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Medical Biomathematics and Biometrics Commons™
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Articles 1 - 12 of 12
Full-Text Articles in Medical Biomathematics and Biometrics
James-Stein Estimation And The Benjamini-Hochberg Procedure, Debashis Ghosh
James-Stein Estimation And The Benjamini-Hochberg Procedure, Debashis Ghosh
Debashis Ghosh
For the problem of multiple testing, the Benjamini-Hochberg (B-H) procedure has become a very popular method in applications. Based on a spacings theory representation of the B-H procedure, we are able to motivate the use of shrinkage estimators for modifying the B-H procedure. Several generalizations in the paper are discussed, and the methodology is applied to real and simulated datasets.
Propensity Score Modelling In Observational Studies Using Dimension Reduction Methods, Debashis Ghosh
Propensity Score Modelling In Observational Studies Using Dimension Reduction Methods, Debashis Ghosh
Debashis Ghosh
Conditional independence assumptions are very important in causal inference modelling as well as in dimension reduction methodologies. These are two very strikingly different statistical literatures, and we study links between the two in this article. The concept of covariate sufficiency plays an important role, and we provide theoretical justication when dimension reduction and partial least squares methods will allow for valid causal inference to be performed. The methods are illustrated with application to a medical study and to simulated data.
Links Between Analysis Of Surrogate Endpoints And Endogeneity, Debashis Ghosh, Jeremy M. Taylor, Michael R. Elliott
Links Between Analysis Of Surrogate Endpoints And Endogeneity, Debashis Ghosh, Jeremy M. Taylor, Michael R. Elliott
Debashis Ghosh
There has been substantive interest in the assessment of surrogate endpoints in medical research. These are measures which could potentially replace \true" endpoints in clinical trials and lead to studies that require less follow-up. Recent research in the area has focused on assessments using causal inference frameworks. Beginning with a simple model for associating the surrogate and true endpoints in the population, we approach the problem as one of endogenous covariates. An instrumental variables estimator and general two-stage algorithm is proposed. Existing surrogacy frameworks are then evaluated in the context of the model. A numerical example is used to illustrate …
Meta-Analysis For Surrogacy: Accelerated Failure Time Models And Semicompeting Risks Modelling, Debashis Ghosh, Jeremy M. Taylor, Daniel J. Sargent
Meta-Analysis For Surrogacy: Accelerated Failure Time Models And Semicompeting Risks Modelling, Debashis Ghosh, Jeremy M. Taylor, Daniel J. Sargent
Debashis Ghosh
There has been great recent interest in the medical and statistical literature in the assessment and validation of surrogate endpoints as proxies for clinical endpoints in medical studies. More recently, authors have focused on using meta-analytical methods for quanti cation of surrogacy. In this article, we extend existing procedures for analysis based on the accelerated failure time model to this setting. An advantage of this approach relative to proportional hazards model is that it allows for analysis in the semi-competing risks setting, where we constrain the surrogate endpoint to occur before the true endpoint. A novel principal components procedure is …
Spline-Based Models For Predictiveness Curves, Debashis Ghosh, Michael Sabel
Spline-Based Models For Predictiveness Curves, Debashis Ghosh, Michael Sabel
Debashis Ghosh
A biomarker is dened to be a biological characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. The use of biomarkers in cancer has been advocated for a variety of purposes, which include use as surrogate endpoints, early detection of disease, proxies for environmental exposure and risk prediction. We deal with the latter issue in this paper. Several authors have proposed use of the predictiveness curve for assessing the capacity of a biomarker for risk prediction. For most situations, it is reasonable to assume monotonicity of …
Combining Multiple Models With Survival Data: The Phase Algorithm, Debashis Ghosh, Zheng Yuan
Combining Multiple Models With Survival Data: The Phase Algorithm, Debashis Ghosh, Zheng Yuan
Debashis Ghosh
In many scientic studies, one common goal is to develop good prediction rules based on a set of available measurements. This paper proposes a model averaging methodology using proportional hazards regression models to construct new estimators of predicted survival probabilities. A screening step based on an adaptive searching algorithm is used to handle large numbers of covariates. The nite-sample properties of the proposed methodology is assessed using simulation studies. Application of the method to a cancer biomarker study is also given.
Semiparametric Analysis Of Recurrent Events: Artificial Censoring, Truncation, Pairwise Estimation And Inference, Debashis Ghosh
Semiparametric Analysis Of Recurrent Events: Artificial Censoring, Truncation, Pairwise Estimation And Inference, Debashis Ghosh
Debashis Ghosh
The analysis of recurrent failure time data from longitudinal studies can be complicated by the presence of dependent censoring. There has been a substantive literature that has developed based on an artificial censoring device. We explore in this article the connection between this class of methods with truncated data structures. In addition, a new procedure is developed for estimation and inference in a joint model for recurrent events and dependent censoring. Estimation proceeds using a mixed U-statistic based estimating function approach. New resampling-based methods for variance estimation and model checking are also described. The methods are illustrated by application to …
Direct Effect Models, Mark J. Van Der Laan, Maya L. Petersen
Direct Effect Models, Mark J. Van Der Laan, Maya L. Petersen
Maya Petersen
The causal effect of a treatment on an outcome is generally mediated by several intermediate variables. Estimation of the component of the causal effect of a treatment that is not mediated by an intermediate variable (the direct effect of the treatment) is often relevant to mechanistic understanding and to the design of clinical and public health interventions. Robins, Greenland and Pearl develop counterfactual definitions for two types of direct effects, natural and controlled, and discuss assumptions, beyond those of sequential randomization, required for the identifiability of natural direct effects. Building on their earlier work and that of others, this article …
Multiple Testing Procedures Under Confounding, Debashis Ghosh
Multiple Testing Procedures Under Confounding, Debashis Ghosh
Debashis Ghosh
While multiple testing procedures have been the focus of much statistical research, an important facet of the problem is how to deal with possible confounding. Procedures have been developed by authors in genetics and statistics. In this chapter, we relate these proposals. We propose two new multiple testing approaches within this framework. The first combines sensitivity analysis methods with false discovery rate estimation procedures. The second involves construction of shrinkage estimators that utilize the mixture model for multiple testing. The procedures are illustrated with applications to a gene expression profiling experiment in prostate cancer.
Joint Variable Selection And Classification With Immunohistochemical Data, Debashis Ghosh, Ratna Chakrabarti
Joint Variable Selection And Classification With Immunohistochemical Data, Debashis Ghosh, Ratna Chakrabarti
Debashis Ghosh
To determine if candidate cancer biomarkers have utility in a clinical setting, validation using immunohistochemical methods is typically done. Most analyses of such data have not incorporated the multivariate nature of the staining profiles. In this article, we consider modelling such data using recently developed ideas from the machine learning community. In particular, we consider the joint goals of feature selection and classification. We develop esti- mation procedures for the analysis of immunohistochemical profiles using the least absolute selection and shrinkage operator. These lead to novel and flexible models and algorithms for the analysis of compositional data. The techniques are …
An Improved Model Averaging Scheme For Logistic Regression, Debashis Ghosh, Zheng Yuan
An Improved Model Averaging Scheme For Logistic Regression, Debashis Ghosh, Zheng Yuan
Debashis Ghosh
Recently, penalized regression methods have attracted much attention in the statistical literature. In this article, we argue that such methods can be improved for the purposes of prediction by utilizing model averaging ideas. We propose a new algorithm that combines penalized regression with model averaging for improved prediction. We also discuss the issue of model selection versus model averaging and propose a diagnostic based on the notion of generalized degrees of freedom. The proposed methods are studied using both simulated and real data.
Identifying Important Explanatory Variables For Time-Varying Outcomes., Oliver Bembom, Maya L. Petersen, Mark J. Van Der Laan
Identifying Important Explanatory Variables For Time-Varying Outcomes., Oliver Bembom, Maya L. Petersen, Mark J. Van Der Laan
Maya Petersen
This chapter describes a systematic and targeted approach for estimating the impact of each of a large number of baseline covariates on an outcome that is measured repeatedly over time. These variable importance estimates can be adjusted for a user-specified set of confounders and lend themselves in a straightforward way to obtaining confidence intervals and p-values. Hence, they can in particular be used to identify a subset of baseline covariates that are the most important explanatory variables for the time-varying outcome of interest. We illustrate the methodology in a data analysis aimed at finding mutations of the human immunodeficiency virus …