This statistic shows the percentage of clinical studies with posted results worldwide by type, as of June 13, 2025. Some 94 percent of studies with posted results were interventional types.

As of June 13, 2025, interventional types of studies made up 76 percent of the total number of registered clinical studies. This statistic shows the percentage of registered clinical studies worldwide by type.

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
   Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

Technical notes and documentation on the common data model of the project CONCEPT-DM2.

This publication corresponds to the Common Data Model (CDM) specification of the CONCEPT-DM2 project for the implementation of a federated network analysis of the healthcare pathway of type 2 diabetes.

Aims of the CONCEPT-DM2 project:

General aim: To analyse chronic care effectiveness and efficiency of care pathways in diabetes, assuming the relevance of care pathways as independent factors of health outcomes using data from real life world (RWD) from five Spanish Regional Health Systems.

Main specific aims:

• To characterize the care pathways in patients with diabetes through the whole care system in terms of process indicators and pharmacologic recommendations
• To compare these observed care pathways with the theoretical clinical pathways derived from the clinical practice guidelines
• To assess if the adherence to clinical guidelines influence on important health outcomes, such as cardiovascular hospitalizations.
• To compare the traditional analytical methods with process mining methods in terms of modeling quality, prediction performance and information provided.

Study Design: It is a population-based retrospective observational study centered on all T2D patients diagnosed in five Regional Health Services within the Spanish National Health Service. We will include all the contacts of these patients with the health services using the electronic medical record systems including Primary Care data, Specialized Care data, Hospitalizations, Urgent Care data, Pharmacy Claims, and also other registers such as the mortality and the population register.

Cohort definition: All patients with code of Type 2 Diabetes in the clinical health records

• Inclusion criteria: patients that, at 01/01/2017 or during the follow-up from 01/01/2017 to 31/12/2022 had active health card (active TIS - tarjeta sanitaria activa) and code of type 2 diabetes (T2D, DM2 in spanish) in the clinical records of primary care (CIAP2 T90 in case of using CIAP code system)
• Exclusion criteria:
  • patients with no contact with the health system from 01/01/2017 to 31/12/2022
  • patients that had a T1D (DM1) code opened after the T2D code during the follow-up.
• Study period. From 01/01/2017 to 31/12/2022

Files included in this publication:

• Datamodel_CONCEPT_DM2_diagram.png
• Common data model specification (Datamodel_CONCEPT_DM2_v.0.1.0.xlsx)
• Synthetic datasets (Datamodel_CONCEPT_DM2_sample_data)
  • sample_data1_dm_patient.csv
  • sample_data2_dm_param.csv
  • sample_data3_dm_patient.csv
  • sample_data4_dm_param.csv
  • sample_data5_dm_patient.csv
  • sample_data6_dm_param.csv
  • sample_data7_dm_param.csv
  • sample_data8_dm_param.csv
• Datamodel_CONCEPT_DM2_explanation.pptx

This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Clinical Attribute". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.

This dataset was created for an Eli Lilly and Company employee information management training program. It was part of a project that explored the potential use of ClinicalTrials.gov (CT.gov) as a tool to evaluate the severity of changes to inclusion and exclusion criteria on clinical trial operations. CT.gov is a public clinical study registry that records summary data about clinical trials. The registry includes a historical record of changes (change history) to inclusion and exclusion criteria and other data that is accessible by users.

Objective: Normalizing mentions of medical concepts to standardized vocabularies is a fundamental component of clinical text analysis. Ambiguity—words or phrases that may refer to different concepts—has been extensively researched as part of information extraction from biomedical literature, but less is known about the types and frequency of ambiguity in clinical text. This study characterizes the distribution and distinct types of ambiguity exhibited by benchmark clinical concept normalization datasets, in order to identify directions for advancing medical concept normalization research.

Materials and Methods: We identified ambiguous strings in datasets derived from the two available clinical corpora for concept normalization, and categorized the distinct types of ambiguity they exhibited. We then compared observed string ambiguity in the datasets to potential ambiguity in the Unified Medical Language System (UMLS), to assess how representative available datasets are of ambiguity in clinical language.

Results: We observed twelve distinct types of ambiguity, distributed unequally across the available datasets. However, less than 15% of the strings were ambiguous within the datasets, while over 50% were ambiguous in the UMLS, indicating only partial coverage of clinical ambiguity.

Discussion: Existing datasets are not sufficient to cover the diversity of clinical concept ambiguity, limiting both training and evaluation of normalization methods for clinical text. Additionally, the UMLS offers important semantic information for building and evaluating normalization methods.

Conclusion: Our findings identify three opportunities for concept normalization research, including a need for ambiguity-specific clinical datasets and leveraging the rich semantics of the UMLS in new methods and evaluation measures for normalization.

Methods These data are derived from benchmark datasets released for Medical Concept Normalization research focused on Electronic Health Record (EHR) narratives. Data included in this release are derived from:

SemEval-2015 Task 14 (Publication DOI: 10.18653/v1/S15-2051, data accessed through release at https://physionet.org/content/shareclefehealth2014task2/1.0/)
CUILESS2016 (Publication DOI: 10.1186/s13326-017-0173-6, data accessed through release at https://physionet.org/content/cuiless16/1.0.0/)

These datasets consist of EHR narratives with annotations including: (1) the portion of a narrative referring to a medical concept, such as a problem, treatment, or test; and (2) one or more Concept Unique Identifiers (CUIs) derived from the Unified Medical Language System (UMLS), identifying the reification of the medical concept being mentioned.

The data were processed using the following procedure:

All medical concept mention strings were preprocessed with lowercasing and removing of determiners ("a", "an", "the").
All medical concept mentions were analyzed to identify strings that met the following conditions: (1) string occurred more than once in the dataset, and (2) string was annotated with at least two different CUIs, when aggregating across dataset samples. Strings meeting these conditions were considered "ambiguous strings".
Ambiguous strings were reviewed by article authors to determine (1) the category and subcategory of ambiguity exhibited (derived from an ambiguity typology described in the accompanying article); and (2) whether the semantic differences in CUI annotations were reflected by differences in textual meaning (strings not meeting this criterion were termed "arbitrary").

For more details, please see the accompanying article (DOI: 10.1093/jamia/ocaa269).

Introduction: Type 2 diabetes (T2D) is a multifactorial complex chronic disease with a high prevalence worldwide, and Type 2 diabetes patients with different comorbidities often present multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of the clinical Type 2 diabetes population to help identify more accurate disease subtypes for personalized treatment.Methods: Here, utilizing the traditional Chinese medicine (TCM) clinical electronic medical records (EMRs) of 2137 Type 2 diabetes inpatients, we followed a heterogeneous medical record network (HEMnet) framework to construct heterogeneous medical record networks by integrating the clinical features from the electronic medical records, molecular interaction networks and domain knowledge.Results: Of the 2137 Type 2 diabetes patients, 1347 were male (63.03%), and 790 were female (36.97%). Using the HEMnet method, we obtained eight non-overlapping patient subgroups. For example, in H3, Poria, Astragali Radix, Glycyrrhizae Radix et Rhizoma, Cinnamomi Ramulus, and Liriopes Radix were identified as significant botanical drugs. Cardiovascular diseases (CVDs) were found to be significant comorbidities. Furthermore, enrichment analysis showed that there were six overlapping pathways and eight overlapping Gene Ontology terms among the herbs, comorbidities, and Type 2 diabetes in H3.Discussion: Our results demonstrate that identification of the Type 2 diabetes subgroup based on the HEMnet method can provide important guidance for the clinical use of herbal prescriptions and that this method can be used for other complex diseases.

BackgroundElderly patients represent the greatest consumers of healthcare per capita but have historically been underrepresented in clinical trials. It is unknown how many trials are designed to focus exclusively on elderly patients.ObjectiveTo define the prevalence of interventional trials that study exclusively elderly persons and describe the characteristics of these trials, including their distribution across conditions most prevalent in the elderly.DesignAll interventional clinical trials enrolling exclusively elderly patients (≥65 years), conducted primarily in high-income countries, and initiated between 2006 and 2014, identified through ClincialTrials.gov.Main MeasuresTrials were identified and characterized according to design features and disease categories studied. Across disease categories we examined the burden of disease in the elderly in high-income countries (measured in disability-adjusted life years [DALYs]) and compared to the number of trials conducted exclusively in the elderly.ResultsAmong 80,965 interventional trials, 1,112 (1.4%) focused on elderly patients. Diverse types of interventions were studied in these trials (medications 33%, behavioral interventions 18%, and dietary supplements 10%) and the majority was funded by non-profit organizations (81%). Studies tended to be small (median sample size 122 participants [IQR 58, 305]), single-center studies (67%). Only 43% of 126 disease categories affecting elderly persons were studied in trials focused on the elderly. Among these disease categories, there was a 5162-fold range in the ratio of DALYs per trial. Across 5 conditions where over 80% of DALYs are in the elderly, there were a total of only 117 trials done exclusively in the elderly.ConclusionsVery few and mostly small studies are conducted exclusively in elderly persons, even for conditions that affect almost exclusively the elderly.

Dataset is a JSON file containing the Intents Information: 1. Greetings 2. Introduction to the Diseases 3. Types of Diseases 4. Symptoms of Disease 5. Prevention of Disease

aThis table summarizes data presented in Tables 4 and 5 and Text S1's table 1. Along the top row in this table, we show every type of efficacy analysis described in the protocols, SAPs, and publications across all nine trials for which we compared these documents. The first column on the left lists the criteria used to define the types of analysis across all studies. For each type of analysis, an “X” denotes that the criterion was applied in at least one of the documents for any of the nine trials we examined. For example, the second column summarizes the five criteria used across all documents and trials to define ITT: in Table 4, four criteria were used in different combinations to define ITT analysis; in Text S1's table 1, one additional criterion was used in the SAP.bThis type of analysis was specified protocols, SAPs, and publications for the trials we examined (Tables 4 and 5 and Text S1's table 1).cThis type of analysis was specified only in the protocol and publications for some of the trials we examined (see Table 5).dThis type of analysis specified only in SAPs for some of the trials we examined (see Text S1's table 1).CGIS, clinical global impression of severity; HAM-D, Hamilton Depression Rating Scale; YMRS, Young Mania Rating Scale.

The Clinical Data Analytics market is projected to be valued at $5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12%, reaching approximately $15 billion by 2034.

Snapshot img

Medical Writing Market Size 2024-2028

The medical writing market size is forecast to increase by USD 1.18 billion, at a CAGR of 6.45% between 2023 and 2028.

The market growth depends on key drivers such as the increase in the number of clinical trials. The medical writing market plays a crucial role in scientific data analysis, regulatory submissions, and the creation of educational materials. As the healthcare industry invests heavily in evidence-based medicine, skilled medical writers are in demand to communicate complex scientific information effectively. A significant trend shaping the market is the increasing adoption of AI in medical writing, which enhances efficiency and accuracy in document creation. However, a key challenge affecting the market growth is data security and privacy concerns associated with medical writing, especially when handling sensitive patient and clinical trial information.

What will be the Size of the Market During the Forecast Period?

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The market encompasses various sectors, including patient information leaflets, scientific manuscripts, educational materials, regulatory writing, clinical writing, and medical writing sessions. These materials are essential for physicians and healthcare professionals to effectively communicate complex medical information to patients and peers. The market is significantly influenced by advancements in genetic engineering and bioinformatics, which require precise and accurate documentation. Clinical data management is another critical area that relies on medical writing for the collection, analysis, and reporting of clinical trial data. The market for medical writing continues to grow as the demand for clear and concise communication in the medical field increases.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Clinical writing
  Regulatory writing
  Others


End-user

  Pharmaceutical
  biotech companies
  Contract research organization
  others


Geography

  North America

    US


  Asia

    China
    India


  Europe

    Germany
    UK


  Rest of World (ROW)

By Type Insights

The clinical writing segment is estimated to witness significant growth during the forecast period.

Clinical writing refers to the type of writing that healthcare professionals engage in regularly. Examples of clinical writing include documenting progress or treatment notes in medical records, updating patient charts, preparing referral and consultation letters, and completing various administrative forms. This form of writing communicates essential, accurate, and detailed information regarding a patient's condition, diagnostic tests, treatment plans, and prognosis. Unlike other forms of medical writing, clinical writing directly affects patient care. Additionally, it carries legal implications and may be used as evidence in malpractice or negligence lawsuits.

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The clinical writing segment was valued at USD 1.48 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 36% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

The market is thriving due to the region's emphasis on evidence-based medicine and the substantial healthcare expenditure. With the increasing prevalence of diseases worldwide, there is a growing demand for high-quality scientific data and patient information leaflets. This need is met through the production of scientific manuscripts, educational materials, and regulatory submissions. Skilled medical writers play a crucial role in transforming complex scientific research into clear and concise language for various audiences, including physicians, patients, and regulatory bodies. The market encompasses a wide range of applications, including research articles, conference papers, and documentation for drug-related information, medical device regulations, and study protocols.

Moreover, advancements in medical technologies, such as genetic engineering, bioinformatics, and agriculture biotechnology, necessitate the need for comprehensive clinical data management and medical writing sessions. The internship forum provides opportunities for aspiring medical writers to gain valuable experience and contribute to the development of medication innovations and medical apparatus regulations. The in

This fileset provides supporting data and corpora for the empirical study described in: Laura Miron, Rafael S. Goncalves and Mark A. Musen. Obstacles to the Reuse of Metadata in ClinicalTrials.govDescription of filesOriginal data files:- AllPublicXml.zip contains the set of all public XML records in ClinicalTrials.gov (protocols and summary results information), on which all remaining analyses are based. Set contains 302,091 records downloaded on April 3, 2019.- public.xsd is the XML schema downloaded from ClinicalTrials.gov on April 3, 2019, used to validate records in AllPublicXML.BioPortal API Query Results- condition_matches.csv contains the results of querying the BioPortal API for all ontology terms that are an 'exact match' to each condition string scraped from the ClinicalTrials.gov XML. Columns={filename, condition, url, bioportal term, cuis, tuis}. - intervention_matches.csv contains BioPortal API query results for all interventions scraped from the ClinicalTrials.gov XML. Columns={filename, intervention, url, bioportal term, cuis, tuis}.Data Element Definitions- supplementary_table_1.xlsx Mapping of element names, element types, and whether elements are required in ClinicalTrials.gov data dictionaries, the ClinicalTrials.gov XML schema declaration for records (public.XSD), the Protocol Registration System (PRS), FDAAA801, and the WHO required data elements for clinical trial registrations.Column and value definitions: - CT.gov Data Dictionary Section: Section heading for a group of data elements in the ClinicalTrials.gov data dictionary (https://prsinfo.clinicaltrials.gov/definitions.html) - CT.gov Data Dictionary Element Name: Name of an element/field according to the ClinicalTrials.gov data dictionaries (https://prsinfo.clinicaltrials.gov/definitions.html) and (https://prsinfo.clinicaltrials.gov/expanded_access_definitions.html) - CT.gov Data Dictionary Element Type: "Data" if the element is a field for which the user provides a value, "Group Heading" if the element is a group heading for several sub-fields, but is not in itself associated with a user-provided value. - Required for CT.gov for Interventional Records: "Required" if the element is required for interventional records according to the data dictionary, "CR" if the element is conditionally required, "Jan 2017" if the element is required for studies starting on or after January 18, 2017, the effective date of the FDAAA801 Final Rule, "-" indicates if this element is not applicable to interventional records (only observational or expanded access) - Required for CT.gov for Observational Records: "Required" if the element is required for interventional records according to the data dictionary, "CR" if the element is conditionally required, "Jan 2017" if the element is required for studies starting on or after January 18, 2017, the effective date of the FDAAA801 Final Rule, "-" indicates if this element is not applicable to observational records (only interventional or expanded access) - Required in CT.gov for Expanded Access Records?: "Required" if the element is required for interventional records according to the data dictionary, "CR" if the element is conditionally required, "Jan 2017" if the element is required for studies starting on or after January 18, 2017, the effective date of the FDAAA801 Final Rule, "-" indicates if this element is not applicable to expanded access records (only interventional or observational) - CT.gov XSD Element Definition: abbreviated xpath to the corresponding element in the ClinicalTrials.gov XSD (public.XSD). The full xpath includes 'clinical_study/' as a prefix to every element. (There is a single top-level element called "clinical_study" for all other elements.) - Required in XSD? : "Yes" if the element is required according to public.XSD, "No" if the element is optional, "-" if the element is not made public or included in the XSD - Type in XSD: "text" if the XSD type was "xs:string" or "textblock", name of enum given if type was enum, "integer" if type was "xs:integer" or "xs:integer" extended with the "type" attribute, "struct" if the type was a struct defined in the XSD - PRS Element Name: Name of the corresponding entry field in the PRS system - PRS Entry Type: Entry type in the PRS system. This column contains some free text explanations/observations - FDAAA801 Final Rule FIeld Name: Name of the corresponding required field in the FDAAA801 Final Rule (https://www.federalregister.gov/documents/2016/09/21/2016-22129/clinical-trials-registration-and-results-information-submission). This column contains many empty values where elements in ClinicalTrials.gov do not correspond to a field required by the FDA - WHO Field Name: Name of the corresponding field required by the WHO Trial Registration Data Set (v 1.3.1) (https://prsinfo.clinicaltrials.gov/trainTrainer/WHO-ICMJE-ClinTrialsgov-Cross-Ref.pdf)Analytical Results:- EC_human_review.csv contains the results of a manual review of random sample eligibility criteria from 400 CT.gov records. Table gives filename, criteria, and whether manual review determined the criteria to contain criteria for "multiple subgroups" of participants.- completeness.xlsx contains counts and percentages of interventional records missing fields required by FDAAA801 and its Final Rule.- industry_completeness.xlsx contains percentages of interventional records missing required fields, broken up by agency class of trial's lead sponsor ("NIH", "US Fed", "Industry", or "Other"), and before and after the effective date of the Final Rule- location_completeness.xlsx contains percentages of interventional records missing required fields, broken up by whether record listed at least one location in the United States and records with only international location (excluding trials with no listed location), and before and after the effective date of the Final RuleIntermediate Results:- cache.zip contains pickle and csv files of pandas dataframes with values scraped from the XML records in AllPublicXML. Downloading these files greatly speeds up running analysis steps from jupyter notebooks in our github repository.

aThe table does not include an additional type of analysis (“Efficacy evaluable”) specified in the SAP for study 945-220. See Text S1 for details.bTo be eligible for inclusion in a type of analysis, participants had to meet the criteria that are checked. For example, per the description of modified ITT in the publication for study 945-220 [23], data from participants who were randomized into the trial and met criteria related to completing treatment at a minimum dose and/or for a minimum duration, and availability of measurements for the outcome variable at baseline and during follow-up, were included in the modified ITT analysis (“This population included any patient who was randomized, took at least one dose of study medication during SP2 [Stabilization Period 2], maintained a stable dose of 2400 mg/day during SP2, had baseline migraine headache data and at least 1 day of migraine headache evaluations during SP2.” [23]).

Health Canada is issuing this guidance for clinical trials in type 2 diabetes to provide clarification on the interpretation of the Canadian Diabetes Association Clinical Practice Guidelines (CDA-CPG)Footnote1 in relation to clinical trial applications under Part C, Division 5 of the Food and Drug Regulations.

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