Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material.     

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.     

Health Care Supply Chain Design: Toward Linking the Development and Delivery of Care Globally*

This article is motivated by the gap between the growing demand and available supply of high‐quality, cost‐effective, and timely health care, a problem faced not only by developing and underdeveloped countries but also by developed countries. The significance of this problem is heightened when the economy is in recession. In an attempt to address the problem, in this article, first, we conceptualize care as a bundle of goods, services, and experiences—including diet and exercise, drugs, devices, invasive procedures, new biologics, travel and lodging, and payment and reimbursement. We then adopt a macro, end‐to‐end, supply chain–centric view of the health care sector to link the development of care with the delivery of care. This macro, supply chain–centric view sheds light on the interdependencies between key industries from the upstream to the downstream of the health care supply chain. We propose a framework, the 3A‐framework, that is founded on three constructs—affordability, access, and awareness—to inform the design of supply chain for the health care sector. We present an illustrative example of the framework toward designing the supply chain for implantable device–based care for cardiovascular diseases in developing countries. Specifically, the framework provides a lens for identifying an integrated system of continuous improvement and innovation initiatives relevant to bridging the gap between the demand and supply for high‐quality, cost‐effective, and timely care. Finally, we delineate directions of future research that are anchored in and follow from the developments documented in the article.     

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.     

Applying Process Knowledge for Yield Variation Reduction: A Longitudinal Field Study*

The widespread recognition of the detrimental effects of high yield variation in advanced manufacturing technology settings, both in terms of cost and management of production processes, underscores the need to develop effective strategies for reducing yield variation. In this article, we report the findings of a longitudinal field study in an electromechanical motor assembly plant where we examined how the application of process knowledge by production work teams can reduce yield variation. We propose and provide an operationalizion of a strategy to identify the sequence of particular types of actions—actions to control the mean followed by actions to control the variance—that work teams should pursue over time to apply process knowledge for reducing yield variation. The results of our empirical analysis show that yield variation was significantly reduced on three of the four production lines at the manufacturing plant that served as our research site. Differences in strategies for applying process knowledge help explain the different results on each of the production lines.     

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.     

Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data

Summary.  In a large, prospective longitudinal study designed to monitor cardiac abnormalities in children born to women who are infected with the human immunodeficiency virus, instead of a single outcome variable, there are multiple binary outcomes (e.g. abnormal heart rate, abnormal blood pressure and abnormal heart wall thickness) considered as joint measures of heart function over time. In the presence of missing responses at some time points, longitudinal marginal models for these multiple outcomes can be estimated by using generalized estimating equations (GEEs), and consistent estimates can be obtained under the assumption of a missingness completely at random mechanism. When the missing data mechanism is missingness at random, i.e. the probability of missing a particular outcome at a time point depends on observed values of that outcome and the remaining outcomes at other time points, we propose joint estimation of the marginal models by using a single modified GEE based on an EM-type algorithm. The method proposed is motivated by the longitudinal study of cardiac abnormalities in children who were born to women infected with the human immunodeficiency virus, and analyses of these data are presented to illustrate the application of the method. Further, in an asymptotic study of bias, we show that, under a missingness at random mechanism in which missingness depends on all observed outcome variables, our joint estimation via the modified GEE produces almost unbiased estimates, provided that the correlation model has been correctly specified, whereas estimates from standard GEEs can lead to substantial bias.     

Community sidelined: The loss of community focus in differential response

Differential response (DR) first emerged as one component of a child welfare paradigm that emphasized the need to engage communities in supporting families and children. However, the role of community in differential response has received little attention in recent literature. We examine the intellectual history of these ideas, tracing changes in the framing of community engagement in relation to DR over time. We find that attention to community has been sidelined by an increasingly narrow definition of DR that focuses on the existence of an alternative approach to engaging with screened in families, rather than the building of community support networks. There is currently no clear and explicit theoretical framework connecting community engagement to DR. We find that the absence of such a framework has given rise to a series of conceptual debates about the definition and purpose of DR. The development of a literature that elucidates the topic of community engagement in DR may serve to resolve some of these debates.