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The use of random-effects models to identify health care center-related characteristics modifying the effect of antipsychotic drugs

Overview of attention for article published in Clinical Epidemiology, December 2017
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Title
The use of random-effects models to identify health care center-related characteristics modifying the effect of antipsychotic drugs
Published in
Clinical Epidemiology, December 2017
DOI 10.2147/clep.s145353
Pubmed ID
Authors

Clementine Nordon, Constance Battin, Helene Verdoux, Josef Maria Haro, Mark Belger, Lucien Abenhaim, Tjeerd Pieter van Staa

Abstract

A case study was conducted, exploring methods to identify drugs effects modifiers, at a health care center level. Data were drawn from the Schizophrenia Outpatient Health Outcome cohort, including hierarchical information on 6641 patients, recruited from 899 health care centers from across ten European countries. Center-level characteristics included the following: psychiatrist's gender, age, length of practice experience, practice setting and type, countries' Healthcare System Efficiency score, and psychiatrist density in the country. Mixed multivariable linear regression models were used: 1) to estimate antipsychotic drugs' effectiveness (defined as the association between patients' outcome at 3 months - dependent variable, continuous - and antipsychotic drug initiation at baseline - drug A vs other antipsychotic drug); 2) to estimate the similarity between clustered data (using the intra-cluster correlation coefficient); and 3) to explore antipsychotic drug effects modification by center-related characteristics (using the addition of an interaction term). About 23% of the variance found for patients' outcome was explained by unmeasured confounding at a center level. Psychiatrists' practice experience was found to be associated with patient outcomes (p=0.04) and modified the relative effect of "drug A" (p<0.001), independent of center- or patient-related characteristics. Mixed models may be useful to explore how center-related characteristics modify drugs' effect estimates, but require numerous assumptions.

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Mendeley readers

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The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 32%
Student > Master 4 18%
Student > Doctoral Student 3 14%
Student > Bachelor 2 9%
Professor 1 5%
Other 2 9%
Unknown 3 14%
Readers by discipline Count As %
Psychology 4 18%
Social Sciences 3 14%
Medicine and Dentistry 3 14%
Nursing and Health Professions 2 9%
Immunology and Microbiology 1 5%
Other 2 9%
Unknown 7 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 January 2018.
All research outputs
#18,591,506
of 23,028,364 outputs
Outputs from Clinical Epidemiology
#577
of 727 outputs
Outputs of similar age
#325,866
of 438,018 outputs
Outputs of similar age from Clinical Epidemiology
#28
of 33 outputs
Altmetric has tracked 23,028,364 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 727 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.4. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.