BMC Medical Research Methodology

Call for content!

Real-world data hold great potential for new scientific discoveries, problem-solving and decision-making. To unlock the full potential, new, effective, and practically feasible statistical and machine learning methods are needed. 

Our new collection, guest edited by Prof. Panagiotakos and Prof. Liu, welcomes content highlighting opportunities and challenges around methods and applications for real-world data, as well as big data challenges in medical research.

Methodologies for COVID-19 research and data analysis

Our collection on methodological approaches to medical research and data analysis related to COVID-19 is no longer accepting new submissions. You can continue to browse our published content, and remember all of our articles are open access and free to read! 

Featured article: Machine learning in medicine: a practical introduction to natural language processing

In this paper, Harrison and Sidey-Gibbons provide a practical introduction to contemporary techniques for the analysis of text-data, using freely-available software.

Featured article: Managing overlap of primary study results across systematic reviews: practical considerations for authors of overviews of reviews

Using a primary study result multiple times in the same analysis overstates its sample size and number of events, falsely leading to greater precision in the analysis. Lunny et al describe different types of overlapping data, present methods to identify these and illustrate approaches to manage the overlap.

Featured article: Open science saves lives: lessons from the COVID-19 pandemic

In this Commentary, Besançon et al express concerns about the violation of some of the Open Science principles and its potential impact on the quality of research output during the COVID-19 pandemic. The authors call for a wider adoption of Open Science practices and hope this article serves as a reminder that science should always be a rigorous process, reliable and transparent.