Clinical trials; Random forests; Semi-parametric regression; Personalised medicine; Subgroup analyses; Regression trees
Foster Simon, Mohler-Kuo Meichun, Tay Lynette, Hothorn Torsten, Seibold Heidi (2019), Estimating Patient-specific Treatment Advantages in the ``Treatment for Adolescents with Depression Study'', in Journal of Psychiatric Research
, 112, 61-70.
Heidi Seibold, Achim Zeileis, Torsten Hothorn (2019), model4you: An R Package for Personalised Treatment Effect Estimation, in Journal of Open Research Software
Fokkema M, Smits N, Zeileis A, Hothorn T, Kelderman H (2018), Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees., in Behavior research methods
, 50(6), 2016-2034.
Thomas Marius, Bornkamp Björn, Seibold Heidi (2018), Subgroup identification in dose-finding trials via model-based recursive partitioningSubgroup identification in dose-finding trials, in Statistics in Medicine
, 37(10), 1608-1624.
Seibold Heidi, Zeileis Achim, Hothorn Torsten (2018), Individual treatment effect prediction for amyotrophic lateral sclerosis patients, in Statistical Methods in Medical Research
, 27(10), 3104-3104.
Seibold Heidi, Zeileis Achim, Hothorn Torsten (2016), Model-Based Recursive Partitioning for Subgroup Analyses., in The international journal of biostatistics
, (1), 45-63.
Seibold Heidi, Hothorn Torsten, Zeileis Achim (2016), Generalised Linear Model Trees with Global Additive Effects, in ArXiv e-prints
This research proposal aims at developing methods for inference onpatient-specific treatment effects in the context of stratified medicine.The term ``stratified medicine'' refers to methods ``using a biomarker tomatch a patient to a cohort that has exhibited a differential response to atreatment'' according to the US Food and Drug Administration report ``Pavingthe Way for Personalized Medicine'' published in 2013. Establishedstatistical procedures based on stratified or interaction models areroutinely used when both biomarkers and cohorts have already been identifiedby previous research. This proposal targets statistical procedures tailoredfor the identification of such cohorts from a larger number of potentiallyuseful biomarkers. From a conceptual point of view, we distinguish betweenthree separate steps of such an analysis: (1) the selection of one (or afew) biomarker(s) which is predictive for a differential treatment effect, (2) the identification of patient subgroups (or cohorts) from these selectedbiomarkers, and (3) the estimation of a subgroup-specific treatment effect.Established stratified or interaction models for (3) are commonly refered toas ``primary subgroup analyses'' in stratified, individualised, orpersonalised medicine. With this research plan, we propose statisticalmethods for biomarker selection and subgroup identification and improvedestimation procedures for differential treatment effects in steps (1-3).