Definition query clinical trial

Clinical Data Interchange Standards Consortium CDISC , a multidisciplinary non-profit organization, has developed standards to support acquisition, exchange, submission, and archival of clinical research data and metadata. Metadata is the data of the data entered. The SDTMIG standard[ 4 ] describes the details of model and standard terminologies for the data and serves as a guide to the organization. CDASH v 1. The CDM process, like a clinical trial, begins with the end in mind.

This means that the whole process is designed keeping the deliverable in view. As a clinical trial is designed to answer the research question, the CDM process is designed to deliver an error-free, valid, and statistically sound database. To meet this objective, the CDM process starts early, even before the finalization of the study protocol. The protocol is reviewed from a database designing perspective, for clarity and consistency. During this review, the CDM personnel will identify the data items to be collected and the frequency of collection with respect to the visit schedule.

The data fields should be clearly defined and be consistent throughout. The type of data to be entered should be evident from the CRF. For example, if weight has to be captured in two decimal places, the data entry field should have two data boxes placed after the decimal as shown in Figure 1.

Similarly, the units in which measurements have to be made should also be mentioned next to the data field. Along with the CRF, the filling instructions called CRF Completion Guidelines should also be provided to study investigators for error-free data acquisition.

Annotations are coded terms used in CDM tools to indicate the variables in the study. An example of an annotated CRF is provided in Figure 1. In questions with discrete value options like the variable gender having values male and female as responses , all possible options will be coded appropriately.

Annotations are entered in coloured text in this figure to differentiate from the CRF questions. DMP document is a road map to handle the data under foreseeable circumstances and describes the CDM activities to be followed in the trial.

A list of CDM activities is provided in Table 1. The edit check programs in the DVP help in cleaning up the data by identifying the discrepancies. Databases are the clinical software applications, which are built to facilitate the CDM tasks to carry out multiple studies.

Study details like objectives, intervals, visits, investigators, sites, and patients are defined in the database and CRF layouts are designed for data entry. These entry screens are tested with dummy data before moving them to the real data capture.

Data collection is done using the CRF that may exist in the form of a paper or an electronic version. The traditional method is to employ paper CRFs to collect the data responses, which are translated to the database by means of data entry done in-house. These paper CRFs are filled up by the investigator according to the completion guidelines. In e-CRF method, chances of errors are less, and the resolution of discrepancies happens faster.

Since pharmaceutical companies try to reduce the time taken for drug development processes by enhancing the speed of processes involved, many pharmaceutical companies are opting for e-CRF options also called remote data entry. CRFs are tracked for missing pages and illegible data manually to assure that the data are not lost.

In case of missing or illegible data, a clarification is obtained from the investigator and the issue is resolved. Data entry takes place according to the guidelines prepared along with the DMP. This is applicable only in the case of paper CRF retrieved from the sites. Usually, double data entry is performed wherein the data is entered by two operators separately. Moreover, double data entry helps in getting a cleaner database compared to a single data entry.

Earlier studies have shown that double data entry ensures better consistency with paper CRF as denoted by a lesser error rate. Data validation is the process of testing the validity of data in accordance with the protocol specifications.

Edit check programs are written to identify the discrepancies in the entered data, which are embedded in the database, to ensure data validity. These programs are written according to the logic condition mentioned in the DVP. These edit check programs are initially tested with dummy data containing discrepancies. Discrepancy is defined as a data point that fails to pass a validation check. Discrepancy may be due to inconsistent data, missing data, range checks, and deviations from the protocol.

In e-CRF based studies, data validation process will be run frequently for identifying discrepancies. These discrepancies will be resolved by investigators after logging into the system. Ongoing quality control of data processing is undertaken at regular intervals during the course of CDM. For example, if the inclusion criteria specify that the age of the patient should be between 18 and 65 years both inclusive , an edit program will be written for two conditions viz.

If for any patient, the condition becomes TRUE, a discrepancy will be generated. DCFs are documents containing queries pertaining to the discrepancies identified.

This is also called query resolution. Discrepancy management includes reviewing discrepancies, investigating the reason, and resolving them with documentary proof or declaring them as irresolvable.

Discrepancy management helps in cleaning the data and gathers enough evidence for the deviations observed in data. This log helps maintain an inventory of the study device. Click here for a sample of a Device Accountability Log. Drug Accountability Log: Investigators are responsible for maintaining strict control over investigational drugs to ensure that the device is used only for subjects enrolled in the study.

Click here for a sample of a Drug Accountability Log. Specimen Log: Safety laboratory assessments are usually part of most clinical trials that involve an investigational test article. Biological materials might be sent to the local laboratory or to a central laboratory.

In any case, it is important to keep track of the samples that are collected from study subjects. Click here for a sample of a Specimen Log. Quality Management Plan: Ideally, every study should have a monitoring plan as part of the quality management plan. Quality Management Plans include all the activities undertaken to verify that the requirements for quality of the trial related activities have been fulfilled.

Some of the logs and checklists that might help with the quality management plan are see also this page :. Study Management Pre Study Protocol Template: A protocol is a document that describes the background, rationale, objectives, design, methodology, statistical considerations, and organization of a trial.

Budget Development : Budget development involves three components: identifying the cost of all research items and services required for the study assigning financial responsibility for all items and services; and maintaining a process for recovering costs throughout the study.

Study Initiation Regulatory Binder : The regulatory binder contains all the important study documents. Subject Management Master Subject Log : Most clinical trials involve multiple study visits over an extended period of time.

Tracking and Reporting Adverse Event Tracking Log: At every study visit, the subject should be observed for adverse events and should be asked about any adverse event that might have happened since the previous visit.

Accountability Logs Device Accountability Log: Investigators are responsible for maintaining strict control over investigational devices to ensure that the device is used only for subjects enrolled in the study. Figure 2. Entity normalization Generating standard concept sets that accurately represent biomedical concepts in free-text is a fundamental but challenging component in query formulation. Figure 3. Logic translation The query formulation module takes the concepts and relations produced by the information extraction pipeline, represents them using the concept sets generated in Entity normalization, and formulates query logic using the template introduced in OMOP cohort definition.

Attribute normalization temporal and numeric Temporal normalization unifies all temporal expressions to the same unit days. Evaluation methods Evaluation on a random sample of criteria from ClinicalTrials. User-centered evaluation method We conducted an evaluation of Criteria2Query at the OHDSI annual fall symposium and collected anonymous user feedback and criteria entries from attendees willing to try the demo of Criteria2Query. User interface and availability of Criteria2Query Criteria2Query is deployed as a web-based natural language cohort definition system based on the Spring MVC framework.

Figure 4. Figure 5. Figure 6. Evaluation results Evaluation results for a random sample of criteria from ClinicalTrials. Table 3. Evaluation Matrix Criteria crawled from Clinical Trials. Open source, flexibility, and extensibility Criteria2Query is open source, modular, and follows loose coupling design. Error analysis The information extraction errors can be attributed to suboptimal entity recognition and relation extraction.

Limitations and future work As the initial NLI to clinical databases for cohort definition, Criteria2Query has several limitations. Supplementary Data Click here for additional data file. Conflict of interest statement. Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inf ; 77 : — Automated matching software for clinical trials eligibility: measuring efficiency and flexibility. Contemp Clin Trials ; 31 3 : — Electronic screening improves efficiency in clinical trial recruitment.

J Am Med Inform Assoc ; 16 6 : — Effort required in eligibility screening for clinical trials. J Oncol Pract ; 8 6 : — Knowledge engineering for a clinical trial advice system: uncovering errors in protocol specification. Bull Cancer ; 74 : —6. Weng C. Optimizing clinical research participant selection with informatics. Trends Pharmacol Sci ; 36 11 : —9.

EliIE: an open-source information extraction system for clinical trial eligibility criteria. J Am Med Inform Assoc ; 24 6 : — Friedman CP. Formal representation of eligibility criteria: a literature review. J Biomed Inform ; 43 3 : — ERGO: a template-based expression language for encoding eligibility criteria. Accessed April 7, A practical method for transforming free-text eligibility criteria into computable criteria.

J Biomed Inform ; 44 2 : — Bhattacharya S, Cantor MN.. Analysis of eligibility criteria representation in industry-standard clinical trial protocols. J Biomed Inform ; 46 5 : — EliXR: an approach to eligibility criteria extraction and representation.

Valx: a system for extracting and structuring numeric lab test comparison statements from text. Methods Inf Med ; 55 3 : — Parker CG. Generating medical logic modules for clinical trial eligibility. PhD thesis, Brigham Young University; Accessed March 25, Stud Health Technol Inform ; : —8.

Natural language interfaces to databases—an introduction. Nat Lang Eng ; 1 1 : 29— Copestake A, Jones KS.. Natural language interfaces to databases. Knowl Eng Rev ; 5 4 : — Woods WA. Progress in natural language understanding: an application to lunar geology. Natural language access to a melanoma data base.

Chandra Y, Mihalcea R.. Master of Science thesis, University of North Texas, Recent Advances in Hybrid Intelligence Systems. New York: Springer; ; — Woodyard M, Hamel B.. A natural language interface to a clinical data base management system. Comput Biomed Res ; 14 1 : 41— Roberts K, Demner-Fushman D.. Toward a natural language interface for EHR questions. Loose coupling. Accessed May 14, Accessed July 18, A simple algorithm for identifying negated findings and diseases in discharge summaries.

J Biomed Inform ; 34 5 : — Schuster S, Manning CD. Enhanced English universal dependencies: an improved representation for natural language understanding tasks. Accessed March 26, Bodenreider O. Nucleic Acids Res ; 32 : D— Accessed July 5, Dogan RI, Lu Z.

An inference method for disease name normalization. J Biomed Inform ; 45 4 : — Full text links Read article at publisher's site DOI : Smart citations by scite. The number of the statements may be higher than the number of citations provided by EuropePMC if one paper cites another multiple times or lower if scite has not yet processed some of the citing articles.

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EliIE: An open-source information extraction system for clinical trial eligibility criteria. A natural language interface plug-in for cooperative query answering in biological databases. A natural language user interface for fuzzy scope queries. A knowledge base of clinical trial eligibility criteria. LinkHub: a Semantic Web system that facilitates cross-database queries and information retrieval in proteomics. Funding Funders who supported this work.

Joining Europe PMC. Tools Tools overview. Correctly identifying whether a study is considered by NIH to be a clinical trial is crucial to how you will:. In , NIH launched a multi-faceted effort to enhance its stewardship over clinical trials.

The goal of this effort is to encourage advances in the design, conduct, and oversight of clinical trials while elevating the entire biomedical research enterprise to a new level of transparency and accountability.

The NIH definition of a clinical trial was revised in in anticipation of these stewardship reforms to ensure a clear and responsive definition of a clinical trial. Learn more about why NIH has made changes to improve clinical trial stewardship. A research study in which one or more human subjects are prospectively assigned prospectively assigned The term "prospectively assigned" refers to a pre-defined process e. Examples include: positive or negative changes to physiological or biological parameters e.

Your human subjects study may meet the NIH definition of a clinical trial.