Mastering variation: Variance components and personalised medicine
Open accessAbstract
Various sources of variation in observed response in clinical trials and clinical practice are considered, and ways in which the corresponding components of variation might be estimated are discussed. Although the issues have been generally well-covered in the statistical literature, they seem to be poorly understood in the medical literature and even the statistical literature occasionally shows some confusion. To increase understanding and communication, some simple graphical approaches to illustrating issues are proposed. It is also suggested that reducing variation in medical practice might make as big a contribution to improving health outcome as personalising its delivery according to the patient. It is concluded that the common belief that there is a strong personal element in response to treatment is not based on sound statistical evidence.
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Most randomized clinical trials use a parallel design, assigning each person randomly to a different treatment group, or arm.
“Most RCTs follow a parallel design, where each individual gets randomly assigned to a different treatment group, called an arm (69).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [69]
More complex RCTs, like repeated cross-over designs, assign patients randomly to different treatments across periods and can measure patient/cluster-treatment interactions.
“More complex RCTs, such as repeated cross-over designs (69), where patients are randomly assigned to different treatments at different periods throughout the study, can measure patient/cluster-treatment interactions.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [69]
Current tools for the homogeneity assumption might not help with treatment-effect heterogeneity in randomized clinical trials, where several factors can drive variation in treatment response.
“For example, our current tools to tackle the homogeneity assumption may not help address the treatment-effect het- erogeneity problem (67–69) in research clinical trials (RCTs), where multiple factors may drive treatment-response variation.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [69]
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- Authors
- Senn, S.
- Affiliations
- Luxembourg Institute of Health (LU)
- Corresponding author
- Senn, Stephen
- ORCID
- Senn, Stephen
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2015-09-28
- Volume
- 35
- Issue
- 7
- Pages
- 966-977
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- PMC
- PMC5054923
- OpenAlex
- W2166770001
- Cited by
- 205
- References
- 55
- Citations by year
- 2026: 15 · 2025: 23 · 2024: 22 · 2023: 21 · 2022: 11 · 2021: 36 · 2020: 20 · 2019: 24 · 2018: 21 · 2017: 8 · 2016: 4
- FWCI
- 9.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Statistics in Medicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- European Union FP7 programme (602552)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Reliability and Agreement in Measurement
- Keywords
- variance components, personalized medicine, sources of variation, clinical trials, clinical practice variation, graphical method, treatment response heterogeneity, health outcomes
- MeSH terms
- Analysis of Variance, Asthma, Clinical Trials as Topic, Computer Graphics, Computer Simulation, Forced Expiratory Volume, Humans, Tonsillectomy, Randomized Controlled Trials as Topic, Cross-Over Studies, Biostatistics, Precision Medicine
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