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Semiparametric, multiplicative-form regression models are specified for marginal single and double failure hazard rates for the regression analysis of multivariate failure time data. Cox-type estimating functions are specified for single and double failure hazard ratio parameter estimation, and corresponding Aalen–Breslow estimators are specified for baseline hazard rates. Generalization to allow classification of failure times into a smaller set of failure types, with failures of the same type having common baseline hazard functions, is also included. Asymptotic distribution theory arises by generalization of the marginal single failure hazard rate estimation results of Lin et al. The Péano series representation for the bivariate survival function in terms of corresponding marginal single and double failure hazard rates leads to novel estimators for pairwise bivariate survival functions and pairwise dependency functions, at specified covariate history. Related asymptotic distribution theory follows from that for the marginal single and double failure hazard rates and the continuity, compact differentiability of the Péano series transformation and bootstrap applicability. Simulation evaluation of the proposed estimation procedures is presented, and an application to multiple clinical outcomes in the Women's Health Initiative Dietary Modification Trial is provided. Higher dimensional marginal hazard rate regression modeling is briefly mentioned. for this article are available online. [ABSTRACT FROM AUTHOR]

Background: In biomedical and epidemiological studies, gene-environment (G-E) interactions play an important role in the etiology and progression of many complex diseases. In ultra-high-dimensional survival genomic data, two common approaches (marginal and joint models) are proposed to determine important interaction biomarkers. Most existing methods for detecting G-E interactions (marginal Cox model and marginal accelerated failure time model) are limited by a lack of robustness to contamination/outliers in response outcome and prediction biomarkers. In particular, right-censored survival outcomes and ultra-high-dimensional feature space make relevant feature screening even more challenging. Methods: In this paper, we utilize the non-parametric Kendall’s partial correlation method to obtain pure correlation to determine the importance of G-E interactions concerning clinical survival data under a marginal modeling framework. Results: A series of simulated scenarios are conducted to compare the performance of our proposed method (Kendall’s partial correlation) with some commonly used methods (marginal Cox’s model, marginal accelerated failure time model, and censoring quantile partial correlation approach). In real data applications, we utilize Kendall’s partial correlation method to identify G-E interactions related to the clinical survival results of patients with esophageal, pancreatic, and lung carcinomas using The Cancer Genome Atlas clinical survival genetic data, and further establish survival prediction models. Conclusions: Overall, both simulation with medium censoring level and real data studies show that our method performs well and outperforms existing methods in the selection, estimation, and prediction accuracy of main and interacting biomarkers. These applications reveal the advantages of the non-parametric Kendall’s partial correlation approach over alternative semi-parametric marginal modeling methods. We also identified the cancer-related G-E interactions biomarkers and reported the corresponding coefficients with p-values.

A novel Bayesian modeling framework for response accuracy (RA), response times (RTs) and other process data is proposed. In a Bayesian covariance structure modeling approach, nested and crossed dependences within test-taker data (e.g., within a testlet, between RAs and RTs for an item) are explicitly modeled. The local dependences are modeled directly through covariance parameters in an additive covariance matrix. The inclusion of random effects (on person or group level) is not necessary, which allows constructing parsimonious models for responses and multiple types of process data. Bayesian Covariance Structure Models (BCSMs) are presented for various well-known dependence structures. Through truncated shifted inverse-gamma priors, closed-form expressions for the conditional posteriors of the covariance parameters are derived. The priors avoid boundary effects at zero, and ensure the positive definiteness of the additive covariance structure at any layer. Dependences of categorical outcome data are modeled through latent continuous variables. In a simulation study, a BCSM for RAs and RTs is compared to van der Linden's hierarchical model (LHM; van der Linden, 2007). Under the BCSM, the dependence structure is extended to allow variations in test-takers' working speed and ability and is estimated with a satisfying performance. Under the LHM, the assumption of local independence is violated, which results in a biased estimate of the variance of the ability distribution. Moreover, the BCSM provides insight in changes in the speed-accuracy trade-off. With an empirical example, the flexibility and relevance of the BCSM for complex dependence structures in a real-world setting are discussed.

A novel Bayesian modeling framework for response accuracy (RA), response times (RTs) and other process data is proposed. In a Bayesian covariance structure modeling approach, nested and crossed dependences within test-taker data (e.g., within a testlet, between RAs and RTs for an item) are explicitly modeled. The local dependences are modeled directly through covariance parameters in an additive covariance matrix. The inclusion of random effects (on person or group level) is not necessary, which allows constructing parsimonious models for responses and multiple types of process data. Bayesian Covariance Structure Models (BCSMs) are presented for various well-known dependence structures. Through truncated shifted inverse-gamma priors, closed-form expressions for the conditional posteriors of the covariance parameters are derived. The priors avoid boundary effects at zero, and ensure the positive definiteness of the additive covariance structure at any layer. Dependences of categorical outcome data are modeled through latent continuous variables. In a simulation study, a BCSM for RAs and RTs is compared to van der Linden's hierarchical model (LHM; van der Linden, 2007). Under the BCSM, the dependence structure is extended to allow variations in test-takers' working speed and ability and is estimated with a satisfying performance. Under the LHM, the assumption of local independence is violated, which results in a biased estimate of the variance of the ability distribution. Moreover, the BCSM provides insight in changes in the speed-accuracy trade-off. With an empirical example, the flexibility and relevance of the BCSM for complex dependence structures in a real-world setting are discussed. [ABSTRACT FROM AUTHOR]

Background and Objectives: Disorders of height and weight growth are the most important health disorders affecting children younger than two in developing societies. Failure to treat these disorders can lead to the increased mortality and mental, emotional or physical disability. The objective of this study was to investigate the growth trends of children and the factors affecting it. Methods: This retrospective cohort study was conducted on 2030 children younger than two in Khorramabad, Iran who were selected using stratified and cluster sampling. Based on household records, the weight growth trend was recorded as a four-level variable (decline, stagnation, slowness and desirable) and the the height growth trend was recorded as a three-level variable (stagnation, slowness and desirable); finally, the data were modeled using a longitudinal marginal model and the SAS software version 9.2. Results: The incidence of at least one decline in the weight growth curve and one stagnation in the height growth curve was 14.2% and 10.4%, respectively. The child’s age and the maternal educational level had a significant effect on the growth trends. However, the sex, parity and the exclusive breastfeeding during the first six months had no significant relationship with the growth trends. Conclusion: Given the results and the relatively high prevalence of growth disorders among children, it seems that increasing the awareness of low literate women about feeding of the children is the most important approach to manage growth disorders. Additionally, health-care professionals should mostly focus on monitoring the growth of children older than 12 months.

Background: Height and weight growth failure are among the most important health disorders affecting children less than two in the developing countries. Failure to treat this disorder can lead to the development of serious conditions including increased mortality and types of disability. The aim of the present study is to investigate the factors affecting the height and weight growth of children less than two years old. Materials and Methods: This historical cohort study was conducted on a sample of 2030 children less than two in Khorramabad who were selected using stratified and cluster sampling methods. Based on household records, data related to the height and weight were recorded as quantitative variables and finally were modeled using a longitudinal marginal model, identity link functions and the R software. Results: Based on the marginal model, the effect of such variables as child's age, child's gender and mother's educational attainment were significantly related to the child's height and weight. Moreover the child's birth order had significant relationship with only the child's height. However, the variable of exclusive breastfeeding during the first six months was no significantly related to weight and height growth of the children. Conclusion: Given the results, it seems that monitoring the growth of the children of the illiterate mothers, specially the girls and the children with first birth orders are the most important approaches to deal with growth disorders among this population. [ABSTRACT FROM AUTHOR]

In the context of two-way contingency tables, the RC model may be seen as a way of modeling the set of log-odds ratios for adjacent 2 × 2 subtables through row and column scores. This article presents an extended class of RC models that allows simultaneous modeling of the marginal distributions and the association between two categorical variables; these components are parameterized with logits and log-odds ratios that may be of different types (e.g., global or continuation) to suit the nature of the data and the type of dependence of interest. An algorithm for maximum likelihood estimation under equality and inequality constraints is presented, the asymptotic distribution of the likelihood-ratio statistic to test some interesting hypotheses is derived, and extensions to multiway tables are outlined. Three examples are presented to highlight the features of the new approach. [ABSTRACT FROM AUTHOR]