Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Software For 'The Predictive Hazard Ratio For Biomarker Evaluation Studies', Debashis Ghosh
Software For 'The Predictive Hazard Ratio For Biomarker Evaluation Studies', Debashis Ghosh
Debashis Ghosh
This is software to accompany the paper `The predictive hazard ratio for biomarker evaluation studies.' It is saved as a compressed ZIP folder.
The Predictive Hazard Ratio For Biomarker Evaluation Studies, Debashis Ghosh
The Predictive Hazard Ratio For Biomarker Evaluation Studies, Debashis Ghosh
Debashis Ghosh
There is tremendous scientific and medical interest in the use of biomarkers to better facilitate medical decision making. In this article, we present a simple framework for assessing the predictive ability of a biomarker. The methodology requires use of techniques from a subfield of survival analysis termed semicompeting risks; results are presented to make the article self-contained. A crucial parameter for evaluating is the predictive hazard ratio, which is different from the usual hazard ratio from Cox regression models for right-censored data. This quantity will be defined; its estimation, inference and adjustment for covariates will be discussed. Aspects of censoring …
Penalized Regression Procedures For Variable Selection In The Potential Outcomes Framework, Debashis Ghosh, Yeying Zhu, Donna L. Coffman
Penalized Regression Procedures For Variable Selection In The Potential Outcomes Framework, Debashis Ghosh, Yeying Zhu, Donna L. Coffman
Debashis Ghosh
A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple `impute, then select' class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model, and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data and imputation …