In [1,2] we defined a new measure of variable importance of an exposure on a continuous outcome, accounting for potential confounders, when the exposure features a reference level x0 with positive mass and a continuum of other levels. We also showed how to build asymptotic confidence intervals for it, using the semi-parametric estimation methodology called targeted minimum loss estimation methodology (TMLE) [3,4,5]. In the application, which motivated the study, the exposure and response are, respectively, the DNA copy number and expression level of a given gene in a cancer cell. The reference level is $$x_0=2$$, that is the expected DNA copy number in a normal cell. As for the confounder, it is a measure of the methylation of the gene.

This two-part talk will present and discuss the theoretical, computational and applied facets of the study.


[1] Chambaz, Neuvial and van der Laan, Estimation of a non-parametric variable importance measure of a continuous exposure, Electron. J. Stat., 6:1059-1099 (2012)
[2] Chambaz and Neuvial, tmle.npvi: targeted, integrative search of associations between DNA copy number and gene expression, accounting for DNA methylation, Bioinformatics, 31(18):3054-3056 (2015)
[3] van der Laan and Rubin, Targeted maximum likelihood learning, Int. J. Biostat., 2:Article 11 (2006)
[4] van der Laan and Rose, Targeted Learning: Causal Inference for Observational and Experimental Data, Springer Verlag (2011)
[5] van der Laan and Rose, [Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies, Springer Verlag (2018)

This event will be streamed via Webex (go to https://shortest.link/4imF and enter meeting number (access code): 2741 068 2978 and passcode: rJUJTsf4r68)

Or join us physically: room 2011 at LISN Campus Universitaire Bâtiment 660 Claude Shannon. Rue René Thom, 91190 Gif-sur-Yvette.