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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 …
A Data-Adaptive Strategy For Inverse Weighted Estimation Of Causal Effects, Yeying Zhu, Debashis Ghosh, Bhramar Mukherjee, Nandita Mitra
A Data-Adaptive Strategy For Inverse Weighted Estimation Of Causal Effects, Yeying Zhu, Debashis Ghosh, Bhramar Mukherjee, Nandita Mitra
Debashis Ghosh
In most nonrandomized observational studies, differences between treatment groups may arise not only due to the treatment but also because of the effect of confounders. Therefore, causal inference regarding the treatment effect is not as straightforward as in a randomized trial. To adjust for confounding due to measured covariates, the average treatment effect is often estimated by using propensity scores. In this article, we focus on the use of inverse probability weighted (IPW) estimation methods. Typically, propensity scores are estimated by logistic regression. More recent suggestions have been to employ nonparametric classification algorithms from machine learning. In this article, we …
Predictive Accuracy Of Risk Factors And Markers: A Simulation Study Of The Effect Of Novel Markers On Different Performance Measures For Logistic Regression Models, Peter C. Austin
Peter Austin
The change in c-statistic is frequently used to summarize the change in predictive accuracy when a novel risk factor is added to an existing logistic regression model. We explored the relationship between the absolute change in the c-statistic, Brier score, generalized R(2) , and the discrimination slope when a risk factor was added to an existing model in an extensive set of Monte Carlo simulations. The increase in model accuracy due to the inclusion of a novel marker was proportional to both the prevalence of the marker and to the odds ratio relating the marker to the outcome but inversely …