This post originally appeared on Fast Data Science’s blog on LinkedIn.
A well-constructed study plan is the backbone of any clinical research project. It guides the research process and ensures that the study is feasible, ethical, and capable of generating valid results. Here’s a step-by-step guide to developing an effective study plan.
The core of your study plan is the research question. It must be specific and significant and address an unresolved issue. For example, “Can we diminish loneliness among the elderly by providing a structured in-home support program?” This question is precise and has direct implications for improving patient care.
Could you conduct a thorough literature review to understand what is already known?
Define the significance of the research question and its potential impact on clinical practice or public health.
2. Study Design:
Choose between observational and clinical trials based on the research question and available resources.
Consider various designs, such as cross-sectional, cohort, or case-control studies for observational research and randomised controlled intervention trials.
3. Participants:
Define explicit inclusion and exclusion criteria to ensure a representative sample.
Estimate the required sample size early to avoid feasibility issues later.
4. Variables:
Clearly define predictor, confounding, and outcome variables.
Ensure these variables are measurable and relevant to the research question.
5. Statistical Issues:
Formulate hypotheses and determine the appropriate statistical methods.
Plan for data analysis to ensure the results are valid and reliable.
Expertise in your study area is crucial. Keep abreast of recent advances and engage with the scientific community through:
Systematic literature reviews.
Participation in scientific meetings and conferences.
Building relationships with other experts in the field.
Ethical standards are paramount in clinical research. Ensure your study:
Obtains informed consent from participants.
Maintains participant confidentiality.
Receives approval from relevant ethical committees.
Throughout the study, remain open to new ideas and improvements:
Attend conferences and engage in informal discussions with peers.
Be receptive to feedback and willing to refine your study plan as necessary.
Developing a robust study plan involves meticulous preparation and continuous learning. By focusing on a well-defined research question and adhering to ethical standards, you can ensure that your clinical research will be feasible, impactful, and valuable to the scientific community.
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#ClinicalResearch #StudyDesign #MedicalScience #ResearchEthics
Source:
Browner, Warren, S. et al. Designing Clinical Research. Available from: Wolters Kluwer, (5th Edition). Wolters Kluwer Health, 2022.
Browner, Warren, S. et al. Designing Clinical Research. Available from: Wolters Kluwer, (5th Edition). Wolters Kluwer Health, 2022.
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.
