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A strategy to build and validate a prognostic biomarker model based on RT-qPCR gene expression and clinical covariates

Overview of attention for article published in BMC Bioinformatics, March 2015
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2 tweeters

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5 Dimensions

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Title
A strategy to build and validate a prognostic biomarker model based on RT-qPCR gene expression and clinical covariates
Published in
BMC Bioinformatics, March 2015
DOI 10.1186/s12859-015-0537-9
Pubmed ID
Authors

Maud Tournoud, Audrey Larue, Marie-Angelique Cazalis, Fabienne Venet, Alexandre Pachot, Guillaume Monneret, Alain Lepape, Jean-Baptiste Veyrieras

Abstract

Construction and validation of a prognostic model for survival data in the clinical domain is still an active field of research. Nevertheless there is no consensus on how to develop routine prognostic tests based on a combination of RT-qPCR biomarkers and clinical or demographic variables. In particular, the estimation of the model performance requires to properly account for the RT-qPCR experimental design. We present a strategy to build, select, and validate a prognostic model for survival data based on a combination of RT-qPCR biomarkers and clinical or demographic data and we provide an illustration on a real clinical dataset. First, we compare two cross-validation schemes: a classical outcome-stratified cross-validation scheme and an alternative one that accounts for the RT-qPCR plate design, especially when samples are processed by batches. The latter is intended to limit the performance discrepancies, also called the validation surprise, between the training and the test sets. Second, strategies for model building (covariate selection, functional relationship modeling, and statistical model) as well as performance indicators estimation are presented. Since in practice several prognostic models can exhibit similar performances, complementary criteria for model selection are discussed: the stability of the selected variables, the model optimism, and the impact of the omitted variables on the model performance. On the training dataset, appropriate resampling methods are expected to prevent from any upward biases due to unaccounted technical and biological variability that may arise from the experimental and intrinsic design of the RT-qPCR assay. Moreover, the stability of the selected variables, the model optimism, and the impact of the omitted variables on the model performances are pivotal indicators to select the optimal model to be validated on the test dataset.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 4%
Brazil 1 4%
Unknown 25 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 33%
Student > Ph. D. Student 4 15%
Student > Master 3 11%
Other 2 7%
Student > Postgraduate 2 7%
Other 3 11%
Unknown 4 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 22%
Agricultural and Biological Sciences 5 19%
Medicine and Dentistry 5 19%
Unspecified 1 4%
Economics, Econometrics and Finance 1 4%
Other 3 11%
Unknown 6 22%

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 May 2015.
All research outputs
#6,962,244
of 11,233,326 outputs
Outputs from BMC Bioinformatics
#2,913
of 4,193 outputs
Outputs of similar age
#116,110
of 209,015 outputs
Outputs of similar age from BMC Bioinformatics
#105
of 132 outputs
Altmetric has tracked 11,233,326 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
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