STRengthening Analytical Thinking for Observational Studies: a brief overview of some contributions of the STRATOS initiative

On recent progress of topic groups and panels
Willi Sauerbrei1, Michal Abrahamowicz2, Marianne Huebner3, Ruth Keogh4 on behalf of the STRATOS initiative, Freiburg, Germany
1Medical Center – University of Freiburg, Germany, 2McGill, Montreal, Canada, 3Michigan State University, East Lansing, USA, 4London School of Hygiene and Tropical Medicine, UK

Observational studies present researchers with a number of analytical challenges, related to both: complexity of the underlying processes and imperfections of the available data (e.g. unmeasured confounders, missing data, measurement errors). Whereas many methods have been proposed to address specific challenges, there is little consensus regarding which among the alternative methods are preferable for what types of data. Often, there is also lack of solid evidence concerning systematic validation and comparisons of the performance of the methods.

To address these complex issues, the STRATOS initiative was launched in 2013. In 2021, STRATOS involves more than 100 researchers from 19 countries worldwide with background in biostatistical and epidemiological methods. The initiative has 9 Topic Groups (TG), each focusing on a different set of ‘generic’ analytical challenges (e.g. measurement errors or survival analysis) and 11 panels (e.g. publications, simulation studies, visualisation) co-ordinate it,  to share best research practices and to disseminate research tools and results from the work of the TGs.  

We will provide a short overview of recent progress, point to some research urgently needed and emphasize the importance of knowledge translation.More details are provided in short reports from all TGs and some panels which are regular contributions in the Biometric Bulletin, the newsletter of the International Biometric Society (, since issue 3 from 2017.

Statistical analysis of high-dimensional biomedical data: issues and challenges in translation to medically useful results
Lisa Meier McShane on behalf of the high-dimensional data topic group, US-NCI, Bethesda, USA
Division of Cancer Treatment and Diagnosis, U.S. National Cancer Institute, National Institutes of Health, USA

Successful translation of research involving high-dimensional biomedical data to medically useful results requires a research team with expertise including clinical and laboratory science, bioinformatics, computational science, and statistics.  A proliferation of pubic databases and powerful data analysis tools have led to many biomedical publications reporting results suggested to have potential clinical application.  However, many of these results cannot be reproduced in subsequent studies, or the findings, although meeting statistical significance criteria or other numerical performance criteria, have no clear clinical utility.  Many factors have been suggested as contributors to irreproducible or clinically non-translatable biomedical research, including poor study design, analytic instability of measurement methods, sloppy data handling, inappropriate and misleading statistical analysis methods, improper reporting or interpretation of results, and on rare occasions, outright scientific misconduct.  Although these challenges can arise in a variety of medical research studies, this talk will focus on research involving use of novel measurement technologies such as “omics assays” which generate large volumes of data requiring specialized expertise and computational approaches for proper management, analysis and interpretation [].  Research team members share responsibility for ensuring that research is performed with integrity and best practices are followed to ensure reproducible results.  Further, strong engagement of statisticians and other computational scientists with experts in the relevant medical specialties is critical to generation of medically interpretable and useful findings.  Through a series of case studies, the many dimensions of reproducible and medically translatable omics research are explored and recommendations aiming to increase the translational value of the research output are discussed. 

Towards stronger simulation studies in statistical research
Tim Morris on behalf of the Simulation panel, London, UK
MRC Clinical Trials Unit at University College London, UK

Simulation studies are a tool for understanding and evaluating statistical methods. They are sometimes necessary to generate evidence about which methods are suitable and – importantly – unsuitable for use, and when. In medical research, statisticians have been pivotal to the introduction of reporting guidelines such as CONSORT. The idea is that these give readers enough understanding of how a study was conducted that they could replicate the study themselves. Simulation studies are relatively easy to replicate but, as a profession, we tend to forget our fondness for clear reporting. In this talk, I will describe some common failings and make suggestions about the structure and details that help to clarify published reports of simulation studies.

General discussion about potential contributions to the future work of the STRATOS initiative (