Table of Contents
Overview
Definition and Importance
Clinical epidemiology is a science that applies the principles and methods of epidemiology to clinical practice and research. It focuses on conducting, appraising, and applying clinical research studies that emphasize the prevention, diagnosis, prognosis, and treatment of diseases. This field extends the traditional study of disease distribution and determinants in populations to the clinical setting, thereby enhancing patient care through evidence-based practices.[3.1] The significance of clinical epidemiology lies in its dual focus: it is conducted in clinical environments using patients as study subjects, and its findings are utilized by clinicians to enhance decision-making regarding patient care.[2.1] This integration of clinical practice with epidemiological research facilitates a more informed approach to healthcare, allowing for better outcomes through the application of rigorous scientific methods to individual patient cases.[5.1] The term "clinical epidemiology" was introduced by John R. Paul in 1938, who described it as "a marriage between quantitative concepts used by epidemiologists to study disease in populations and decision-making in the individual case which is the daily fare of clinical medicine".[5.1] This field is primarily concerned with the investigation and control of health issues, which sets it apart from classical epidemiology.[5.1] Additionally, clinical epidemiology is closely related to evidence-based medicine, as it provides a framework for integrating clinical research with patient care, although the specifics of this relationship will be explored further in subsequent sections.[5.1]Objectives of Clinical Epidemiology
Clinical epidemiology aims to bridge the gap between clinical practice and public health by utilizing epidemiological methods to inform healthcare decisions and improve patient outcomes. One of its primary objectives is to integrate high-quality clinical evidence into medical practice, thereby enhancing the effectiveness of healthcare interventions. This integration is essential for evidence-based medicine (EBM), which emphasizes the use of data from rigorous clinical trials over traditional sources of knowledge, such as expert opinion or theoretical understanding.[11.1] Another significant objective of clinical epidemiology is to inform health policy through the systematic collection and analysis of epidemiological data. Policymakers rely on this data to allocate health resources effectively and develop policies that promote public health.[7.1] The role of clinical epidemiology extends to evaluating the consequences of health policies, as seen in the history of youth tobacco control in the United States, which exemplifies how epidemiological insights can drive meaningful changes in health outcomes.[6.1] Clinical epidemiology has evolved significantly over the past 50 years, transitioning from a focus on clinician-driven research in clinical settings to a broader emphasis on large cohorts, trials, and practice guidelines.[17.1] This evolution is particularly relevant in the context of emerging health challenges, such as antimicrobial resistance (AMR) and pandemics, which pose serious threats to global public health by increasing morbidity and mortality and imposing substantial economic burdens on healthcare systems.[15.1] To effectively address these challenges, clinical epidemiologists are increasingly employing methodologies that include mathematical and statistical modeling, which are essential for understanding infectious diseases and facilitating collaboration across scientific disciplines during pandemics.[18.1] Additionally, the integration of digital epidemiology, which utilizes new data sources and digital methods for health-related research, is becoming crucial in the analysis of health determinants and outcomes.[19.1] This multifaceted approach underscores the importance of clinical epidemiology in shaping effective health strategies and improving population health in the face of ongoing and future public health crises.In this section:
History
Evolution of Clinical Epidemiology
Clinical epidemiology emerged as a distinct subfield of epidemiology focused on issues pertinent to clinical medicine. The term was first introduced by virologist John R. Paul during his presidential address to the American Society for Clinical Investigation in 1938, where he characterized it as "the basic science of clinical medicine".[45.1] Initially, clinical epidemiology was framed as a new basic science for preventive medicine, but its definition evolved to encompass a more bedside-oriented approach, adapting to the needs of practitioners over time.[43.1] The formal establishment of clinical epidemiology as a course of study occurred in 1967 at McMaster University, under the leadership of Dean John Evans and Pathology Chairman Fraser Mustard. This initiative led to the creation of the Department of Clinical Epidemiology and Biostatistics, marking a significant milestone in the academic recognition of the field.[44.1] Alvan Feinstein played a pivotal role in shaping clinical epidemiology, nurturing many practitioners and contributing to the field's development.[42.1] As clinical epidemiology progressed, it began to differentiate itself from population epidemiology, which focuses on broader public health issues. This distinction is particularly relevant in the context of precision medicine, which emphasizes individualized patient care.[48.1] The evolution of clinical epidemiology reflects a growing recognition of the importance of evidence-based approaches in clinical decision-making, which has become increasingly vital in contemporary healthcare.[55.1]In this section:
Recent Advancements
Modern Epidemiological Techniques
Recent advancements in clinical epidemiology have reshaped the methodologies employed in the field, particularly through the expansion of its interdisciplinary nature. The 21st century has witnessed an increasing complexity of scientific inquiry, which now involves multilevel analyses and a comprehensive consideration of disease etiology and progression throughout the life course.[88.1] This transformation has been driven by the emergence of new sources and technologies for data generation, including novel medical and environmental data sources as well as -omics technologies.[88.1] These -omics technologies provide in-depth information about the effects of exposure on various metabolome profiles, such as the transcriptome, microbiome, and lipidome, thereby enhancing exposure characterization and facilitating the identification of associations between gene expression profiles and confounding environmental factors.[105.1] Furthermore, there is a growing demand to integrate new knowledge from basic, clinical, and population sciences, which underscores the evolving landscape of epidemiological research.[88.1] The COVID-19 pandemic has underscored the critical importance of epidemiology in public health, particularly in facilitating rapid and flexible responses to emerging health threats. In response to the pandemic, the National Heart, Lung, and Blood Institute launched five multisite clinical trials aimed at testing candidate host tissue-directed medical interventions to hasten recovery, improve function, and reduce morbidity and mortality, emphasizing the necessity of speed, flexibility, and collaboration in research efforts.[101.1] The crisis also resulted in a significant increase in clinical trial activity, with the COVID-evidence database reporting 2,814 randomized trials registered as of February 16, 2021; however, it is noteworthy that most of these trials were small, with only 18% having a planned sample size greater than 500.[102.1] Furthermore, the Centers for Disease Control and Prevention (CDC) played a pivotal role by establishing The Science Agenda for COVID-19, which was designed to guide the development of the evidence base necessary for public health actions, guidance, and policy to mitigate the impact of SARS-CoV-2 and ultimately bring the pandemic to an end.[103.1] Recent advancements in clinical epidemiology have transformed the discipline, emphasizing the integration of observational studies with traditional randomized clinical trials. This shift is crucial as clinical epidemiology is defined as the "science of making predictions about individual patients…using strong scientific methods" to provide the necessary information for effective patient care.[91.1] In the realm of autoimmune liver diseases, which include primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and autoimmune hepatitis (AIH), there has been a notable increase in both incidence and prevalence, particularly in Europe.[94.1] Despite their rarity, the clinical burden of these diseases is disproportionately high relative to their population incidence, with age, sex, and race significantly influencing clinical outcomes and highlighting the need for specialized medical services in gastroenterology and hepatology.[92.1] Furthermore, recent studies have explored the genetic associations and causal relationships between various modifiable risk factors and autoimmune liver diseases, indicating that these factors can impact patient prognosis and management.[93.1] Overall, the evolution of epidemiological methodologies, including the use of multilevel analyses and new data generation technologies, has enhanced our understanding of disease dynamics and improved the accuracy of findings in this field.[88.1]Impact of Technology on Clinical Research
The increasing digitization of health data, along with recent advances in various fields of computer science such as natural language processing and deep learning, has created more opportunities for applications in public health and epidemiology. These technological advancements enable clinical epidemiologists to better identify and analyze disease patterns, particularly in the context of autoimmune liver diseases. Furthermore, data generated on the Internet and the high-quality clinical data registries available enhance the analytical capabilities within this domain.[97.1] Innovative strategies in biomedical data analytics have the potential to bridge the gap between clinical practice and medical research, ultimately leading to improved patient outcomes. By utilizing large datasets from numerous patients, researchers can employ big data analytics to develop predictive models through data mining methods, which can identify clusters and correlations across various data sets.[98.1] Furthermore, the integration of artificial intelligence (AI) into epidemiologic research has opened new avenues for making causal inferences and decisions, thereby enhancing the rigor and impact of studies in this field.[99.1] AI technologies can accelerate the resolution of key epidemiological questions by improving data processing and analysis, increasing speed and efficiency, and enhancing accuracy through the integration of diverse data sources, including health records and real-time surveillance data. This comprehensive approach enables the creation of more accurate epidemic forecasts.[100.1] In the context of rare diseases, big data can provide real-world evidence for analyzing autoimmune diseases, including drug prescription patterns and treatment adherence, which are crucial for shaping future treatment protocols.[109.1] The current literature highlights AI's applications in diagnosing and managing a variety of liver diseases, including autoimmune hepatitis and primary biliary cholangitis. Beyond diagnosis, AI shows promise in predicting disease progression, such as advanced fibrosis.[111.1] The transformative potential of big data and AI in diagnosing, predicting prognosis, and treating liver diseases is evident, although further work is necessary to fully realize these advancements.[112.1] The integration of human and artificial intelligence in medicine is still in its early stages, yet it has already demonstrated the capacity to significantly improve the management of liver diseases.[113.1]Methodological Approaches
Types of Epidemiological Studies
Clinical epidemiology encompasses various methodological approaches that are essential for conducting research in clinical settings. One of the primary distinctions in this field is between classical epidemiology, which focuses on the distribution and determinants of diseases within populations, and clinical epidemiology, which applies these principles to individual patient care. This application includes conducting, appraising, and implementing clinical research studies that emphasize prevention, diagnosis, prognosis, and treatment of diseases.[133.1] A foundational text in this area, "Methods of Clinical Epidemiology," serves as a comprehensive guide for clinical researchers, providing a clear introduction to methodologies specific to clinical research. It aims to address the gaps left by traditional texts that primarily focus on public health epidemiology.[131.1] This resource is particularly valuable for both students and researchers who seek to understand the nuances of clinical research methodologies.[130.1] Additionally, "Clinical Epidemiology: Methods and Protocols, Third Edition" is recognized as a cutting-edge resource that supports clinicians and researchers in translating their findings into practical applications within health systems.[134.1] This highlights the importance of methodological rigor in clinical epidemiology, ensuring that modern medical practices are grounded in the best available scientific evidence.[132.1]Evidence-Based Practice in Clinical Epidemiology
Evidence-based practice in clinical epidemiology relies heavily on the appropriate application of statistical methods and the design of research studies. An essential component of biostatistics is the sound application of appropriate statistical methods, which is complemented by knowledge and skills in the design of both clinical trials and observational research studies.[135.1] In clinical research, various types of data, such as continuous, binary, count, multinomial, and time-to-event data, necessitate the use of specific statistical methods for analysis.[136.1] Interpreting study results requires caution, as statistically significant findings are often mistakenly viewed as clinically important. Common pitfalls in data interpretation include misinterpretation of "P" values and the overemphasis on statistical significance without considering clinical relevance.[137.1] Moreover, valid causal inference in nonexperimental data hinges on the adequate handling of confounding variables. Successful adjustment for confounding is critical, as it allows researchers to distinguish between confounders and intermediates in the causal chain, a task that can be complex and prone to errors.[138.1] The integration of population health data into clinical decision-making processes presents both opportunities and challenges. Health care professionals have identified several benefits of patient-generated health data (PGHD), including deeper insights into a patient's condition and more accurate patient information, which can enhance care plans.[141.1] However, challenges such as data volume, standards, interoperability, and privacy concerns hinder the seamless integration of these data into healthcare systems.[142.1] To determine the most appropriate methodological approach for a specific clinical research question, researchers must consider various factors that influence their choice of methods. Theoretical factors play a significant role, as positivists tend to favor quantitative research methods, emphasizing reliability and representativeness, while interpretivists prefer qualitative methods, often prioritizing depth over these aspects.[143.1] Additionally, the Patient-Centered Outcomes Research Institute (PCORI) has established methodology standards specifically for qualitative and mixed methods research. These standards are designed to ensure that research studies are effectively structured to generate the necessary evidence to address the questions of patients and clinicians regarding which methods are most effective, for whom, and under what circumstances.[144.1] Ultimately, the evolution of clinical epidemiology in health policy development is influenced by the need for evidence-based practices that address emerging health challenges. Epidemiology provides critical data on disease prevalence and risk factors, informing policy decisions at various levels.[148.1] The experience gained during the COVID-19 pandemic may also revitalize the practice of clinical epidemiology, emphasizing its role in improving health through the application of epidemiologic methods in direct patient care.[149.1]Applications In Healthcare
Clinical Decision-Making
Clinical decision-making in healthcare is significantly informed by clinical epidemiology, which employs robust scientific methods to predict individual patient outcomes and guide treatment options. This discipline emphasizes the importance of integrating observational data alongside randomized clinical trials to enhance decision-making processes in patient care.[181.1] One critical aspect of clinical decision-making is the use of decision analysis frameworks, which systematically evaluate the potential outcomes associated with various treatment options. These frameworks incorporate probabilities of outcomes and align them with patient preferences, thereby facilitating shared decision-making between clinicians and patients.[182.1] Furthermore, the integration of patient preferences into medical decision models has been recognized as essential for enhancing patient-centered care. This approach utilizes explicit patient data, clinical expertise, and medical evidence to inform treatment decisions.[183.1] The selection of appropriate analytical designs is also vital in clinical epidemiology, as it influences the quality and applicability of research findings. Factors such as expectation bias and work-up bias can affect the outcomes of studies employing two-stage methods, highlighting the need for careful methodological considerations.[177.1] Additionally, modern epidemiological techniques, including propensity score analysis and mixed effects modeling, have gained traction for their ability to assess treatment effects in observational studies effectively.[178.1] Moreover, the integration of machine learning (ML) and artificial intelligence (AI) into clinical epidemiology presents new opportunities for enhancing patient care. These technologies can help identify complex interactions among biological, social, and environmental factors that influence health trajectories, thereby informing more targeted public health interventions.[188.1] Despite advancements in clinical epidemiology, significant challenges persist in translating research findings into routine clinical practice. The gap between establishing scientific evidence and integrating it into clinical settings is well characterized, with studies indicating that only a small proportion of scientific innovations are translated into practice, often taking more than a decade to do so.[195.1] To address this issue, employing critical appraisal quality filters can enhance the likelihood of successfully distilling published research into advances that are most applicable to clinical practice.[193.1] Furthermore, the heart of evidence-based practice lies in utilizing the most current and relevant research findings, including peer-reviewed studies, systematic reviews, clinical guidelines, and well-designed trials, to guide decisions about patient care.[196.1] Ultimately, these strategies are essential for ensuring that treatment approaches remain evidence-based and effective.Public Health Implications
Clinical epidemiology plays a crucial role in shaping public health policies and improving health outcomes through its application of epidemiological methods. It serves as a bridge between research and clinical practice, guiding public health initiatives and informing clinical decision-making. The integration of clinical epidemiology into public health is essential for addressing complex health challenges, including emerging threats such as pandemics and antibiotic resistance. The historical context of clinical epidemiology dates back to 1938 when Jean Paul defined it as a foundational science for preventive medicine, emphasizing its importance in evidence-based medicine and clinical decision-making.[175.1] By applying epidemiological principles, clinical epidemiology aids in the design and evaluation of clinical research studies that focus on prevention, diagnosis, prognosis, and treatment.[176.1] This application is vital for developing effective disease prevention strategies, as robust epidemiological studies inform public policies aimed at mitigating health risks.[191.1] Moreover, the evolving landscape of healthcare, characterized by the integration of big data analytics and artificial intelligence, has further enhanced the capabilities of clinical epidemiology. These technologies enable more precise diagnoses and individualized treatment regimens, ultimately improving patient care and chronic illness management.[186.1] Leading healthcare institutions have adopted these innovations to tackle complex challenges, demonstrating the positive impact of data-driven insights on clinical decision-making and patient outcomes.[185.1] However, the integration of clinical epidemiology into public health policy is not without challenges. There is a noted gap in the training of epidemiologists regarding policy advocacy and the incorporation of process issues into epidemiological training.[190.1] Despite this, the evidence generated from epidemiological research continues to influence policy decisions, particularly in response to public health emergencies.[189.1] The concept of One Health, which links human, animal, and environmental health, provides a framework for addressing multifactorial health challenges, further underscoring the importance of clinical epidemiology in public health.[191.1]Challenges And Future Directions
Addressing Rare Diseases
Addressing rare diseases in clinical epidemiology presents unique challenges that necessitate a focus on transparency and data accessibility. Research in this field often relies on datasets that may not be ideal; however, it is crucial to emphasize that high-quality evidence can still be generated through rigorous methodologies. Transparency plays a significant role in enhancing the credibility and relevance of observational epidemiological findings, particularly when addressing the complexities associated with rare diseases.[239.1] Moreover, making research data and analysis more transparent offers numerous advantages that are vital for scientific progress. These advantages include promoting replicability, ensuring accountability, enhancing efficiency, and facilitating the cumulation of evidence over time. Additionally, transparency aids in the prevention and correction of disputable data management practices and potential misconduct, which is particularly important in the context of rare diseases where data may be scarce or difficult to obtain.[240.1] By prioritizing transparency and stakeholder engagement, clinical epidemiology can better communicate findings related to rare diseases to policymakers and the public, ultimately leading to improved health outcomes and resource allocation for these often-overlooked conditions.Integrating Health Informatics
The integration of health informatics into clinical epidemiology presents both opportunities and challenges that are critical for advancing public health outcomes. The rise of artificial intelligence (AI) and machine learning (ML) in healthcare is reshaping the landscape by enhancing diagnostics, improving clinical outcomes, and optimizing operational efficiency. AI facilitates remote patient care, streamlines workflows, and reduces healthcare costs, thereby contributing to sustainability by minimizing waste and energy consumption.[230.1] However, the implementation of AI tools has raised significant questions regarding the transparency of data models, the clinical plausibility of underlying data assumptions, and the ethical considerations surrounding their deployment.[231.1] Moreover, the advent of big data in epidemiology has transformed the approach to pandemic response by providing data-driven insights that differ from traditional methods. This evolution has enabled the utilization of large interlinkable datasets, which serve as powerful research tools for studying and improving patient care through clinical epidemiologic research.[233.1] Despite these advancements, challenges remain in integrating AI and big data within existing frameworks, particularly concerning the integration of these technologies into electronic health records (EHR) and the enhancement of decision and workflow support tools.[231.1] In low-resource settings, the emergence of point-of-care (POC) diagnostics specifically designed for these environments is crucial, as it addresses the rapid increase in the need for routine care among patients with chronic diseases. This situation necessitates a reconsideration of how healthcare can be delivered most beneficially and cost-effectively in developing countries, emphasizing the importance of bolstering support for primary care.[234.1] Additionally, epidemiology serves as a foundational element in scientific decision-making within healthcare and public health, guiding health care clients, professionals, and practitioners in their decision-making and health education based on sound epidemiological studies.[219.1] The integration of epidemiological evidence into policy is vital for addressing emerging public health challenges, as it informs the development of effective public health strategies.[221.1] However, there is a notable gap in formal training for many epidemiologists in the necessary domains to support policy development, which can impede the translation of evidence into actionable strategies.[221.1]In this section:
References
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[19] Fighting pandemics with digital epidemiology - eClinicalMedicine — Digital epidemiologists conduct traditional epidemiological studies and health-related research using new data sources and digital methods from data collection to analysis . According to Salathé, digital epidemiology is epidemiology building on digital data and tools, but a narrower definition defines it as epidemiology building on data generated and obtained with a primary goal other
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[44] History of evidence-based medicine - PMC - PubMed Central (PMC) — History of evidence-based medicine. Roger L Sur. ... Clinical epidemiology became a formal course of study first at McMaster University's new medical school in 1967 under their first dean, John Evans, and pathology chairman, Fraser Mustard, with the introduction of the new Department of Clinical Epidemiology and Biostatistics.
[45] Clinical epidemiology - Wikipedia — Clinical epidemiology is a subfield of epidemiology specifically focused on issues relevant to clinical medicine. The term was first introduced by virologist John R. Paul in his presidential address to the American Society for Clinical Investigation in 1938. It is sometimes referred to as "the basic science of clinical medicine".
[48] Precision Epidemiology: A Computational Analysis of the Impact of ... — The difference between population epidemiology and clinical epidemiology was already based on the distinction between general (macro) and individual (micro) reference, i.e., on the issues brought to the fore by the recent development of precision medicine, which has a specifical history in the field . Since the 1980s, the debate within
[55] Implementation of a competency-based medical education approach in ... — Results. Central competencies included: epidemiology and statistics for appraisal of the literature and implementation of research; the application of health promotion principles and health education strategies in disease prevention; the use of an evidence-based approach in clinical and public health decision making; the examination and analysis of disease trends at the population level; and
[88] Advances in Epidemiology - Using 21st Century Science to Improve Risk ... — In summary, the factors reshaping the field of epidemiology in the 21st century include expansion of the interdisciplinary nature of the discipline; the increasing complexity of scientific inquiry that involves multilevel analyses and consideration of disease etiology and progression throughout the life course; emergence of new sources and technologies for data generation, such as new medical and environmental data sources and -omics technologies; advances in exposure characterization; and increasing demands to integrate new knowledge from basic, clinical, and population sciences (Lam et al. Measurement-error corrections can be made by using data from validation studies and statistical models that have been developed over the last 2 decades and applied, for example, to studies on diet and disease risk, radiation and cancer, and air pollution and health (Li et al.
[91] Clinical Epidemiology, Clinical Care, and the Public's Health — Clinical epidemiology is the "science of making predictions about individual patients…using strong scientific methods" to "obtain the kind of information clinicians need to make good decisions in the care of patients."1 Although randomized clinical trials are cited routinely as the highest form of clinical epidemiology,2 recent interest has focused on the ability of observational
[92] Recent advances in clinical practice: epidemiology of autoimmune liver ... — Collectively, while autoimmune liver diseases are rare, the clinical burden is disproportionately high relative to population incidence and prevalence. Age, sex and race also impact clinical outcomes, and patient morbidity and mortality are reflected by high need for gastroenterology, hepatology and organ transplant services.
[93] Genetic association and causal relationship between multiple modifiable ... — Genetic association and causal relationship between multiple modifiable risk factors and autoimmune liver disease: a two-sample mendelian randomization study ... as the co-occurrence of these conditions can impact the prognosis and management of patients. ... Recent advances in clinical practice: epidemiology of autoimmune liver diseases. Gut
[94] Recent advances in clinical practice: epidemiology of autoimmune liver ... — Autoimmune liver diseases are chronic inflammatory hepatobiliary disorders that when classically defined encompass three distinctive clinical presentations; primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC) and autoimmune hepatitis (AIH). Meaningful changes in disease epidemiology are reported, with increasing incidence and prevalence of AIH and PSC in Europe, and rising
[97] Public Health and Epidemiology Informatics: Recent Research Trends ... — The increasing digitization of health data and the recent advances in several fields of computer science such as natural language processing and deep learning offer more opportunities for applications in the domain of public health and epidemiology. Data generated on the Internet can be ... and clinical data registries contain very high quality
[98] Biomedical data analytics for better patient outcomes — By leveraging innovative strategies in biomedical data analytics, health care can be improved globally by closing the gap between clinical practice and medical research, leading to more-effective patient outcomes. Large data sets from hundreds of patients can be analyzed using big data analytics in medicine and health care, allowing for the development of prediction models using data mining methods, identifying clusters and correlations across data sets8. An effective up-sampling approach for breast cancer prediction with imbalanced data: a machine learning model-based comparative analysis Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma
[99] Integrating Artificial Intelligence into Causal Research in Epidemiology — Purpose of Review Recent advances in Artificial Intelligence (AI) present new and not widely recognized opportunities to advance the rigor, scope, efficiency, and impact of epidemiologic research aiming to make causal inferences or causal decisions. We describe recent developments, challenges, and examples for integrating varied AI tools into the steps of Petersen and van der Laan's causal
[100] Harnessing AI to model infectious disease epidemics — AI can accelerate breakthroughs in answering key epidemiological questions via data processing and analysis, speed and efficiency, improved accuracy, and integration of multiple data sources—such as health records, real-time surveillance data, and environmental factors—to create more comprehensive and accurate epidemic forecasts.
[101] Lessons Learned from National Heart, Lung, and Blood Institute Covid-19 ... — AbstractIn response to the Covid-19 pandemic, the National Heart, Lung, and Blood Institute launched five multisite clinical trials testing candidate host tissue-directed medical interventions to hasten recovery, improve function, and reduce morbidity and mortality. Speed, flexibility, and collaboration were essential.
[102] Challenges and Lessons Learned From COVID-19 Trials: Should We Be Doing ... — The COVID-19 crisis led to a flurry of clinical trials activity. The COVID-evidence database shows 2814 COVID-19 randomized trials registered as of February 16, 2021. Most were small (only 18% have a planned sample size > 500) and the rare completed
[103] Biomedical Research COVID-19 Impact Assessment: Lessons Learned and ... — Similarly, the Centers for Disease Control and Prevention (CDC), at the forefront of the public health response to the COVID-19 pandemic, established The Science Agenda for COVID-19 to guide the development of the evidence base needed for public health actions, guidance, and policy to curb the impact of SARS-CoV-2 and ultimately bring the COVID-19 pandemic to an end . Before the onset of the COVID-19 pandemic, the U.S. biomedical and health research workforce was under stress related to “hyper-competition,” inequality, lack of diversity, and loss of early and mid-career scientists, to name a few. Available at: https://www.washingtonpost.com/health/2020/07/26/trial-coronavirus-vaccine-researchers-making-sure-black-hispanic-communities-are-included-studies/ (accessed January 2, 2021).
[105] Challenges and opportunities of molecular epidemiology: using omics to ... — Omics-based research provides in-depth information about the effect of exposure on the metabolome profile (the transcriptome, microbiome, lipidome, etc.) and how these complex changes may allow for the identification of associations between the gene expression profile and confounding environmental factors .
[109] Digital health, big data and smart technologies for the care of ... — Of crucial importance in the context or rare diseases, bigdata can be used to provide real-world evidence (RWE) from real-life practice settings for the analysis of autoimmune rCTD disease progression , drug prescription patterns including adherence to treatment guidelines, cost-effectiveness of therapy, safety , as well as into the
[111] AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver ... — A review of the current literature highlights AI's applications in diagnosing and managing a wide range of liver diseases, including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC) [], MASLD , and viral hepatitis .Beyond diagnosis, AI shows promise in predicting disease progression, such as advanced fibrosis and
[112] The application of artificial intelligence in hepatology: A systematic ... — Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21 st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to
[113] The application of artificial intelligence in hepatology: A systematic ... — The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating
[130] Methods of Clinical Epidemiology | SpringerLink — "Methods of Clinical Epidemiology" serves as a text on methods useful to clinical researchers. It provides a clear introduction to the common research methodology specific to clinical research for both students and researchers. This book sets out to fill the gap left by texts that concentrate on public health epidemiology and focuses on what is not covered well in such texts. The four
[131] Methods of Clinical Epidemiology | Request PDF - ResearchGate — "Methods of Clinical Epidemiology" serves as a text on methods useful to clinical researchers. It provides a clear introduction to the common research methodology specific to clinical research
[132] Sage Research Methods - Encyclopedia of Epidemiology - Clinical ... — Clinical epidemiology is the application of the methods and principles of epidemiology, which is focused on population health, to the practice of clinical medicine, which is focused on the health of particular individuals. Modern medical practice should at all times be predicated on the best available scientific evidence.
[133] PDF — epidemiology to clinical medicine. While classical epidemiology is the study of the distribution and determinants of diseases in populations, clinical epidemiology is the application of the principles and methods of epidemiology to conduct, appraise or apply clinical research studies focusing on prevention, diagnosis, prognosis, and treatment
[134] Clinical Epidemiology: Practice and Methods | SpringerLink — Cutting-edge and thorough, Clinical Epidemiology: Methods and Protocols, Third Edition is a valuable resource for clinicians and researchers who want to expand their works to humans and use their findings in the health system.
[135] Biostatistics and Clinical Epidemiology - University of Melbourne — An essential component of Biostatistics is the sound application of appropriate statistical methods. This is complemented by knowledge and skills in the design of both clinical trials (see figure below) and observational research studies (see figure below), as well as an ability to appropriately report and interpret data from clinical and
[136] Statistical considerations for outcomes in clinical research: A review ... — Our goal with this body of work is to highlight common types of data and analyses in clinical research. We provide a brief, yet comprehensive overview of common data types in clinical research and appropriate statistical methods for analyses. These include continuous data, binary data, count data, multinomial data, and time-to-event data.
[137] Common pitfalls in statistical analysis: Clinical versus statistical ... — In clinical research, study results, which are statistically significant are often interpreted as being clinically important. ... Keywords: Biostatistics, confidence intervals, data interpretation, statistical. ... Ranganathan P, Pramesh CS, Buyse M. Common pitfalls in statistical analysis: "P" values, statistical significance and
[138] A Very Short List of Common Pitfalls in Research Design, Data Analysis ... — One of the keys to success for valid causal inference in nonexperimental data is the adequate handling of confounding.24 Successful adjustment for confounding means being able to distinguish potential confounders from intermediates in the causal chain between the factor of interest and the outcome25 and colliders,26 which sometimes is more easily said than done.27 If the right confounders have been selected and adjusted for through, eg, by multivariable regression analysis (notice the distinction from multivariate regression28), it is tempting to also interpret the regression coefficients of the confounding variables as being corrected for confounding, which would be committing a common error known as the Table 2 fallacy.29 While substantiating causal claims is often difficult, avoiding causal inference altogether or simply replacing words like “cause” by “association” is not often the solution.30
[141] Integrating Patient-Generated Health Data Into Clinical Care Settings ... — Health care professionals identified 3 main benefits of PGHD accessibility in clinical settings: (1) deeper insight into a patient's condition; (2) more accurate patient information, particularly when of clinical relevance; and (3) insight into a patient's health between clinic visits, enabling revision of care plans for improved health
[142] The role of artificial intelligence for the application of integrating ... — Challenges mainly stem from the large volume of integrated data, data standards, data exchange and interoperability, security and privacy, interpretation, and meaningful use. The use of PGHD in health care is at a promising stage but needs further work for widespread adoption and seamless integration into health care systems.
[143] Factors Affecting Choice of Research Methods - ReviseSociology — What are the theoretical, ethical and practical factors which influence a sociologist's choice of research method? 1. Theoretical factors: Positivists prefer quantitative research methods and are generally more concerned with reliability and representativeness.Interpretivists prefer qualitative research methods and are prepared to sacrifice reliability and representativeness to gain deeper
[144] Methodological standards for qualitative and mixed methods patient ... — The Patient-Centered Outcomes Research Institute's (PCORI) methodology standards for qualitative methods and mixed methods research help ensure that research studies are designed and conducted to generate the evidence needed to answer patients' and clinicians' questions about which methods work best, for whom, and under what circumstances. This set of standards focuses on factors
[148] The Future of Epidemiology in Healthcare - Epidemiologist.io — Epidemiology's influence extends beyond the realm of healthcare to that of public health policy. By providing data on disease prevalence, risk factors, and health outcomes, epidemiology informs policy decisions on a local, national, and global scale.
[149] COVID-19 and the future of clinical epidemiology - PubMed — Only time will tell whether the experience during COVID-19 will revive the original practice of clinical epidemiology as "the application by a physician who provides direct patient care, of epidemiologic and biometric methods to the study of diagnostic and therapeutic process in order to effect an improvement in health" .
[175] PDF — 3. Clinical Epidemiology In 1938, Jean Paul coined the term clinical epidemiology, and defined it as: 'a new basic science for preventive medicine'. Therefore, the practical application of clinical epidemiology is a key part of Evidence Based Medicine and clinical decision making . Clinical epidemiology is a
[176] PDF — epidemiology to clinical medicine. While classical epidemiology is the study of the distribution and determinants of diseases in populations, clinical epidemiology is the application of the principles and methods of epidemiology to conduct, appraise or apply clinical research studies focusing on prevention, diagnosis, prognosis, and treatment
[177] A Critical Look at the Methodology of Epidemiological Studies — Accordingly, factors that can influence the quality of an epidemiological study that uses the two-stage method include variables such as expectation bias (whether the person who was evaluating the participant in the second phase was blind to the results of the screening instrument, and vice versa) and work-up bias (i.e., did the participants
[178] Introduction to clinical research based on modern epidemiology — Currently, it has become more popular to use recent modern epidemiological techniques, such as propensity score, instrumental variable, competing risks, marginal structural modeling, mixed effects modeling, bootstrapping, and missing data analyses, than before. Over the past 40 years, researchers have recognized the need to develop more efficient approaches for assessing treatment effects from observational studies, and statisticians (e.g., Rosenbaum & Rubin) and econometricians (e.g., Heckman) have developed a new approach called propensity score analysis [9–11]. There are five steps in a propensity score analysis: (1) selecting the variables for the PS model, (2) estimating the PSs, (3) applying the PS methods, (4) assessing the balance, and (5) estimating the treatment effect .
[181] Clinical Epidemiology, Clinical Care, and the Public's Health — Clinical epidemiology is the "science of making predictions about individual patients…using strong scientific methods" to "obtain the kind of information clinicians need to make good decisions in the care of patients."1 Although randomized clinical trials are cited routinely as the highest form of clinical epidemiology,2 recent interest has focused on the ability of observational
[182] Clinical epidemiology challenges when involving patients — Decision analysis involves structuring a decision using an analytical framework that includes all important outcomes associated with each treatment option along with their probabilities of occurring and the patient's preferences for these outcomes, JCE has published several articles on shared decision making but this the first to systematically
[183] Practical applications of methods to incorporate patient preferences ... — Practical applications of methods to incorporate patient preferences into medical decision models: a scoping review | BMC Medical Informatics and Decision Making | Full Text These models are systematic frameworks or tools designed to support clinical decision-making by incorporating medical evidence, clinical expertise, explicit patient preferences, and individual patient data, such as laboratory results or disease stage. Although there are studies that have already reviewed certain aspects of incorporating patient preferences for medical decision-making , our approach offers a comprehensive overview of already existing methods and identifies current trends of certain methods and gaps where further research or a paradigm change may be needed to enhance patient-centered care and SDM. This scoping review included studies involving any type of patient and focused on integrating patient preferences into medical decision-making algorithms and models.
[185] Impact of AI and big data analytics on healthcare outcomes: An ... — Leading institutions such as King Hussein Cancer Center and Jordan University Hospital have spearheaded the adoption of these technologies to address complex healthcare challenges, ranging from resource management to delivering high-quality patient care.1–3 AI and big data analytics are at the forefront of this transformation. These studies collectively support the hypothesis that adopting big data analytics can positively influence healthcare outcomes in Jordanian healthcare institutions by providing accurate, data-driven insights that enhance clinical decision-making and patient care. Thus, organizational capabilities are crucial in mediating the relationship between big data analytics adoption and healthcare outcomes, enabling healthcare institutions to fully utilize these technologies to improve patient care and operational efficiency.
[186] PDF — The integration of big data analytics in healthcare has ushered in a transformative era, redefining the landscape of patient care and treatment strategies. This review examines the multifaceted implications of big data on patient outcomes and the individualization of medical interventions.
[188] Integrating machine learning and artificial intelligence in life-course ... — The integration of machine learning (ML) and artificial intelligence (AI) techniques in life-course epidemiology offers remarkable opportunities to advance our understanding of the complex interplay between biological, social, and environmental factors that shape health trajectories across the lifespan. The integration of ML and AI techniques in life-course epidemiology has the potential to revolutionize our understanding of the complex determinants of diseases and inform the development of more targeted and effective public health interventions. In life-course epidemiology that considers long-term effects of biological, behavioral, and social exposures during gestation, childhood, adolescence, and adulthood, ML and AI offer numerous opportunities by enabling researchers to identify sensitive periods, model complex interactions, predict disease risk trajectories, and enhance causal inference methods.
[189] Bridging the epidemiology-policy divide: A consequential and evidence ... — Bridging the epidemiology-policy divide: A consequential and evidence-based framework to optimize population health - ScienceDirect Bridging the epidemiology-policy divide: A consequential and evidence-based framework to optimize population health One avenue for bridging this divide is widespread adoption and implementation of a consequential, evidence-based framework—whereby we can systematically facilitate the translation of epidemiology into policies and interventions to optimize population health. Understanding evidence-based public health policy Notably, this study also heeds recent calls for a more “consequential” epidemiology, whereby epidemiologic research can serve to more directly inform contemporary social policies to improve population health, including in response to emerging public health threats and crises such as pandemics (Kim, 2019).
[190] How do we more effectively move epidemiology into policy action? — Policy and process issues are not well incorporated into current epidemiologic training, and controversy remains over the role of the epidemiologist as an advocate for policy changes. As these case studies show, epidemiologic evidence impacts policy to address emerging public health problems yet few epidemiologists are formally trained in the domains to support policy development. Process issues are not well incorporated into current epidemiologic training and there remains controversy over the role of the epidemiologist as an advocate for policy changes and what that means (8, 10). As these case studies show, epidemiologic evidence impacts policy to address emerging public health problems yet few epidemiologists are formally trained in the domains to support policy development.
[191] Integrating a One Health approach into epidemiology to improve public ... — Public policies rely on robust epidemiological studies to develop effective disease prevention strategies. One Health, the concept that human, animal, environmental and ecosystem health are linked, provides a useful framework for researching, analysing and addressing complex interactions between multifactorial health challenges such as antimicrobial resistance.
[193] How Can Clinicians Incorporate Research Advances into Practice? — Efforts to distill published research into advances most applicable to clinical practice are more likely to be successful when they employ a critical appraisal quality filter. Another selection mechanism is needed to limit published material to research relevant to clinical practice. Practice patterns are usually determined in a collegial fashion.
[195] Advancing Translation of Clinical Research Into Practice and Population ... — The gap between establishing scientific evidence and integrating that evidence into routine clinical practice has been well characterized. It is frequently noted that only a small proportion of scientific innovation is translated into routine clinical practice and that even then, the process can take more than a decade. 1, 2 Whereas this lag may vary by measurement approach, funding mechanism
[196] Strategies for implementing evidence-based practice effectively — At the heart of evidence-based practice is the utilization of the most current and relevant research findings. Peer-reviewed studies, systematic reviews, clinical guidelines, and well-designed trials to guide decisions about patient care are all examples of potentially useful and valid research evidence. 2. Clinical expertise
[219] 12.7 The Role of Epidemiology in Scientific Decision-Making and Policy ... — 12.7 The Role of Epidemiology in Scientific Decision-Making and Policy Development - Population Health for Nurses | OpenStax Population Health for Nurses12.7 The Role of Epidemiology in Scientific Decision-Making and Policy Development 3.3 Public/Community Health Nursing Practice 12 Epidemiology for Informing Population/Community Health Decisions Epidemiology is at the foundation of scientific decision-making in health care and public health. Health care clients, professionals, and public health practitioners, including nurses, base their health care decision-making and health education on sound epidemiological studies. This chapter has highlighted the important role epidemiology plays in public health, particularly in disease control and prevention. Section URL: https://openstax.org/books/population-health/pages/12-7-the-role-of-epidemiology-in-scientific-decision-making-and-policy-development
[221] How do we more effectively move epidemiology into policy action? — Search in PubMed Search in PubMed A major focus of the American College of Epidemiology's Policy Committee has been to review the translation of epidemiologic evidence into policy by developing case studies. A framework for evidence-based public health policy has emerged to suggest that process, content, and outcomes are all needed to help move policy forward. As these case studies show, epidemiologic evidence impacts policy to address emerging public health problems, yet few epidemiologists are formally trained in the domains to support policy development. Search in PubMed Search in PubMed Search in PubMed Search in PubMed Search in PubMed Search in PubMed Search in PubMed Search in PubMed
[230] The Transformative Role of AI in Healthcare: From Clinical ... — Artificial Intelligence is reshaping healthcare by enhancing diagnostics, improving clinical outcomes and optimising operational efficiency. It enables remote patient care, streamlines workflows and reduces healthcare costs. AI also contributes to sustainability by minimising waste and energy consumption.
[231] Deploying Artificial Intelligence in Clinical Settings — Even at this early stage of AI implementation in health care, the use of AI tools has raised questions about the expectations of clinicians and health systems regarding transparency of the data models, the clinical plausibility of the underlying data assumptions, whether AI tools are suitable for discovery of new causal links, and the ethics of how, where, when, and under what circumstances AI should be deployed (He et al., 2019). One of the challenges of the use of AI in health care is that integrating it within the EHR and improving existing decision and workflow support tools may be viewed as an extension of an already unpopular technology (Sinsky et al., 2016).
[233] Clinical epidemiology in the era of big data: new opportunities ... — Routinely recorded health data have evolved from mere by-products of health care delivery or billing into a powerful research tool for studying and improving patient care through clinical epidemiologic research. Big data in the context of epidemiologic research means large interlinkable data sets within a single country or networks of
[234] Point-of-Care Diagnostics in Low-Resource Settings ... - SLAS Technology — The emergence of point-of-care (POC) diagnostics specifically designed for low-resource settings coupled with the rapid increase in need for routine care of patients with chronic diseases should prompt reconsideration of how health care can be delivered most beneficially and cost-effectively in developing countries. Bolstering support for primary care to provide rapid and appropriate
[239] How Should Meaningful Evidence Be Generated From Datasets? — But even research that uses ideal datasets might not generate high-quality evidence. This article emphasizes the roles that transparency plays in enhancing observational epidemiological findings' credibility and relevance and argues that epidemiological research can produce high-quality evidence even when datasets are not ideal.
[240] Promoting transparency of research and data needs much more attention — Making research data and data analysis more transparent has many advantages for scientific progress . It serves important objectives such as replicability, accountability, efficiency, cumulation of evidence over time, and prevention and correction of disputable data management and (un)intentional misconduct. In addition, if collected data are publicly available, the international research