Cox's proportional hazard model is often t to grouped survival data (i.e., occurrence and exposure data over various speci ed time-intervals and covariate bins), as opposed to continuous data. The practical limits to using such data for inference in the Cox model are investigated. A large sample theory, allowing the bins and time-intervals to shrink as the sample size increase, is developed. It...

Let δi = 1 if the i time Yi is an observed death and δi = 0 if it was a right-censored event: That is, the individual was alive at time Yi, but was last seen at that time. If Ti (1 ≤ i ≤ n) are the true survival or failure times, then Yi = Ti if δi = 1 and Yi < Ti if δi = 0, in which case the true failure time Ti is unknown. We also assume d-dimensional covariate vectors X1, X2, . . . , Xn for ...

The Dantzig Selector is a recent approach to estimation in high-dimensional linear regression models with a large number of explanatory variables and a relatively small number of observations. As in the least absolute shrinkage and selection operator (LASSO), this approach sets certain regression coefficients exactly to zero, thus performing variable selection. However, such a framework, contra...

Variable selection in high dimensional space has challenged many contemporary statistical problems from many frontiers of scientific disciplines. Recent technological advances have made it possible to collect a huge amount of covariate information such as microarray, proteomic and SNP data via bioimaging technology while observing survival information on patients in clinical studies. Thus, the ...

High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection. In this paper we establish strong oracle properties of non-concave penalized methods for non-polynomial (NP) dimensional data with censoring in the framework of Cox's proportional h...

The Cox proportional-hazards regression model has achieved widespread use in the analysis of time-to-event data with censoring and covariates. The covariates may change their values over time. This article discusses the use of such time-dependent covariates, which offer additional opportunities but must be used with caution. The interrelationships between the outcome and variable over time can ...

A formula is derived for determining the number of observations necessary to test the equality of two survival distributions when concomitant information is incorporated. This formula should be useful in designing clinical trials with a heterogeneous patient population. Schoenfeld (1981, Biometrika 68, 316-319) derived the asymptotic power of a class of statistics used to test the equality of t...

In survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of the model assumptions is a key component. However, the presence of complicated types of censoring su...

As customers are the main asset of any organization, customer churn management is becoming a major task for organizations to retain their valuable customers. In the previous studies, the applicability and efficiency of hierarchical data mining techniques for churn prediction by combining two or more techniques have been proved to provide better performances than many single techniques over a nu...

Publications Methodological articles [1] J L Hutton. Non-negative time series models for dry river flow. [2] J L Hutton and R G Owens. Bayesian sample size calculations and prior beliefs about child sexual abuse. [5] J L Hutton and P J Solomon. Parameter orthogonality in mixed regression models for survival data. Individual patient data meta-analysis of randomised anti-epileptic drug monotherap...