Analysis of cure rate survival data under proportional odds model

Due to significant progress in cancer treatments and management in survival studies involving time to relapse (or death), we often need survival models with cured fraction to account for the subjects enjoying prolonged survival. Our article presents a new proportional odds survival models with a cured fraction using a special hierarchical structure of the latent factors activating cure. This new model has same important differences with classical proportional odds survival models and existing cure-rate survival models. We demonstrate the implementation of Bayesian data analysis using our model with data from the SEER (Surveillance Epidemiology and End Results) database of the National Cancer Institute. Particularly aimed at survival data with cured fraction, we present a novel Bayes method for model comparisons and assessments, and demonstrate our new tool’s superior performance and advantages over competing tools.     

Managing Quality in the E‐Service System: Development and Application of a Process Model

In this paper, we develop a process model for assessing and managing e‐service quality based on the underlying components of the e‐service system and, in turn, address the growing need to look in more detail at the system component level for sources of poor quality. The proposed process model is comprised of a set of entities representing the e‐service system, a network defining the linking between all pairs of entities via transactions and product flows, and a set of outcomes of the processes in terms of quality dimensions. The process model is developed using Unified Modeling Language (UML), a pictorial language for specifying service designs that has achieved widespread acceptance among e‐service designers. Examples of applications of the process model are presented to illustrate how the model can be use to identify operational levers for managing and improving e‐service quality.     

The effect of child support enforcement on abortion in the United States

Objectives: This project aims to answer a critically important question of public policy: Does effective child support enforcement lead to a change in the incidence of abortion across the United States? Methods: Using state-level data collected from 1978–2003 from a variety of sources, we employ fixed effects regression analysis to examine whether financial security as measured by five types of child support enforcement effectiveness impacts abortion outcomes. Results: We find that child support enforcement effectiveness decreases the incidence of abortion as measured by the abortion rate, but not the abortion ratio. Conclusions: Income transfer policies such as child support enforcement can affect certain fertility outcomes such as abortion rates across the states.     

Bootstrap tests for variance components in generalized linear mixed models

In many applications of generalized linear mixed models to clustered correlated or longitudinal data, often we are interested in testing whether a random effects variance component is zero. The usual asymptotic mixture of chi‐square distributions of the score statistic for testing constrained variance components does not necessarily hold. In this article, the author proposes and explores a parametric bootstrap test that appears to be valid based on its estimated level of significance under the null hypothesis. Results from a simulation study indicate that the bootstrap test has a level much closer to the nominal one while the asymptotic test is conservative, and is more powerful than the usual asymptotic score test based on a mixture of chi‐squares. The proposed bootstrap test is illustrated using two sets of real‐life data obtained from clinical trials. The Canadian Journal of Statistics © 2009 Statistical Society of Canada     

On the consistency between model-and design-based estimators in survey sampling

In finite population sampling, often a distinction is made between model-and design-based estimators of the parameters of interest (like the population total, population variance, etc.). The model-based estimators depend on the (known) parameters of the model, while the design-based estimators depend on the (known) selection probabilities of the different units in the population. It is shown in this paper that the two approaches are not necessarily incompatible, and indeed can often lead to the same estimator. Our ideas are illustrated with the Horvitz-Thompson, and the generalized Horvitz-Thompson estimator. These estimators are identified as hierarchical Bays estimators. Also, certain “stepwise-Bayes” estimators of Vardeman and Meeden (J. Stat. Inf. (1983), V7, pp 329-341) are unified from a hierarchical Bayes point of view.     

An estimated‐score approach for dealing with missing covariate data in matched case–control studies

Matched case–control designs are commonly used in epidemiological studies for estimating the effect of exposure variables on the risk of a disease by controlling the effect of confounding variables. Due to retrospective nature of the study, information on a covariate could be missing for some subjects. A straightforward application of the conditional logistic likelihood for analyzing matched case–control data with the partially missing covariate may yield inefficient estimators of the parameters. A robust method has been proposed to handle this problem using an estimated conditional score approach when the missingness mechanism does not depend on the disease status. Within the conditional logistic likelihood framework, an empirical procedure is used to estimate the odds of the disease for the subjects with missing covariate values. The asymptotic distribution and the asymptotic variance of the estimator when the matching variables and the completely observed covariates are categorical. The finite sample performance of the proposed estimator is assessed through a simulation study. Finally, the proposed method has been applied to analyze two matched case–control studies. The Canadian Journal of Statistics 38: 680–697; 2010 © 2010 Statistical Society of Canada     

Principal component analysis of interrelations in stored-wheat ecosystems infested with multiple species of insects

Summary Principal component analysis (PCA) was used to determine the effects of infestation of bulk stored wheat by multiple species of insects at 30±2°C for 60 wk. Eight 204-liter drums containing wheat at 15.5% moisture content were used as three distinct man-made ecosystems: (a) Control system (2 drums), insect-free; (b) RST system (3 drums), artificially infested withRhyzopertha dominica (F.),Sitophilus oryzae (L.), andTribolium castaneum (Herbst); and (c) COT system (3 drums), infested withCryptolestes ferrugineus (Stephens),Oryzaephilus surinamensis (L.), andTribolium castaneum. The variables measured tri-weekly within each system included carbon dioxide, oxygen, temperature, grain moisture, seed damage, grain weight and volume, dust weight and volume, fat acidity values (FAV) of the wheat, seed germination, microflora includingAlternaria alternata (Fr.) Keissler,Aspergillus glaucus group,Aspergillus candidus Link, and bacteria, insects and the miteTarsonemus granarius Lindquist. PCA provided multivariate synopsis of the data quantifying several important relationships among the variables monitored. Tri-weekly and cumulative 60-wk analyses of each system showed that high bacterial counts were associated with high FAV levels;Tarsonemus numbers were positively related toAspergillus; Alternaria and seed germination were negatively related to FAV, bacteria and grain damage; and that the number of insects was related to the presence ofAspergillus and negatively related to the presence of bacteria. Seed germination andAlternaria infection often decrease rapidly presumably because of infection by fungi of theAspergillus glaucus group. The combined action ofR. dominica andAspergillus spp. enhanced seed damage and increased grain moisture content thus promoting bacterial growth which in turn inhibited insect and mold growth. Fat acidity values increased with time unless seed damage and bacterial infection were extensive as in the RST system. Contribution No. 903, Agriculture Canada, Research Station, 195 Dafoe Road, Winnipeg, Manitoba, R3T 2M9.     

Minimax weights for generalised M‐estimation in biased regression models

The authors consider the construction of weights for Generalised M‐estimation. Such weights, when combined with appropriate score functions, afford protection from biases arising through incorrectly specified response functions, as well as from natural variation. The authors obtain minimax fixed weights of the Mallows type under the assumption that the density of the independent variables is correctly specified, and they obtain adaptive weights when this assumption is relaxed. A simulation study indicates that one can expect appreciable gains in precision when the latter weights are used and the various sources of model uncertainty are present.     

Estimation of variance based on a ranked set sample

In this paper we examine the problem of the estimation of the variance σ² of a population based on a ranked set sample (RSS) from a nonparametric point of view. It is well known that based on a single cycle RSS, there does not exist an unbiased estimate of σ². We show that for more than one cycle, it is possible to construct a class of quadratic unbiased estimates of σ² in both balanced and unbalanced cases. Moreover, a minimum variance unbiased quadratic nonnegative estimate of σ² within a certain class of quadratic estimates is derived.     

Robust sequential designs for nonlinear regression

The authors introduce the formal notion of an approximately specified nonlinear regression model and investigate sequential design methodologies when the fitted model is possibly of an incorrect parametric form. They present small‐sample simulation studies which indicate that their new designs can be very successful, relative to some common competitors, in reducing mean squared error due to model misspecifi‐cation and to heteroscedastic variation. Their simulations also suggest that standard normal‐theory inference procedures remain approximately valid under the sequential sampling schemes. The methods are illustrated both by simulation and in an example using data from an experiment described in the chemical engineering literature.