Methodological Playlists
SERplaylists are curated collections of key papers and expert commentary on foundational topics in epidemiology. Designed by leaders in the field, these playlists offer a guided pathway through core methodological and substantive themes.
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Jessie K. Edwards and Jessica G. Young address the challenges of causal inference when competing events occur. This article explores methodological approaches to handle these complexities and offers guidance for improving validity in epidemiologic research.
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Eric Lofgren explores the promise and peril of randomness in epidemiologic research. This article discusses the role of random variation in study design and interpretation, highlighting both its benefits and potential pitfalls for scientific inference.
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Laura Balzer introduces Super Learner and practical R coding strategies for epidemiologic analysis. This article explains how ensemble machine learning can improve prediction and inference, outlines implementation steps in R, and offers tips for reproducible code, model validation, and performance assessment.
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Charles Poole of the University of North Carolina explains the fundamentals of case-control studies in epidemiology. This article reviews design principles, strengths, limitations, and practical considerations for conducting valid and efficient case-control research.
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Anusha Vable explores approaches to measuring socioeconomic status (SES) in epidemiologic research. This article reviews common indicators, methodological considerations, and the implications of SES measurement for understanding health disparities.
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Andrew Olshan offers a “random shuffle” of thought-provoking topics in epidemiology. This article features a mix of insights and resources designed to spark discussion and deepen understanding of key concepts in population health research.
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Maria Glymour introduces the concept of confounding in epidemiologic research. This article provides foundational readings on how confounding arises, its impact on study validity, and strategies for identifying and addressing it in analysis.
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Nancy Cook discusses risk prediction in epidemiology, focusing on methods to estimate individual and population-level risks. This article reviews key concepts, model development, and validation strategies for improving predictive accuracy in health research.
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Ashley Naimi introduces mediation analysis, a method for understanding pathways through which exposures affect outcomes. This article explains key concepts, assumptions, and approaches for applying mediation techniques in epidemiologic research.
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Jay Kaufman explores the concept of selection bias in epidemiologic research. This article explains how systematic differences in study participation can distort findings and offers strategies to identify and mitigate bias in study design and analysis.
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Ali Rowhani-Rahbar explains self-controlled designs in epidemiologic research, focusing on methods that use individuals as their own controls. This article reviews applications, strengths, and limitations of these designs for studying transient exposures and outcomes.
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Charles Poole of the University of North Carolina highlights ten influential papers by Sander Greenland. This curated selection showcases foundational contributions to epidemiologic theory and methods, offering essential reading for researchers and students alike.
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Matthew P. Fox and Timothy L. Lash introduce quantitative bias analysis, a powerful approach for assessing the impact of systematic errors in epidemiologic research. This article explains methods to evaluate bias and strengthen study validity.
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Jason Boardman of the University of Colorado, Boulder, discusses the role of sociological research in understanding health outcomes. This article explores how social structures and contexts shape population health and inform epidemiologic studies.
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Explore the foundations of Marginal Structural Models in epidemiology. Stephen Cole, from the University of North Carolina at Chapel Hill, introduces key concepts and practical applications for causal inference in longitudinal studies.
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Learn the fundamentals of Bayesian methods in epidemiologic research with this introductory playlist. Authored by Richard MacLehose (University of Minnesota), it explains core concepts, practical applications, and advantages of Bayesian approaches for statistical analysis and decision-making.
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Explore Consequentialist Epidemiology, a perspective that prioritizes real-world impact and ethical responsibility in research. Authored by Sandro Galea (Columbia University), this playlist examines how consequentialist thinking influences epidemiologic methods and decision-making to improve public health outcomes.
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Dive into the meaning and interpretation of p-values in statistical analysis with this scholarly playlist. Authored by Charles Poole (University of North Carolina, Chapel Hill), this resource clarifies common misconceptions and offers practical guidance for epidemiologic research and hypothesis testing.
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Delve deeper into competing risks in survival analysis with Part 2 of this scholarly playlist. Authored by Bryan Lau, this resource explores advanced concepts and practical applications for epidemiologic research, helping analysts address complex time-to-event data challenges.
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Explore the fundamentals of competing risks in survival analysis with this curated playlist of scholarly articles. Featured authors include Thomas A. Louis (Johns Hopkins Bloomberg School of Public Health) and Daniel Scharfstein (Johns Hopkins Bloomberg School of Public Health), offering expert insights into statistical methods and practical applications. Dive into Part 1 for a clear introduction and essential concepts.
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