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Prediction methods for rare diseases or outcomes

Guest Editors:
Cindy Feng: Dalhousie University, Canada
Longhai Li: University of Saskatchewan, Canada
Chang Xu: Ministry of Education of the People's Republic of China, China


BMC Medical Research Methodology welcomed submissions for our collection focused on "Prediction Methods for Rare Diseases or Outcomes". The collection aims to showcase innovative research on the development and validation of prediction models with a focus on rare events.
These models can be used to identify patients at high risk of developing a rare disease, facilitate early diagnosis and treatment, and inform the design of clinical trials.

Meet the Guest Editors

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Cindy Feng: Dalhousie University, Canada

Dr. Feng is an Associate Professor in Biostatistics at the Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Canada. Her research interests lie primarily in developing biostatistical models and methods for analyzing spatially and temporally correlated data. She is also interested in developing predictive models of modelling rare events, such as psychiatric conditions, cancer and injury, etc. She has a deep desire to bridge the gap between statistical methods and practice by pursuing methodological development and application of statistical methods in public health, which has led her to develop partnerships with researchers from other disciplines, i.e., medicine, psychology, biology, and sociology.

Longhai Li: University of Saskatchewan, Canada

Longhai Li is a professor at the University of Saskatchewan. He received his Ph.D. degree in statistics from the University of Toronto. His research activities focus on developing and applying statistical learning methods for high-throughput data and complex-structured data.  His research has been funded by NSERC, CFI, CFREF, and MITACS. His research papers have appeared in highly reputed journals, such as Journal of American Statistical Association, Statistics in Medicine, Statistics and Computing, and Scientific Reports.

Chang Xu: Ministry of Education of the People's Republic of China, China

Chang Xu is a full professor in Anhui Medical University since 2022. He received his MD and PhD in Chinese Evidence-based Medicine Centre from 2017 to 2020. Since his PhD program, Dr. Xu saved his energy on creative ideas of evidence synthesis methods, rapid evidence-based decision-making, and replicability & reproducibility of clinical trials, with a special focus on the methods for drug safety assessment. He has published more than 30 academic papers in high-quality international journals. His work has been supported by four national or institutional funding bodies.

About the collection

BMC Medical Research Methodology is welcoming submissions for our collection focused on "Prediction Methods for Rare Diseases or Outcomes". The collection aims to showcase innovative research on the development and validation of prediction models with a focus on rare events.

In recent years, there has been growing interest in the development of prediction models for rare diseases, which are often difficult to diagnose due to their low prevalence and frequent lack of knowledge about their underlying biology. These models can be used to identify patients at high risk of developing a rare disease, facilitate early diagnosis and treatment, and inform the design of clinical trials. Additionally, these methods can be used to predict rare outcomes such as adverse drug reactions or surgical complications.

We welcome papers that address the following topics:

•    Development and validation of prediction models for rare diseases or outcomes
•    Discussion of challenges and advances in modeling rare event data
•    Innovative Application of prediction models to undiagnosed diseases
•    Comparison of different prediction methods for rare diseases or outcomes

We encourage submissions from a wide range of disciplines, including biostatistics, medical informatics, epidemiology, genetics, and clinical medicine. This collection aims to contribute to the development of effective prediction methods for rare diseases or outcomes, and to facilitate the translation of these methods into clinical practice.

Image credit: © H_Ko / stock.adobe.com

  1. Clinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patien...

    Authors: Pedro Cardoso, Timothy J. McDonald, Kashyap A. Patel, Ewan R. Pearson, Andrew T. Hattersley, Beverley M. Shields and Trevelyan J. McKinley
    Citation: BMC Medical Research Methodology 2024 24:128
    Content type: Research Published on:

  2. Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential ...

    Authors: Gunjan Chandra, Junfeng Wang, Pekka Siirtola and Juha Röning
    Citation: BMC Medical Research Methodology 2024 24:112
    Content type: Research Published on:

  3. Invasive micropapillary carcinoma (IMPC) of the breast is known for its high propensity for lymph node (LN) invasion. Inadequate LN dissection may compromise the precision of prognostic assessments. This study...

    Authors: Xiangdi Meng, Furong Hao, Nan Wang, Peiyan Qin, Zhuojun Ju and Daqing Sun
    Citation: BMC Medical Research Methodology 2024 24:90
    Content type: Research Published on:

  4. Rapidly developing tests for emerging diseases is critical for early disease monitoring. In the early stages of an epidemic, when low prevalences are expected, high specificity tests are desired to avoid numer...

    Authors: Sierra Pugh, Bailey K. Fosdick, Mary Nehring, Emily N. Gallichotte, Sue VandeWoude and Ander Wilson
    Citation: BMC Medical Research Methodology 2024 24:30
    Content type: Research Published on:

  6. Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explo...

    Authors: Yinan Huang, Jieni Li, Mai Li and Rajender R. Aparasu
    Citation: BMC Medical Research Methodology 2023 23:268
    Content type: Research Published on:

Submission Guidelines

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This Collection welcomes submission of Research articles, Database articles, and Software articles. Before submitting your manuscript, please ensure you have read our submission guidelines. 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 ["Prediction Methods for Rare Diseases or Outcomes"] from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.