Evidence map›Paper›PMID 31073534›Full record

ArticleAnnual review of statistics and its application2019

Precision Medicine.

Michael R Kosorok, Eric B Laber

Abstract read
In one paragraph

Article in Annual review of statistics and its application, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
147citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

147 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  20. GLP-1 receptor agonists or SGLT2-inhibitors? Evaluation of a personalized treatment algorithm for individuals with type 2 diabetes: a registry-based cohort study.Experimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association · 2026
    Observational

87 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Michael R KosorokDepartment of Biostatistics and Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, U.S.A.; kosorok@unc.edu.
Eric B LaberDepartment of Statistics, North Carolina State University, Raleight, North Carolina, 27695, U.S.A.; eblaber@ncsu.edu.

Funding

Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
Statistical methods for healthcare in complex patients with diabetesR01DK108073 · NIDDK · FRED HUTCHINSON CANCER RESEARCH CENTER · PI ZHAO, YINGQI · 2015 to 2019
$1.5M
NCI NIH HHS P01 CA142538NIDDK NIH HHS R01 DK108073
6 · The paper itself

Abstract

Precision medicine seeks to maximize the quality of healthcare by individualizing the healthcare process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime which comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, timing of administration, specific diet or exercise recommendation, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes which maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges.

Indexed as

data-driven decision sciencedynamic treatment regimesmachine learningpatient heterogeneitystatistical inference

Identifiers

PMID31073534
PMCPMC6502478

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.