ArticleThe open translational medicine journal2009
Improved Statistical Methods are Needed to Advance Personalized Medicine.
Article in The open translational medicine journal, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Implementation of a "People-Like-Me" Tool for Personalized Rehabilitation After Total Knee Arthroplasty: A Mixed Methods Pilot Study.Journal of evaluation in clinical practice · 2025Article
- Evaluating the implementation of personalised outcomes forecasts to optimise supervised exercise therapy in patients with intermittent claudication in the Netherlands: a multimethods study.BMJ open quality · 2024Article
- Comparing "people-like-me" and linear mixed model predictions of functional recovery following knee arthroplasty.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Neighbors-based prediction of physical function after total knee arthroplasty.Scientific reports · 2021Article
- How New Technology Is Improving Physical Therapy.Current reviews in musculoskeletal medicine · 2020Review
- Individualizing the dosage of Methylphenidate in children with attention deficit hyperactivity disorder.BMC medical research methodology · 2020Article
- Person-Centered Care and Physical Therapy: A "People-Like-Me" Approach.Physical therapy · 2020Article
- YM500v3: a database for small RNA sequencing in human cancer research.Nucleic acids research · 2017Article
- N-of-1 Design and Its Applications to Personalized Treatment Studies.Statistics in biosciences · 2017Article
- Integrated Genomic Medicine: A Paradigm for Rare Diseases and Beyond.Advances in genetics · 2017Review
- From small studies to precision medicine: prioritizing candidate biomarkers.Genome medicine · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Common methods of statistical analysis, e.g. Analysis of Variance and Discriminant Analysis, are not necessarily optimal in selecting therapy for an individual patient. These methods rely on group differences to identify markers for disease or successful interventions and ignore sub-group differences when the number of sub-groups is large. In these circumstances, they provide the same advice to an individual as the average patient. Personalized medicine needs new statistical methods that allow treatment efficacy to be tailored to a specific patient, based on a large number of patient characteristics. One such approach is the sequential k-nearest neighbor analysis (patients-like-me algorithm). In this approach, the k most similar patients are examined sequentially until a statistically significant conclusion about the efficacy of treatment for the patient-at-hand can be arrived at. For some patients, the algorithm stops before the entire set of data is examined and provides beneficial advice that may contradict recommendations made to the average patient. Many problems remain in creating statistical tools that can help individual patients but this is an important area in which progress in statistical thinking is helpful.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.