Does evidence support the high expectations placed in precision medicine? A bibliographic review

Jordi Cortés*, José Antonio González, María Nuncia Medina, Markus Vogler, Marta Vilaró, Matt Elmore, Stephen John Senn, Michael Campbell, Erik Cobo

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

23 Citations (Scopus)


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 ideal, a constant effect would facilitate individual management. Another consequence of a constant effect is that the outcome variance would be the same in 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 needed. Methods: We reviewed parallel trials with quantitative outcomes published in 2004, 2007, 2010 and 2013. We collected baseline and final standard deviations of the main outcome. 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 seven studies (n = 30, 14.4%) had statistically different variances between groups, leading 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: We found that the outcome variance was more often smaller in the intervention group, suggesting that treated patients may end up pertaining more often to reference values. This observed reduction in variance might also imply that there could be a subgroup of less ill patients who derive no benefit, which would require studying whether the effect merits enduring the side effects as well as the economic costs. We have shown that the comparison of variances is a useful but not definitive tool for assessing whether or not the assumption of a constant effect holds.

Original languageEnglish
Publication statusPublished - 7 Mar 2019


  • Clinical trial
  • Constant effect
  • Homoscedasticity
  • Precision medicine
  • Review
  • Standard deviation
  • Variability


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