Statistical methods applied to omics data

Ternès, Nils; Arnedos, Mónica; Koscielny, Serge; Michiels, Stefan; Lanoy, Émilie

doi:10.1097/cco.0000000000000134

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…Identifying a meaningful prognostic model through high-dimensional regression raises particular challenges from a statistical point of view, including nonidentifiability of the models, instability of selected biomarkers [15], sparse model selection and multiple testing. Several penalized methods exist to perform variable selection in this high-dimensional space [16], while controlling the risk of false positives [17]. …”

Section: Prognostic Gene Signatures: the Evidence-based Path From Promentioning

confidence: 99%

Statistical controversies in clinical research: prognostic gene signatures are not (yet) useful in clinical practice

2016

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With the genomic revolution and the era of targeted therapy, prognostic and predictive gene signatures are becoming increasingly important in clinical research. They are expected to assist prognosis assessment and therapeutic decision making. Notwithstanding, an evidence-based approach is needed to bring gene signatures from the laboratory to clinical practice. In early breast cancer, multiple prognostic gene signatures are commercially available without having formally reached the highest levels of evidence-based criteria. We discuss specific concepts for developing and validating a prognostic signature and illustrate them with contemporary examples in breast cancer. When a prognostic signature has not been developed for predicting the magnitude of relative treatment benefit through an interaction effect, it may be wishful thinking to test its predictive value. We propose that new gene signatures be built specifically for predicting treatment effects for future patients and outline an approach for this using a cross-validation scheme in a standard phase III trial. Replication in an independent trial remains essential.

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Section: Prognostic Gene Signatures: the Evidence-based Path From Promentioning

confidence: 99%

Statistical controversies in clinical research: prognostic gene signatures are not (yet) useful in clinical practice

2016

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Statistical approaches for evaluating body composition markers in clinical cancer research

Bayar

Antoun

Lanoy

2017

Expert Review of Anticancer Therapy

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The term 'morphomics' stands for the markers of body composition in muscle and adipose tissues. in recent years, as part of clinical cancer research, several associations between morphomics and outcome or toxicity were found in different treatment settings leading to a growing interest. we aim to review statistical approaches used to evaluate these markers and suggest practical statistical recommendations. Area covered: We identified statistical methods used recently to take into account properties of morphomics measurements. We also reviewed adjustment methods on major confounding factors such as gender and approaches to model morphomic data, especially mixed models for repeated measures. Finally, we focused on methods for determining a cut-off for a morphomic marker that could be used in clinical practice and how to assess its robustness. Expert commentary: From our review, we proposed 13 key points to strengthen analyses and reporting of clinical research assessing associations between morphomics and outcome or toxicity.

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Utilising high-dimensional data in randomised clinical trials: A review of methods and practice

Cherlin,

Bigirumurame,

Grayling

et al. 2023

Research Methods in Medicine & Health Sciences

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Introduction Even in effectively conducted randomised trials, the probability of a successful study remains relatively low. With recent advances in the next-generation sequencing technologies, there is a rapidly growing number of high-dimensional data, including genetic, molecular and phenotypic information, that have improved our understanding of driver genes, drug targets, and drug mechanisms of action. The leveraging of high-dimensional data holds promise for increased success of clinical trials. Methods We provide an overview of methods for utilising high-dimensional data in clinical trials. We also investigate the use of these methods in practice through a review of recently published randomised clinical trials that utilise high-dimensional genetic data. The review includes articles that were published between 2019 and 2021, identified through the PubMed database. Results Out of 174 screened articles, 100 (57.5%) were randomised clinical trials that collected high-dimensional data. The most common clinical area was oncology (30%), followed by chronic diseases (28%), nutrition and ageing (18%) and cardiovascular diseases (7%). The most common types of data analysed were gene expression data (70%), followed by DNA data (21%). The most common method of analysis (36.3%) was univariable analysis. Articles that described multivariable analyses used standard statistical methods. Most of the clinical trials had two arms. Discussion New methodological approaches are required for more efficient analysis of the increasing amount of high-dimensional data collected in randomised clinical trials. We highlight the limitations and barriers to the current use of high-dimensional data in trials, and suggest potential avenues for improvement and future work.

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Statistical methods applied to omics data

Abstract: The review highlighted small sample sizes, few randomized trials and a large panel of statistical methods used in this setting. In our illustrated neoadjuvant example, causal inference methods did not outperform the penalized methods.

Cited by 4 publications

References 46 publications

Statistical controversies in clinical research: prognostic gene signatures are not (yet) useful in clinical practice

Statistical controversies in clinical research: prognostic gene signatures are not (yet) useful in clinical practice

Statistical approaches for evaluating body composition markers in clinical cancer research

Utilising high-dimensional data in randomised clinical trials: A review of methods and practice

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