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Does evidence support the high expectations placed in precision medicine? A bibliographic review
- Source :
- Recercat. Dipósit de la Recerca de Catalunya, instname, UPCommons. Portal del coneixement obert de la UPC, Universitat Politècnica de Catalunya (UPC), F1000Research
- Publication Year :
- 2019
- Publisher :
- F1000 Research Ltd, 2019.
-
Abstract
- Background: Precision medicine is the Holy Grail of interventions that are tailored to a patient’s individual characteristics. However, conventional clinical trials are designed to find differences in averages, and interpreting these differences depends on untestable assumptions. Although only an ideal, a constant effect of treatment would facilitate individual management. A direct consequence of a constant effect is that the variance of the outcome measure would be the same in the treated and control arms. We reviewed the literature to explore the similarity of these variances as a foundation for examining whether and how often precision medicine is definitively required. Methods: We reviewed parallel clinical trials with numerical primary endpoints published in 2004, 2007, 2010 and 2013. We collected the baseline and final standard deviations of the main outcome measure. We assessed homoscedasticity by comparing the variance of the primary endpoint between arms through the outcome variance ratio (treated to control group). Results: The review provided 208 articles with enough information to conduct the analysis. One out of five studies (n = 40, 19.2%) had statistically different variances between groups, implying a non-constant-effect. The adjusted point estimate of the mean outcome variance ratio (treated to control group) is 0.89 (95% CI 0.81 to 0.97). Conclusions: The mean variance ratio is significantly lower than 1 and the lower variance was found more often in the intervention group than in the control group, suggesting it is more usual for treated patients to be stable. This observed reduction in variance might also imply that there could be a subgroup of less ill patients who derive no benefit from treatment. This would require further study as to whether the treatment effect outweighs the side effects as well as the economic costs. We have shown that there are ways to analyze the apparently unobservable constant effect.
- Subjects :
- Combinatorial analysis
MEDLINE
030209 endocrinology & metabolism
Matemàtiques i estadística::Matemàtica discreta::Combinatòria [Àrees temàtiques de la UPC]
Review
Homoscedasticity
62 Statistics::62D05 Sampling theory, sample surveys [Classificació AMS]
General Biochemistry, Genetics and Molecular Biology
Standard deviation
03 medical and health sciences
0302 clinical medicine
Outcome Assessment, Health Care
Statistics
Clinical endpoint
Humans
Medicine
030212 general & internal medicine
Point estimation
Sampling (Statistics)
Variability
General Pharmacology, Toxicology and Pharmaceutics
Randomized Controlled Trials as Topic
Evidence-Based Medicine
General Immunology and Microbiology
business.industry
Precision medicine
Articles
General Medicine
Variance (accounting)
Clinical Trial
Outcome (probability)
3. Good health
Clinical trial
Combinacions (Matemàtica)
Matemàtiques i estadística::Estadística aplicada [Àrees temàtiques de la UPC]
05 Combinatorics::05E Algebraic combinatorics [Classificació AMS]
business
Constant Effect
Mostreig (Estadística)
Research Article
Subjects
Details
- ISSN :
- 20461402
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- F1000Research
- Accession number :
- edsair.doi.dedup.....d486da50db33f6712eafe4ca12a6e519
- Full Text :
- https://doi.org/10.12688/f1000research.13490.5