Evidence map›Paper›PMID 29954372›Full record

ArticleBMC medical informatics and decision making2018

Development and evaluating multimarker models for guiding treatment decisions.

Parvin Tajik, Mohammad Hadi Zafarmand, Aeilko H Zwinderman, Ben W Mol, Patrick M Bossuyt

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 38% of its field
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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Trial
  4. Review
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

5 authors at 3 institutions in 2 countries.

Parvin TajikDepartment of Pathology, Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Department of Obstetrics & Gynaecology, Academic Medical Centre - University of Amsterdam, Room J1b-210, PO Box 22700, 1100, DE, Amsterdam, the Netherlands. p.tajik@amc.uva.nl.ORCID 0000-0003-3945-853X
Mohammad Hadi ZafarmandDepartment of Clinical Epidemiology, Biostatistics & Bioinformatics, Department of Obstetrics & Gynaecology, Academic Medical Centre, Amsterdam, the Netherlands.
Aeilko H ZwindermanDepartment of Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Centre, Amsterdam, the Netherlands.
Ben W MolDepartment of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia.
Patrick M BossuytDepartment of Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Centre, Amsterdam, the Netherlands.
Amsterdam UMC Location University of Amsterdam · NLMonash University · AUUniversity of Amsterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite the growing interest in developing markers for predicting treatment response and optimizing treatment decisions, an appropriate methodology to identify, combine and evaluate such markers has been slow to develop. We propose a step-by-step strategy for analysing data from existing randomised trials with the aim of identifying a multi-marker model for guiding decisions about treatment.

methodsWe start with formulating the treatment selection problem, continue with defining the treatment threshold, prepare a list of candidate markers, develop the model, apply the model to estimate individual treatment effects, and evaluate model performance in the study group of patients who meet the trial eligibility criteria. In this process, we rely on some well-known techniques for multivariable prediction modelling, but focus on predicting benefit from treatment, rather than outcome itself. We present our approach using data from a randomised trial in which 808 women with multiple pregnancy were assigned to cervical pessary or control, to prevent adverse perinatal outcomes. Overall, cervical pessary did not reduce the risk of adverse perinatal outcomes.

resultsThe treatment threshold was zero. We had a preselected list of 5 potential markers and developed a logistic model including the markers, treatment and all marker-by-treatment interaction terms. The model was well calibrated and identified 35% (95% confidence interval (CI) 32 to 39%) of the trial participants as benefitting from pessary insertion. We estimated that the risk of adverse outcome could be reduced from 13.5 to 8.1% (5.4% risk reduction; 95% CI 2.1 to 8.6%) through model-based selective pessary insertion. The next step is external validation upon existence of independent trial data.

conclusionsWe suggest revisiting existing trials data to explore whether differences in treatment benefit can be explained by differences in baseline characteristics of patients. This could lead to treatment selection tools which, after validation in comparable existing trials, can be introduced into clinical practice for guiding treatment decisions in future patients.

Indexed as

BiomarkersClinical Decision-MakingModels, TheoreticalOutcome Assessment, Health CareRandomized Controlled Trials as TopicAdultFemaleHumansPessariesPregnancyPregnancy ComplicationsPregnancy, MultipleBiomarkersBiomarkerIndividualised medicineModel developmentPrediction modelsPrognosticRandomised controlled trialsStratified medicineSubgroup analysisTreatment selectionValidation

Identifiers

PMID29954372
PMCPMC6022448
OpenAlexW2810543949

What OpenQuestion holds

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LicenceCC BY
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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.