Evidence map›Paper›PMID 28858397›Full record

Trial reportJournal of clinical pharmacology2018

Model-Based Approach to Predict Adherence to Protocol During Antiobesity Trials.

Vishnu D Sharma, François P Combes, Majid Vakilynejad, Gezim Lahu, Lawrence J Lesko, Mirjam N Trame

Abstract readClinical Trial, Phase IIClinical Trial, Phase IIIMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of clinical pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

6 authors.

Vishnu D SharmaCenter for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA.
François P CombesCenter for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA.
Majid VakilynejadTakeda Pharmaceuticals Research Division, Pharmacometrics, Deerfield, IL, USA.
Gezim LahuTakeda Pharmaceuticals Research Division, Pharmacometrics, Zurich, Switzerland.
Lawrence J LeskoCenter for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA.
Mirjam N TrameCenter for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave

Indexed as

Models, BiologicalAdultAnti-Obesity AgentsBody WeightDouble-Blind MethodFemaleHumansMaleMiddle AgedObesityPatient DropoutsAnti-Obesity AgentsClinical TrialsContrave®Markov ModelNonlinear Mixed-Effect ModelsNONMEMObesityPopulation Pharmacodynamics

Identifiers

PMID28858397
PMCPMC5811797

What OpenQuestion holds

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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.