Related Experiment Videos
The future of biostatistics: expecting the unexpected
1Department of Medical Statistics, Leiden University, The Netherlands.
Statistics in Medicine
|March 4, 1998
Summary
This paper reviews emerging trends in clinical biostatistics, including complex data analysis and causal modeling. It also outlines future aspirations and concerns, such as integrating models into clinical practice and moving beyond traditional statistical tests.
Area of Science:
- Biostatistics
- Clinical Informatics
- Data Science
Background:
- Biostatistics is crucial for advancing clinical research and healthcare.
- The field faces evolving challenges with new data types and analytical demands.
Purpose of the Study:
- To review current trends shaping the future of clinical biostatistics.
- To explore potential advancements and challenges within the field.
Main Methods:
- Review of key biostatistical trends and applications.
- Discussion of future 'dreams' (aspirations) and 'nightmares' (undesirable practices).
- Exploration of advanced statistical techniques like causal models and Markov chain Monte Carlo.
Main Results:
- Identified trends include complex data analysis, causal inference, advanced modeling techniques, and efficient computation.
- Future aspirations involve clinical implementation of models, improved data infrastructure, and spatiotemporal disease mapping.
- Concerns focus on phasing out P-values, rank tests, exact methods, meta-analysis, and matched case-control studies.
Conclusions:
- The future of biostatistics lies in embracing complex data, causal inference, and advanced computational methods.
- Successful integration of biostatistical models into clinical practice is a key aspiration.
- A shift away from certain traditional statistical methods is envisioned for future practice.