Skip to main content

Methods and Applications for Real World Data

Opportunities and Challenges for an evidence based approach

New Content Item © © Orbon Alija

Call for content!

Real-world data and evidence become increasingly important in medical science and health care. The effort in curbing the COVID-19 pandemic is a perfect example of that. Real-world health data and evidence may come from different sources such as computers, mobile devices, and wearables and have various types such as internet searches, social media, electronic health/medical records. The vast amount and different types of real-world data and evidence hold great potential for new scientific discovery and solving problems and making decisions that are otherwise infeasible. Meanwhile, new, effective, and practically feasible statistical and machine learning methods are needed to unlock the potential in the real-world data so practitioners and decision makers can apply the results and conclusions to better meet the medical and healthcare needs of our society. 

In this BMC Medical Research Methodology collection, we look for articles and contributions on the following topics:

  • Statistical and machine learning methods for real-world data analysis, which include but are not limited to internet traffic and searches, social media data, mobile device, wearable and apps data, electronic health/medical records, claims and billing activities.
  • New applications of existing methods to real-world data sets that are scientifically and practically relevant. 
  • New and useful real-world data and evaluation of important data sources. 
  • Review and comparisons of existing methods for real-world studies/data and evidence, identification of opportunities and challenges in research direction.
  • Causality and real-world data; how close are we?
  • Big Data challenges in medical research

Submission guidelines
This collection welcomes submissions of Research articles, Database articles and Software articles. Unsolicited narrative reviews will not be considered, as per the journal's policies. 

Articles will undergo the journal’s standard peer-review process overseen by our Guest Editors, Prof. Demosthenes Panagiotakos (Harokopio University, Greece) and Prof. Fang Liu (University of Notre Dame, USA).

Before submitting your manuscript, please ensure you have carefully read the submission guidelines for BMC Medical Research Methodology. Please ensure you highlight in your cover letter that you are submitting to a collection. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Methods and Applications for Real World Data" from the dropdown menu.

Submission deadline: 31 December 2022

New Content ItemDemosthenes Panagiotakos
Demosthenes Panagiotakos is a Professor in Biostatistics, Medical Research Methodology and Epidemiology at the Harokopio University in Athens, Greece, and a Member of the Scientific Committee on Health and Emerging Risks of the European Commission. Panagiotakos obtained his PhD in Biostatistics & Epidemiology from the University of Athens Medical School, Greece, and performed post-doctoral training at VAMC/Georgetown University, Washington DC, USA. His research interests include chronic disease epidemiology, public health, statistical modelling, forecasting, statistical machine learning, and application of multivariate statistical methods to medical sciences. He is currently a Senior Editorial Board member for BMC Medical Research Methodology.

New Content ItemFang Liu
Fang Liu is a Professor in Applied and Computational Mathematics and Statistics at the University of Notre Dame, Notre Dame, IN, USA. Liu obtained her PhD in Biostatistics from the University of Michigan, Ann Arbor, USA. Her research interests include data privacy, differential privacy, statistical machine learning, model regularization, Bayesian statistics, analysis of missing data, and application of statistical methods to biological and medical sciences, engineering and social sciences. She is a Fellow of the American Statistical Association and is currently a Senior Editorial Board member for BMC Medical Research Methodology.

  1. The Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the a...

    Authors: Elizabeth Korevaar, Simon L. Turner, Andrew B. Forbes, Amalia Karahalios, Monica Taljaard and Joanne E. McKenzie
    Citation: BMC Medical Research Methodology 2024 24:31
  2. Measuring the performance of models that predict individualized treatment effect is challenging because the outcomes of two alternative treatments are inherently unobservable in one patient. The C-for-benefit ...

    Authors: C. C. H. M. Maas, D. M. Kent, M. C. Hughes, R. Dekker, H. F. Lingsma and D. van Klaveren
    Citation: BMC Medical Research Methodology 2023 23:165
  3. Real-world data (RWD) and real-world evidence (RWE) have been paid more and more attention in recent years. We aimed to evaluate the reporting quality of cohort studies using real-world data (RWD) published be...

    Authors: Ran Zhao, Wen Zhang, ZeDan Zhang, Chang He, Rong Xu, XuDong Tang and Bin Wang
    Citation: BMC Medical Research Methodology 2023 23:152
  4. To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches ...

    Authors: Roy S. Zawadzki, Joshua D. Grill and Daniel L. Gillen
    Citation: BMC Medical Research Methodology 2023 23:122
  5. To control emerging diseases, governments often have to make decisions based on limited evidence. The effective or temporal reproductive number is used to estimate the expected number of new cases caused by an...

    Authors: Chawarat Rotejanaprasert, Andrew B. Lawson and Richard J. Maude
    Citation: BMC Medical Research Methodology 2023 23:62
  6. The increased adoption of the internet, social media, wearable devices, e-health services, and other technology-driven services in medicine and healthcare has led to the rapid generation of various types of di...

    Authors: Fang Liu and Demosthenes Panagiotakos
    Citation: BMC Medical Research Methodology 2022 22:287

    The Correction to this article has been published in BMC Medical Research Methodology 2023 23:109