This post originally appeared on Fast Data Science’s blog on LinkedIn.
Formulating a robust research question is the foundation of any successful clinical research study. This fundamental step shapes the entire project’s direction, feasibility, and impact. Let’s explore the essential aspects of crafting an effective research question.
The best research questions for seasoned investigators often stem from their prior studies or field observations. However, new investigators, although lacking extensive experience, can bring fresh perspectives that might lead to innovative approaches. It’s crucial to differentiate between a research interest and a research question. For instance, the question, “Does providing a regular basic income reduce depression among middle-aged adults?” arises from a broader interest in social determinants of health.
To identify a compelling research question:
Reflect on studies that have piqued your interest.
Consider clinical scenarios where patient outcomes could have been improved.
Question current practices and their effectiveness.
A substantial research question should be Feasible, Important, Novel, and Ethical (FINE):
Feasible: Ensure the study can be practically conducted with available resources, including sufficient participants.
Important: The question should address a significant issue that can impact patient care or advance scientific knowledge.
Novel: It should contribute new insights or challenge existing paradigms.
Ethical: The study must respect ethical standards and obtain necessary approvals.
Becoming an expert in your chosen area is fundamental. Conduct thorough literature reviews and stay updated with recent advances by:
Attending scientific meetings.
Engaging with experts and following thought leaders.
Utilizing resources like ClinicalTrials.gov and Prospero for ongoing research and systematic reviews.
Research ideas can emerge from various sources:
Clinical experiences and observations.
Collaborations with other investigators.
Teaching and discussions with students.
Informal conversations at conferences.
Creativity plays a vital role in this process. Embrace new technologies and methodologies to generate innovative research questions.
The journey from identifying a research interest to formulating a feasible research question requires careful consideration, thorough research, and creativity. By focusing on the FINE characteristics and continuously building your expertise, you can develop impactful research questions that pave the way for meaningful and valid studies.
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Source:
Browner, Warren, S. et al. Designing Clinical Research. Available from: Wolters Kluwer, (5th Edition). Wolters Kluwer Health, 2022.
#ClinicalResearch #ResearchMethodology #HealthcareInnovation #ScientificInquiry
Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan Introduction The success of a clinical trial is strongly dependent on the structure and coordination of the teams managing it. Given the high stakes and significant impact of every decision made during the trial, it is essential for each team member to collaborate efficiently in order to meet strict deadlines, comply with regulations, and ensure reliable results.
Guest post by Youssef Soliman, medical student at Assiut University and biostatistician Clinical trial protocols are detailed master-plans of a study – often 100–200 pages long – outlining objectives, design, procedures, eligibility and analysis. Reading them cover-to-cover can be daunting and time-consuming. Yet careful review is essential. Protocols are the “backbone” of good research, ensuring trials are safe for participants and scientifically valid [1]. Fortunately, there are systematic strategies to speed up review and keep it objective.
Introduction People have asked us often, how was the Clinical Trial Risk Tool trained? Does it just throw documents into ChatGPT? Or conversely, is it just an expert system, where we have painstakingly crafted keyword matching rules to look for important snippets of information in unstructured documents? Most of the tool is built using machine learning techniques. We either hand-annotated training data, or took training data from public sources. How We Trained the Models inside the Clinical Trial Risk Tool The different models inside the Clinical Trial Risk tool have been trained on real data, mostly taken from clinical trial repositories such as clinicaltrials.
