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Articles 1 - 7 of 7
Full-Text Articles in Medicine and Health Sciences
Discrete Nonparametric Algorithms For Outlier Detection With Genomic Data, Debashis Ghosh
Discrete Nonparametric Algorithms For Outlier Detection With Genomic Data, Debashis Ghosh
Debashis Ghosh
In high-throughput studies involving genetic data such as from gene expression mi- croarrays, dierential expression analysis between two or more experimental conditions has been a very common analytical task. Much of the resulting literature on multiple comparisons has paid relatively little attention to the choice of test statistic. In this article, we focus on the issue of choice of test statistic based on a special pattern of dierential expression. The approach here is based on recasting multiple comparisons procedures for assessing outlying expression values. A major complication is that the resulting p-values are discrete; some theoretical properties of sequential testing …
Detecting Outlier Genes From High-Dimensional Data: A Fuzzy Approach, Debashis Ghosh
Detecting Outlier Genes From High-Dimensional Data: A Fuzzy Approach, Debashis Ghosh
Debashis Ghosh
A recent nding in cancer research has been the characterization of previously undis- covered chromosomal abnormalities in several types of solid tumors. This was found based on analyses of high-throughput data from gene expression microarrays and motivated the development of so-called `outlier' tests for dierential expression. One statistical issue was the potential discreteness of the test statistics. Using ideas from fuzzy set theory, we develop fuzzy outlier detection algorithms that have links to ideas in multiple comparisons. Two- and K-sample extensions are considered. The methodology is illustrated by application to two microarray studies.
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 …