Hervé Atia’s Post

Not all research is valued the same → Here is how your research design dictates the weight of your findings. (The Research Pyramid) 1. Expert Opinion → Based on respected professionals’ insights. → Valuable for forming hypotheses, but highly subjective and prone to bias. 2. Case Report/Case Series → Describes rare or unique cases, providing in-depth insight into unusual conditions. → Useful for sparking further research but lacks generalizability. 3. Cross-Sectional Study → Captures a snapshot of a population at a single point in time. → Helps understand prevalence but does not establish causality. 4. Case-Control Study → Retrospective design comparing individuals with and without a particular outcome. → Efficient for rare diseases but vulnerable to recall bias. 5. Cohort Study → Follows groups over time to identify risk factors and outcomes. → Provides stronger evidence but may be affected by confounding variables. 6. Non-Randomized Control Trial (nRCT) → Offers insight when randomization isn’t feasible. → Prone to bias but still valuable for testing interventions. 7. Randomized Controlled Trial (RCT) → The gold standard for experimental research. → Randomization reduces bias, providing highly accurate, causal evidence. 8. Systematic Review and Meta-Analysis → Aggregates data from multiple studies, offering the highest level of evidence. → Comprehensive and rigorous, it minimizes bias and strengthens conclusions. —————— ♻️♻️♻️Repost for others #ResearchMethodology #EvidenceHierarchy #DataDrivenDecisions #HealthcareResearch

📚 Understanding the Types of Systematic Reviews: A Guide for Researchers 🔍 Systematic reviews are invaluable tools for synthesizing evidence and guiding research and practice. However, did you know there are different types of systematic reviews tailored to specific objectives? Here’s a quick breakdown to help you choose the right approach for your research: 1️⃣ Quantitative Review: Focuses on numerical data, often including a meta-analysis. 2️⃣ Qualitative Review: Synthesizes qualitative studies to explore experiences or behaviors. 3️⃣ Mixed Methods Review: Combines qualitative and quantitative evidence for a comprehensive view. 4️⃣ Meta-Analysis: Uses statistical methods to integrate results from multiple studies. 5️⃣ Scoping Review: Maps out evidence and identifies research gaps. 6️⃣ Umbrella Review: Summarizes findings from multiple systematic reviews. 7️⃣ Rapid Review: Provides evidence quickly by streamlining the review process. 8️⃣ Narrative Review: Focuses on interpreting and describing findings. 9️⃣ Realist Review: Explores how and why complex interventions work in specific contexts. 🔟 Network Meta-Analysis: Compares multiple treatments across studies. 🔢 Integrative Review: Incorporates diverse methodologies for a holistic understanding. 🔄 Living Systematic Review: Continuously updated to include new evidence. 📖 Cochrane Review: Gold standard in healthcare reviews, focusing on interventions. Selecting the right type of systematic review can make your research more effective and impactful. Let’s leverage these tools to advance evidence-based decision-making! 💡 Which type of systematic review have you used, or are planning to use, in your research? Share your experiences below! ⬇️ #SystematicReview #ResearchMethods #EvidenceBasedResearch #AcademicWriting #MetaAnalysis #QualitativeResearch #HealthcareResearch #ScopingReview

Understanding root causes is critical for successful product development, quality improvement, clinical reasoning, and making good everyday decisions. In "The Book of Why," Judea Pearl, in collaboration with Dana Mackenzie, guides us through the complex and often misunderstood realm of causality. 1. Understanding Causation: Pearl begins by untangling the frequent confusion between correlation and causation. He introduces a conceptual tool known as the "Ladder of Causation" to clarify these differences. This tool helps us see that just because two things occur together, it does not mean one causes the other—a principle crucial for both scientific research and everyday reasoning. 2. Significance of Counterfactuals: The authors advocate for "counterfactual thinking," a method of asking "what if" questions to explore alternative scenarios. Counterfactual thinking is a method if asking "what if" questions to explore alternative scenarios and their possible outcomes. This approach is vital for understanding the potential outcomes of different causes and is particularly useful in fields ranging from epidemiology to economic policy. 3. Causal Inference in Real-world Settings: Not all situations permit randomized controlled trials. Pearl and Mackenzie introduce non-experimental tools like the "do" operator and structural causal models. These tools allow us to infer causal relationships from observational data, broadening our ability to apply scientific reasoning in everyday situations where controlled experiments are impractical. 4. Role of Causal Models: Constructing causal models is akin to drawing maps of complex systems, showing how various elements interact to produce certain outcomes. These models are crucial for predicting the effects of interventions and are widely applicable in areas such as public health, economics, and environmental science. 5. Limits of Big Data: While large datasets can reveal patterns, Pearl and Mackenzie caution against overreliance on big data without understanding the causal mechanisms behind these patterns. They stress the importance of integrating causal thinking with data analysis to avoid misleading conclusions that could result from mere correlations. 6. Empowerment Through Causal Understanding: A deeper understanding of causality empowers us to make better decisions in our personal lives and to engage more effectively with societal issues. By fostering a causal understanding, people can navigate complex information and make informed choices that reflect a better understanding of potential consequences. Their work serves as a call to integrate causal thinking into our daily lives, enhancing our understanding of the world and improving our capacity to act within it effectively. https://lnkd.in/g76t_8Ks #datascience #systemsthinking #qualityimprovement

📢 Exploring the PRISMA Checklist for Systematic Reviews 📝 Are you conducting or evaluating systematic reviews? The PRISMA Checklist (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is your go-to guide for ensuring transparency, completeness, and rigor in research reporting. 🔍 What is PRISMA? PRISMA is a set of evidence-based items designed to help researchers clearly and accurately report the methods and findings of systematic reviews. Whether you're a seasoned researcher or just starting, following the PRISMA guidelines can enhance the quality and reliability of your work. 💡 Key Benefits of PRISMA: - Structured Approach: Ensures your review covers every essential aspect, from title to data synthesis. - Transparency: Helps researchers provide clear explanations of search strategies, data collection, and study selection processes. - Reproducibility: Facilitates other researchers in replicating or building upon your work. 🛠️ How Does it Help? - It provides a checklist of 27 key items, including objectives, methods, study selection, results, and limitations. - Useful for writing protocols, systematic reviews, and meta-analyses in healthcare, social sciences, and beyond. 🔗 Learn More: Explore the PRISMA 2020 checklist and flow diagram to make your research more robust, reproducible, and trustworthy. Let's elevate the standard of evidence synthesis together! 🌍💼 #Research #SystematicReview #PRISMA #MetaAnalysis #ScienceCommunication #TransparencyInResearch #DataAnalysis

Excited to Share My Recent Research Publication! I am thrilled to announce the publication of our research paper titled, "A Study of Impact of COVID-19 on Society Using Statistical Methods", in the International Research Journal of Modernization in Engineering, Technology, and Science (Volume 05, Issue 06,June 2023). KEY HIGHLIGHTS: The study examines the societal and health impacts of COVID-19, leveraging statistical tools like Chi-Square, Mann-Whitney U tests, and graphical analyses. Key findings include: - The effect of the pandemic on dietary habits and health checkups. - Independence of side effects from demographic factors such as gender and area. - Insight into government planning and isolation preferences. This interdisciplinary work involved: - Data collection from 200 participants across rural and urban areas. - Application of statistical software (MS Excel, R) for rigorous analysis. The research highlights areas for improvement in post-pandemic healthcare planning and emphasizes the importance of data-driven decision-making. You can find the publication here: paper ID "IRJMETS50600010092" Let’s continue using data and research to make meaningful societal impacts! #ResearchPublication #COVID19 #StatisticalAnalysis #DataScience #HealthcareResearch

Hey everyone! Check out EmpiricBio! They offer expert systematic review services tailored to your research needs. Their structured and unbiased approach provides high-quality information essential for evidence-based practice. Dive into their latest post to learn more about how they can support your projects! #MedicalResearch #SystematicReview #EmpiricBio

Meta-analysis traditionally relies on aggregate data to infer a pooled overall quantity of interest, such as effect size or accuracy metrics. While this method is relatively simple and easy to learn, it can lose critical information, limiting its suitability for more complex analyses like survival and moderation analyses. This is where IPD meta-analysis (Individual Participant/Patient Data) becomes invaluable. By using individual-level data instead of aggregate data, IPD meta-analysis overcomes these limitations and enables more nuanced examinations, such as interactions between treatment effects and participant-level covariates, which are crucial for modern precision medicine. My main resource for learning about this topic has been the book Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research by Richard D. Riley. Having recently completed most of it, I can say that while the book is highly theoretical, it also covers various applications that can greatly benefit research. I recommend studying this book with a group or a mentor to avoid feeling overwhelmed by its complexity. Undertaking IPD meta-analysis requires significantly more effort and skill, which is why many researchers tend to avoid it. However, it can answer fascinating research questions, and I am eager to participate in more studies of this kind. #IPD #Research #MetaAnalysis

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐒𝐲𝐬𝐭𝐞𝐦𝐚𝐭𝐢𝐜 𝐑𝐞𝐯𝐢𝐞𝐰? A Systematic Review is a rigorous and structured approach to reviewing existing research on a particular topic. Unlike traditional literature reviews, systematic reviews follow a defined protocol to minimize bias and provide more reliable findings. 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞: • Clear Research Question: Focused on a specific question. • Comprehensive Search Strategy: Covers all relevant studies. • Quality Assessment: Evaluates the quality and relevance of included studies. • Data Synthesis: Combines results using statistical methods (meta-analysis) if appropriate. Systematic reviews are essential for evidence-based practice, providing high-quality information to inform healthcare decisions. EmpiricBio offers expert systematic review services tailored to your research needs. Contact us to learn more about how we can support your projects! #MedicalResearch #SystematicReview #EmpiricBio

Do you have the correct #mindset to approach evidence search? Sometimes, researchers build their argumentation to support a phenomenon based on 'ad hoc' evidence research. That means we sometimes look for evidence which supports our data. But wait: discussions on your results, or any fact which relies on evidence, should be honest and not biased. This is critical to avoid unconscious - or sometimes conscious - bias. Here's 3 ways to approach your evidence search: 1️⃣ Don't believe a sigle evidence going in favour of your data, nor a single one going in wichever other direction. Don't give as well any single evidence as valid, nor invalid per se. Allways keep a doubting mindset. 2️⃣ When analizing evidence, try to identify all or most of literature about a phenomenon ot topic. This will give you a broad spectrum of the data collected. 3️⃣ Science grows by generating cummulative data, and trends change, as science changes as well. This leads to set theories that may explain what happens in whatever thing you are studying. But in biomedicine, theories in a given moment may not represent what's there for a long time, and for all cases. This means two things: 🔹theories that represent a given fact or phenomenon are valid whenever they do represent what may happen in a biologic system, such as how the body works, or the effect of a new drug. But other visions may come in the future. These theories are built by cummulative evidence on a topic. 🔹evidence comes often under a statistical representation. Actually, biology is diverse, and every single case doesn't need to behave as the mean, median or any other averaged representation of data necessarily. So, evidence search and discussion on new findings shoud be honest: succint - short as possible and to the point - broad, and unbiased. And further, always keep a tiny possibility in a small place of your head that your results or findings may be wrong, or at least rebbutted. #science #mindset #natureofscience #scienceandmedicalwriting

🔬 Study Design: Importance and Types 📊 In the realm of research, the foundation of credible and actionable results lies in robust study design. Understanding various study designs is crucial for researchers to select the most appropriate method for their investigations and ensure the validity of their findings. Here’s a quick overview of key study designs and their significance: 1. Randomized Controlled Trials (RCTs) 🧪   Description: Participants are randomly assigned to either the treatment group or the control group.  Importance: Considered the gold standard for testing interventions due to its ability to minimize bias and confounding variables. 2. Cohort Studies 📅 Description: Follows a group of people (cohort) over time to assess the impact of exposure on outcomes. Importance: Useful for studying the long-term effects of exposures and identifying potential risk factors for diseases. 3. Case-Control Studies 🔍 Description:Compares individuals with a particular condition (cases) to those without it (controls) to identify potential causes. Importance: Ideal for studying rare diseases or outcomes and can help identify associations between exposures and outcomes. 4. Cross-Sectional Studies 📏 Description: Analyzes data from a population at a specific point in time. Importance: Provides a snapshot of the prevalence of an outcome or characteristic and is useful for assessing public health needs. 5. Longitudinal Studies ⏳ Description: Observes the same subjects over a period to track changes and developments. Importance: Helps in understanding how variables influence changes over time and is valuable for studying developmental trends. Choosing the right study design is critical to addressing research questions effectively and ensuring reliable results. Each design has its strengths and limitations, and selecting the most suitable one depends on the research objectives and the nature of the data. 🔍 Which study design do you find most challenging or rewarding in your research? Share your experiences and insights! #Research #StudyDesign #RCT #CohortStudies #CaseControlStudies #CrossSectionalStudies #LongitudinalStudies #DataAnalysis #AcademicResearch #HealthResearch #AMRI