Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Five questions that need answering when considering the design of clinical trials.

ABSTRACT: Evidence suggests that research protocols often lack important information on study design, which hinders external review. The study protocol should provide an adequate explanation for why the proposed study methodology is appropriate for the question posed, why the study design is likely to answer the research question, and why it is the best approach. It is especially important that researchers explain why the treatment difference sought is worthwhile to patients, and they should reference consultations with the public and patient groups and existing literature. Moreover, the study design should be underpinned by a systematic review of the existing evidence, which should be included in the research protocol. The Health Research Authority in collaboration with partners has published guidance entitled 'Specific questions that need answering when considering the design of clinical trials'. The guidance will help those designing research and those reviewing it to address key issues.

SUBMITTER: Clark T

PROVIDER: S-EPMC4223595 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Five questions that need answering when considering the design of clinical trials.

Clark Timothy T Davies Hugh H Mansmann Ulrich U

Trials 20140716

Evidence suggests that research protocols often lack important information on study design, which hinders external review. The study protocol should provide an adequate explanation for why the proposed study methodology is appropriate for the question posed, why the study design is likely to answer the research question, and why it is the best approach. It is especially important that researchers explain why the treatment difference sought is worthwhile to patients, and they should reference con ...[more]

PMID: 25027292

Similar Datasets

Answering physicians' clinical questions: obstacles and potential solutions.

Project description:ObjectiveTo identify the most frequent obstacles preventing physicians from answering their patient-care questions and the most requested improvements to clinical information resources.DesignQualitative analysis of questions asked by 48 randomly selected generalist physicians during ambulatory care.MeasurementsFrequency of reported obstacles to answering patient-care questions and recommendations from physicians for improving clinical information resources.ResultsThe physicians asked 1,062 questions but pursued answers to only 585 (55%). The most commonly reported obstacle to the pursuit of an answer was the physician's doubt that an answer existed (52 questions, 11%). Among pursued questions, the most common obstacle was the failure of the selected resource to provide an answer (153 questions, 26%). During audiotaped interviews, physicians made 80 recommendations for improving clinical information resources. For example, they requested comprehensive resources that answer questions likely to occur in practice with emphasis on treatment and bottom-line advice. They asked for help in locating information quickly by using lists, tables, bolded subheadings, and algorithms and by avoiding lengthy, uninterrupted prose.ConclusionPhysicians do not seek answers to many of their questions, often suspecting a lack of usable information. When they do seek answers, they often cannot find the information they need. Clinical resource developers could use the recommendations made by practicing physicians to provide resources that are more useful for answering clinical questions.

| S-EPMC551553 | biostudies-literature

Answering the Big Clinical Questions in Brain Metastasis Management.

Project description:Management of brain metastases is challenging, both because of the historically guarded prognosis and evolving, more efficacious treatment paradigms for metastatic cancer. This perspective addresses several of the important difficult questions that practitioners treating patients with brain tumors face in the clinic. Successfully answering these questions requires knowledge of the clinical evidence, thoughtful discussion of the patient's goals of care and collaboration in a multi-disciplinary setting.

| S-EPMC8805701 | biostudies-literature

Accuracy of ChatGPT3.5 in answering clinical questions on guidelines for severe acute pancreatitis.

Project description:BackgroundGuidelines must be interpreted comprehensively and correctly to standardize the clinical process. However, this process is challenging and requires interpreters to have a medical background and qualifications. In this study, the accuracy of ChatGPT3.5 in answering clinical questions related to the 2019 guidelines for severe acute pancreatitis was evaluated.Methods and resultsAn observational study was conducted using the 2019 guidelines for severe acute pancreatitis. The study compared the accuracy of ChatGPT3.5 in English versus Chinese and found that it was more accurate in English (71%) than in Chinese (59%) (P value: 0.203). Additionally, the study assessed the accuracy of ChatGPT3.5 in answering short-answer questions versus true/false questions and found that it was more accurate in answering short-answer questions (76%) than in answering true/false questions (60%) (P value: 0.405).ConclusionsFor clinicians managing severe acute pancreatitis, ChatGPT3.5 may have potential value. However, it should not be relied upon excessively for clinical decision making.

| S-EPMC11318168 | biostudies-literature

Answering questions about electronic cigarettes using a multidisciplinary model.

Project description:Electronic cigarettes (ECIGs) are a relatively new class of tobacco products and a subject of much debate for scientists and policymakers worldwide. Objective data that address the ECIG risk-benefit ratio for individual and public health are needed, and addressing this need requires a multidisciplinary approach that spans several areas of psychology as well as chemistry, toxicant inhalation, and physiology. This multidisciplinary approach would benefit from methods that are reliable, valid, and swift. For this reason, we formed a multidisciplinary team to develop methods that could answer questions about ECIGs and other potential modified risk tobacco products. Our team includes scientists with expertise in psychology (clinical, community, and experimental) and other disciplines, including aerosol research, analytical chemistry, biostatistics, engineering, internal medicine, and public health. The psychologists on our team keep other members focused on factors that influence individual behavior, and other team members keep the psychologists aware of other issues, such as product design. Critically, all team members are willing to extend their interests beyond the boundaries of their discipline to collaborate effectively with the shared goal of producing the rigorous science needed to inform empirically based tobacco policy. In addition, our trainees gain valuable knowledge from these collaborations and learn that other disciplines are accessible, exciting, and can enhance their own research. Multidisciplinary work presents challenges: learning other scientists' languages and staying focused on our core mission. Overall, our multidisciplinary team has led to several major findings that inform the scientific, regulatory, and public health communities about ECIGs and their effects. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

| S-EPMC6450402 | biostudies-literature

Supportive care of patients with fibrosing interstitial lung disease: answering a great clinical need.

Project description:Fibrosing interstitial lung disease (F-ILD) significantly reduces quality of life. F-ILD care includes symptom management, end-of-life planning and supportive measures, as well as antifibrotics. Patients and carers should be central to all care decisions. https://bit.ly/2ZAE2Ks.

| S-EPMC7792811 | biostudies-literature

Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?

Project description:Background: The guidelines recommend intensive blood pressure control. Randomized trials have focused on the relevance of the systolic blood pressure (SBP) lowering, leaving the safety of the diastolic blood pressure (DBP) reduction unresolved. There are data available which show that low DBP should not stop clinicians from achieving SBP targets; however, registries and analyses of randomized trials present conflicting results. The purpose of the study was to apply machine learning (ML) algorithms to determine, whether DBP is an important risk factor to predict stroke, heart failure (HF), myocardial infarction (MI), and primary outcome in the SPRINT trial database. Methods: ML experiments were performed using decision tree, random forest, k-nearest neighbor, naive Bayesian, multi-layer perceptron, and logistic regression algorithms, including and excluding DBP as the risk factor in an unselected and selected (DBP < 70 mmHg) study population. Results: Including DBP as the risk factor did not change the performance of the machine learning models evaluated using accuracy, AUC, mean, and weighted F-measure, and was not required to make proper predictions of stroke, MI, HF, and primary outcome. Conclusions: Analyses of the SPRINT trial data using ML algorithms imply that DBP should not be treated as an independent risk factor when intensifying blood pressure control.

| S-EPMC9785044 | biostudies-literature

A compendium answering 150 questions on COVID-19 and SARS-CoV-2.

Project description:In December 2019, China reported the first cases of the coronavirus disease 2019 (COVID-19). This disease, caused by the severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2), has developed into a pandemic. To date, it has resulted in ~9 million confirmed cases and caused almost 500 000 related deaths worldwide. Unequivocally, the COVID-19 pandemic is the gravest health and socioeconomic crisis of our time. In this context, numerous questions have emerged in demand of basic scientific information and evidence-based medical advice on SARS-CoV-2 and COVID-19. Although the majority of the patients show a very mild, self-limiting viral respiratory disease, many clinical manifestations in severe patients are unique to COVID-19, such as severe lymphopenia and eosinopenia, extensive pneumonia, a "cytokine storm" leading to acute respiratory distress syndrome, endothelitis, thromboembolic complications, and multiorgan failure. The epidemiologic features of COVID-19 are distinctive and have changed throughout the pandemic. Vaccine and drug development studies and clinical trials are rapidly growing at an unprecedented speed. However, basic and clinical research on COVID-19-related topics should be based on more coordinated high-quality studies. This paper answers pressing questions, formulated by young clinicians and scientists, on SARS-CoV-2, COVID-19, and allergy, focusing on the following topics: virology, immunology, diagnosis, management of patients with allergic disease and asthma, treatment, clinical trials, drug discovery, vaccine development, and epidemiology. A total of 150 questions were answered by experts in the field providing a comprehensive and practical overview of COVID-19 and allergic disease.

| S-EPMC7323196 | biostudies-literature

Two Questions About the Design of Cluster Randomized Trials: A Tutorial.

Project description:This is a short tutorial on two key questions that pertain to cluster randomized trials (CRTs): 1) Should I perform a CRT? and 2) If so, how do I derive the sample size? In summary, a CRT is the best option when you "must" (e.g., the intervention can only be administered to a group) or you "should" (e.g., because of issues such as feasibility and contamination). CRTs are less statistically efficient and usually more logistically complex than individually randomized trials, and so reviewing the rationale for their use is critical. The most straightforward approach to the sample size calculation is to first perform the calculation as if the design were randomized at the level of the patient and then to inflate this sample size by multiplying by the "design effect", which quantifies the degree to which responses within a cluster are similar to one another. Although trials with large numbers of small clusters are more statistically efficient than those with a few large clusters, trials with large clusters can be more feasible. Also, if results are to be compared across individual sites, then sufficient sample size will be required to attain adequate precision within each site. Sample size calculations should include sensitivity analyses, as inputs from the literature can lack precision. Collaborating with a statistician is essential. To illustrate these points, we describe an ongoing CRT testing a mobile-based app to systematically engage families of intensive care unit patients and help intensive care unit clinicians deliver needs-targeted palliative care.

| S-EPMC8009809 | biostudies-literature

Considering usual medical care in clinical trial design.

Project description: Not available

| S-EPMC2746285 | biostudies-literature

Novel 2019 coronavirus: Genome structure, clinical trials, and outstanding questions.

Project description:Impact statementEarly availability of the sequence, the genetic material of SARS-CoV-2 (the virus that causes COVID-19), has prompted efforts towards identifying a safe and effective vaccine in the current public health emergency. To that end, understanding the pathophysiology of disease is crucial for scientists around the world. Since conventional vaccine development and manufacturing may take several years, it is important to think about alternative strategies that we could use to mitigate imminent catastrophe. We hope that this article will open up new avenues and provide insights that could potentially save hundreds of lives affected by COVID-19.

| S-EPMC7327953 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data