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The “RCT augmentation”: a novel simulation method to add patient heterogeneity into phase III trials

Overview of attention for article published in BMC Medical Research Methodology, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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1 blog
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3 X users
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1 patent

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2 Dimensions

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Title
The “RCT augmentation”: a novel simulation method to add patient heterogeneity into phase III trials
Published in
BMC Medical Research Methodology, July 2018
DOI 10.1186/s12874-018-0534-6
Pubmed ID
Authors

Helene Karcher, Shuai Fu, Jie Meng, Mikkel Zöllner Ankarfeldt, Orestis Efthimiou, Mark Belger, Josep Maria Haro, Lucien Abenhaim, Clementine Nordon, on behalf of the GetReal Consortium Work Package 2

Abstract

Phase III randomized controlled trials (RCT) typically exclude certain patient subgroups, thereby potentially jeopardizing estimation of a drug's effects when prescribed to wider populations and under routine care ("effectiveness"). Conversely, enrolling heterogeneous populations in RCTs can increase endpoint variability and compromise detection of a drug's effect. We developed the "RCT augmentation" method to quantitatively support RCT design in the identification of exclusion criteria to relax to address both of these considerations. In the present manuscript, we describe the method and a case study in schizophrenia. We applied typical RCT exclusion criteria in a real-world dataset (cohort) of schizophrenia patients to define the "RCT population" subgroup, and assessed the impact of re-including each of the following patient subgroups: (1) illness duration 1-3 years; (2) suicide attempt; (3) alcohol abuse; (4) substance abuse; and (5) private practice management. Predictive models were built using data from different "augmented RCT populations" (i.e., subgroups where patients with one or two of such characteristics were re-included) to estimate the absolute effectiveness of the two most prevalent antipsychotics against real-world results from the entire cohort. Concurrently, the impact on RCT results of relaxing exclusion criteria was evaluated by calculating the comparative efficacy of those two antipsychotics in virtual RCTs drawing on different "augmented RCT populations". Data from the "RCT population", which was defined with typical exclusion criteria, allowed for a prediction of effectiveness with a bias < 2% and mean squared error (MSE) = 5.8-6.8%. Compared to this typical RCT, RCTs using augmented populations provided improved effectiveness predictions (bias < 2%, MSE = 5.3-6.7%), while returning more variable comparative effects. The impact of augmentation depended on the exclusion criterion relaxed. Furthermore, half of the benefit of relaxing each criterion was gained from re-including the first 10-20% of patients with the corresponding real-world characteristic. Simulating the inclusion of real-world subpopulations into an RCT before running it allows for quantification of the impact of each re-inclusion upon effect detection (statistical power) and generalizability of trial results, thereby explicating this trade-off and enabling a controlled increase in population heterogeneity in the RCT design.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 18%
Researcher 8 13%
Student > Bachelor 6 10%
Student > Doctoral Student 5 8%
Other 4 7%
Other 9 15%
Unknown 17 28%
Readers by discipline Count As %
Medicine and Dentistry 13 22%
Psychology 10 17%
Nursing and Health Professions 4 7%
Biochemistry, Genetics and Molecular Biology 3 5%
Social Sciences 3 5%
Other 5 8%
Unknown 22 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 25 February 2021.
All research outputs
#2,449,508
of 23,094,276 outputs
Outputs from BMC Medical Research Methodology
#383
of 2,035 outputs
Outputs of similar age
#52,523
of 327,716 outputs
Outputs of similar age from BMC Medical Research Methodology
#7
of 40 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,035 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 327,716 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.