Back

Randomized controlled trials claiming "personalized", "individualized" and "precision" interventions: characteristics, transparency and bias

Russo, L.; Lentini, N.; Soru, L.; Pastorino, R.; Boccia, S.; Ioannidis, J.

2026-02-12 medical education
10.64898/2026.02.09.26345904 medRxiv
Show abstract

The terms personalized, individualized and precision medicine are increasingly used to describe health interventions, yet their operational meaning in clinical research remains unclear. Despite extensive conceptual discussion, there is limited empirical evidence on how these labels are applied in randomized controlled trials (RCTs) and whether such trials meet standards of transparency and methodological rigor. We systematically examined 262 RCTs published between 2020 and 2022 that used the terms "personalized", "individualized", or "precision" in the title to describe an intervention. The term "personalized" was used most frequently (49.2%), followed by "individualized" (45.8%) and "precision" (5.0%). In most trials, personalization involved behavioral, digital, or pharmacological interventions, with few studies employing -omics approaches. Personalization was most often based on individual lifestyle factors, psychological characteristics, or disease classification. We also found that in most trials, personalization consisted of tailoring a single intervention to individuals (82.8%), often through individualized dosage (73.2%). Most included RCTs were judged to be at high risk of bias and showed limited transparency with respect to data and code sharing. Our study suggests that, in contemporary RCTs, the labels "personalized", "individualized", and "precision" are applied interchangeably to a wide range of heterogeneous interventions that are predominantly non-genomic. Greater conceptual clarity and stronger methodological standards are needed to ensure that claims of personalization in clinical research are empirically meaningful and reliable.

Published in Journal of Clinical Epidemiology (predicted rank #3) · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.