SERplaylists

Explore our curated playlist of scholarly articles on implementation science, highlighting foundational ideas and research. This collection includes work by Sita Lujintanon and Sheree Schwartz that clarifies key concepts and frameworks guiding how evidence-based practices are adopted and sustained in real-world settings.

Alison El Ayadi and Lauren Houghton’s Mixed Methods Playlist introduces readers to integrated qualitative and quantitative approaches, offering a clear entry point into how mixed methods can deepen understanding in epidemiologic and public health research.

This playlist, “Methods for Policy Evaluation,” brings together key scholarly articles curated by Rita Hamad and Audrey Renson, the authors listed on the source page. It highlights methodological tools and frameworks used to assess policy impacts, offering readers a concise gateway into current approaches in epidemiologic and policy evaluation research.

Incomplete data are common in epidemiological studies, and complete records analysis (CRA)—the default in most software—can lead to inefficiency and bias. The extent and direction of this bias depend on the analysis model and the mechanism of missingness (MCAR, MAR, or MNAR). Principled methods such as multiple imputation, inverse probability weighting, and maximum likelihood can improve accuracy. This playlist, by Rachael Ross (Columbia University) and Kate Tilling (University of Bristol), highlights key papers explaining missing data concepts and analytic approaches to address them.

Nima Hejazi and Ashley Naimi present advanced perspectives on causal mediation analysis in this special “B-Sides” playlist. Explore nuanced methods and applications that go beyond the basics, offering deeper insight into mediation techniques for epidemiologic research.

Paul Zivich introduces M-estimation, a flexible approach for estimating causal effects in epidemiologic research. This playlist explains how M-estimators work, their advantages over traditional methods, and their role in improving accuracy and robustness in complex analyses.