Evidence map›Paper›PMID 30530757›Full record

ReviewBMJ (Clinical research ed.)2018

Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects.

David M Kent, Ewout Steyerberg, David van Klaveren

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMJ (Clinical research ed.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 247 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
247citing papers in PubMed, 8 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

247 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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187 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

3 authors.

David M KentPredictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA dkent1@tuftsmedicalcenter.org.
Ewout SteyerbergDepartment of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333 ZC, Leiden, Netherlands.
David van KlaverenPredictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA.

Funding

Value of Personalized Risk InformationU01NS086294 · NINDS · TUFTS MEDICAL CENTER · PI KENT, DAVID M, NEUMANN, PETER · 2013 to 2017
$2.6M
NINDS NIH HHS U01 NS086294
6 · The paper itself

Abstract

The use of evidence from clinical trials to support decisions for individual patients is a form of "reference class forecasting": implicit predictions for an individual are made on the basis of outcomes in a reference class of "similar" patients treated with alternative therapies. Evidence based medicine has generally emphasized the broad reference class of patients qualifying for a trial. Yet patients in a trial (and in clinical practice) differ from one another in many ways that can affect the outcome of interest and the potential for benefit. The central goal of personalized medicine, in its various forms, is to narrow the reference class to yield more patient specific effect estimates to support more individualized clinical decision making. This article will review fundamental conceptual problems with the prediction of outcome risk and heterogeneity of treatment effect (HTE), as well as the limitations of conventional (one-variable-at-a-time) subgroup analysis. It will also discuss several regression based approaches to "predictive" heterogeneity of treatment effect analysis, including analyses based on "risk modeling" (such as stratifying trial populations by their risk of the primary outcome or their risk of serious treatment-related harms) and analysis based on "effect modeling" (which incorporates modifiers of relative effect). It will illustrate these approaches with clinical examples and discuss their respective strengths and vulnerabilities.

Indexed as

Evidence-Based MedicineOutcome Assessment, Health CareClinical Decision-MakingHumansPrecision MedicineRandomized Controlled Trials as TopicResearch DesignRisk Assessment

Identifiers

PMID30530757
PMCPMC6889830

What OpenQuestion holds

Textmetadata
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.