
January 2023	82,087
February 2023	80,451
March 2023	100,019
April 2023	82,115
May 2023	89,414
June 2023	104,076
July 2023	93,247
August 2023	104,386
September 2023	88,273
October 2023	94,272
November 2023	100,281
December 2023	81,339
January 2024	91,821
February 2024	94,866
March 2024	94,019

Source: NIHR Clinical Research Network, central portfolio management system

Table 2 shows the number of participants recruited into studies since April 2014, held on the NIHR Clinical Research Network’s central portfolio management system.

Table 2 includes recruitment to 3 study types:

commercial contract studies. These are studies sponsored and fully funded by the life sciences industry
commercial collaborative studies. These are studies typically funded, wholly or in part, by the life sciences industry and sponsored by a combination of life sciences industry and non-commercial organisations. This category has previously been included in non-commercial figures but it is now being presented separately to better represent the breadth of commercial studies. Commercial collaborative studies are supported in the same way as other non-commercial studies
non-commercial studies. These are studies sponsored and wholly funded by one or more non-commercial organisations, including medical research charities, universities and public funders such as NIHR and UK Research and Innovation

The data includes recruitment to both interventional and observational studies.

Table 2: recruitment to clinical research studies broken down by study type, 2014 to 2024

Recruitment month and year	Non-commercial	Commercial collaborative	Commercial contract
April 2014	43,173	5,578	4,376
May 2014	66,388	12,326	2,777
June 2014	50,265	6,927	3,171
July 2014	48,828	6,187	3,483
August 2014	43,815	5,009	3,497
September 2014	47,757	5,369	3,085
October 2014	50,973	7,761	3,702
November 2014	45,728	5,678	3,653
December 2014	38,317	5,003	2,883
January 2015	48,852	5,983	3,489
February 2015	54,665	5,675	3,501
March 2015	67,630	6,463	5,152
April 2015	46,398	5,491	3,952
May 2015	45,736	5,128	3,268
June 2015	52,262	6,359	5,003
July 2015	52,094	6,341	3,318
August 2015	65,660	4,992	2,708
September 2015	53,527	6,263	3,506
October 2015	51,673	6,508	3,346
November 2015	54,737	7,400	4,340
December 2015	35,944	5,501	2,461
January 2016	41,691	6,574	2,952
February 2016	44,330	7,153	3,415
March 2016	46,049	7,676	3,002
April 2016	46,580	6,286	2,972
May 2016	51,508	6,409	3,351
June 2016	61,073	6,254	3,002
July 2016	46,045	5,596	2,764
August 2016	48,837	5,245	2,940
September 2016	50,791	5,029	4,013
October 2016	48,241	5,313	2,841
November 2016	54,962	5,560	3,604
December 2016	43,086	4,334	2,908
January 2017	52,886	5,674	2,694
February 2017	51,752	5,208	3,351
March 2017	99,600	6,468	4,467
April 2017	48,363	4,920	3,775
May 2017	54,453	5,861	3,625
June 2017	65,652	5,645	3,702
July 2017	56,161	5,809	3,632
August 2017	53,720	6,230	3,902
September 2017	56,392	6,642	3,517
October 2017	58,803	7,150	8,053
November 2017	59,785	6,976	9,899
December 2017	41,825	4,981	3,755
January 2018	56,295	6,907	3,770
February 2018	67,389	7,026	3,239
March 2018	59,613	7,229	3,921
April 2018	58,101	9,893	3,407
May 2018	65,842	10,130	3,590
June 2018	90,507	7,226	3,850
July 2018	65,253	8,034	4,824
August 2018	56,754	7,628	4,791
September 2018	64,719	9,524	5,426
October 2018	81,222	15,107	6,089
November 2018	64,477	9,960	5,914
December 2018	43,264	6,072	3,620
January 2019	64,392	7,900	4,754
February 2019	73,602	8,027	4,225
March 2019	78,586	12,842	3,687
April 2019	65,227	8,619	3,047
May 2019	64,321	8,270	2,934
June 2019	58,436	7,384	3,628
July 2019	57,487	8,988	3,611
August 2019	46,164	8,027	3,077
September 2019	51,515	9,676	2,495
October 2019	65,035	13,081	3,067
November 2019	58,227	9,737	3,593
December 2019	43,547	8,062	2,497
January 2020	60,631	10,273	3,522
February 2020	59,870	9,134	2,694
March 2020	61,025	6,653	1,693
April 2020	115,845	2,424	596
May 2020	161,882	2,093	383
June 2020	231,322	3,031	1,380
July 2020	158,874	5,974	1,619
August 2020	146,518	4,112	1,965
September 2020	180,769	5,629	1,417
October 2020	175,345	10,031	8,381
November 2020	197,086	10,666	11,273
December 2020	205,579	9,453	4,433
January 2021	223,132	6,580	5,388
February 2021	190,285	6,573	2,007
March 2021	188,380	10,219	1,980
April 2021	159,679	12,641	1,823
May 2021	144,032	11,711	6,347
June 2021	158,606	9,939	2,537
July 2021	162,320	8,037	3,204
August 2021	93,479	8,438	2,477
September 2021	104,652	9,317	2,309
October 2021	73,910	10,407	2,207
November 2021	79,500	11,252	2,652
December 2021	84,682	6,125	2,149
January 2022	86,960	6,681	2,180
February 2022	82,369	6,405	2,869
March 2022	97,714	8,093	2,951
April 2022	91,085	8,537	4,016
May 2022	70,674	10,166	3,143
June 2022	82,982	9,150	2,150
July 2022	67,393	9,077	2,375
August 2022	68,433	10,222	3,004
September 2022	72,358	10,821	2,896
October 2022	74,041	11,959	3,064
November 2022	93,771	13,873	4,426
December 2022	65,879	8,793	3,120
January 2023	67,678	10,949	3,460
February 2023	64,479	12,045	3,927
March 2023	82,690	10,892	6,437
April 2023	69,130	8,885	4,100
May 2023	75,632	9,370	4,412
June 2023	82,690	8,525	12,861
July 2023	75,273	8,535	9,439
August 2023	82,591	9,036	12,759
September 2023	71,893	8,170	8,210
October 2023	68,843	10,785	14,644
November 2023	72,188	11,216	16,877
December 2023	49,048	7,820	24,471
January 2024	68,659	10,785	12,377
February 2024	71,830	10,266	12,770
March 2024	67,475	11,090	15,454

Methodology and quality note

Inclusion and exclusion criteria.

Recruitment is only recorded in the central portfolio management system if it meets the definitions of recruitment as outlined in the NIHR Clinical Research Network Recruitment Policy Document . For example, recruitment data is not collected for studies classified as non-consenting. These are exceptional circumstances where no form of consent can be obtained.

Only research activity with a status of confirmed and provisional is included. Research activity which is indicated as inaccurate (queried) is excluded from figures.

Confirmed status includes manually uploaded data or data from the local portfolio management system that has been confirmed as accurate by the Chief Investigator, their representative or a representative of the commercial sponsor or contract research organisation.

Provisional status is given to data from the local portfolio management system that has yet to be confirmed or that requires reconfirmation following queries.

Data source and coverage

The data has been sourced from the central portfolio management system. The central portfolio management system consists of all studies held on the Clinical Research Network portfolio, as well as some studies held on the network portfolios of Northern Ireland, Scotland and Wales.

The data is recorded monthly from April 2014 to March 2024. For the purpose of the UK Clinical Research Delivery Key Performance Indicators Report, monthly snapshots are taken according to a data cut schedule. The snapshot used in this publication was taken on 21 June 2024.

Data caveats

The data does not show recruitment to the whole clinical research system, only recruitment to studies on the central portfolio management system. Therefore, this data will be an underestimate of total clinical research recruitment in the UK.

The data is not exclusive to NHS sites and includes recruitment to non- NHS sites.

While data covers the whole of the UK, data relating to studies led by devolved administrations may be incomplete. This is because they are only included in the central portfolio management system when added by the devolved administrations.

The quality of the data is dependent on the accuracy and timeliness of recruitment data being recorded in the system.

There is often a lag between activity taking place at a study site and data being recorded in the system. This means that previous months’ data may be updated retrospectively. Changes in recruitment for individual recent months should not be taken as an indication of overall trend in recruitment.

From the 2023 to 2024 financial year onwards, the data for the commercial collaborative category will be more robust. This is due to data quality improvements linked to reporting this category separately, instead of classifying activities as purely commercial or non-commercial. Not all studies will have been reviewed retrospectively and re-classified where necessary, particularly studies that had already closed.

Using the data

The data cannot be used for:

measuring overall UK study recruitment as the data is only on studies on the central portfolio management system (not activity of the whole research environment). It is unknown what proportion of studies in the UK at that particular point in time are contributing to the figures
comparing (portfolio) recruitment over the time. The network as an organisation and its remit has changed significantly over time. Caution should be taken with time comparisons as portfolio and associated data collection may have changed
UK-wide data within the central portfolio management system across years. It has only been in recent years that UK-wide data for commercial (for example) has been collected into the central portfolio management system

The portfolio balance between observational studies and interventional ones will influence the numbers. For example, if at a particular time, the portfolio has some large sample size observational studies, this will affect recruitment numbers and make it difficult to compare to other years.

Recruitment from private sites may not be collected and so may not be included in the data.

If you have any questions in relation to these statistics, please contact [email protected] .

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ERIC is a free database that the United States Department of Education sponsors to share resources for teachers and other academic professionals. It also has a thesaurus built into the database, which individuals can use while writing their research papers. 6. ScienceDirect.
Free Research Databases from EBSCO
Library, Information Science & Technology Abstracts (LISTA) is a free research database for library and information science studies. LISTA provides indexing and abstracting for hundreds of key journals, books, research reports. It is EBSCO's intention to provide access to this resource on a continual basis. Access now.
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Find the research you need | With 160+ million publication pages, 1+ million questions, and 25+ million researchers, this is where everyone can access science
Qualitative Research Resources: Finding Qualitative Studies
Often, that means that it is hard to find qualitative studies in common health science databases like PubMed; ... Search Filter Resource is a collaborative venture to identify, assess and test search filters designed to retrieve research by study design or focus. The Search Filters Resource aims to provide easy access to published and ...
In brief: What types of studies are there?
There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked. When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following ...
Data Analysis
Each chapter author introduces the method, uses one of their own research projects as a case study of the method described, shows how the specific analytic method can be used in other types of studies, and concludes with three questions/activities to prompt class discussion or personal study. "Analyzing Qualitative Data."
Research Guides: Communication: Databases & Articles
Beginning your research in a database only moderately related to your topic will make searching difficult. Academic Search Ultimate. Multi-disciplinary database providing information for nearly every area of academic study. Includes an enormous collection of the most valuable peer-reviewed full text journals, as well as additional journals ...
Management information on recruitment to clinical research studies
The data does not show recruitment to the whole clinical research system, only recruitment to studies on the central portfolio management system. Therefore, this data will be an underestimate of ...
Accuracy of measurements on CBCT‐generated digital models using
The measurements obtained from EOS scans served as the control for the study. Normality was tested with the Shapiro-Wilk test, comparisons used the Kruskal-Wallis test with Bonferroni-adjusted pairwise comparisons for significant results, and data were analysed using IBM SPSS (Version 26.0), with significance set at p < .05. Results
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with
Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire ...
Relational Database: What It Is and Why It's Important
3. Data architect: Data architects analyse an organisation's data infrastructure to plan or implement databases and database management systems that improve workflow efficiency. 4. Data analyst: Data analysts take data sets from relational databases to clean and interpret them to solve a business question or problem. They can work in industries ...

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