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Recurrent event data with a terminal event commonly arise in many longitudinal follow‐up studies. This article proposes a class of dynamic semiparametric transformation models for the marginal mean functions of the recurrent events with a terminal event, where some covariate effects may be time‐varying. An estimation procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. In addition, relevant significance tests are suggested for examining whether or not covariate effects vary with time, and a model checking procedure is presented for assessing the adequacy of the proposed models. The finite sample performance of the proposed estimators is examined through simulation studies, and an application to a medical cost study of chronic heart failure patients is provided. [ABSTRACT FROM AUTHOR]

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]

Recurrent events often arise in follow‐up studies where a subject may experience multiple occurrences of the same event. Most regression models with recurrent events tacitly assume constant effects of covariates over time, which may not be realistic in practice. To address time‐varying effects, we develop a dynamic regression model to target the mean frequency of recurrent events. We propose an estimation procedure which fully exploits observed data. Consistency and weak convergence of the proposed estimator are established. Simulation studies demonstrate that the proposed method works well, and two real data analyses are presented for illustration. [ABSTRACT FROM AUTHOR]

Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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]

Neuroimaging data collected at repeated occasions are gaining increasing attention in the neuroimaging community due to their potential in answering questions regarding brain development, aging, and neurodegeneration. These datasets are large and complicated, characterized by the intricate spatial dependence structure of each response image, multiple response images per subject, and covariates that may vary with time. We propose a multiscale adaptive generalized method of moments (MA-GMM) approach to estimate marginal regression models for imaging datasets that contain time-varying, spatially related responses and some time-varying covariates. Our method categorizes covariates into types to determine the valid moment conditions to combine during estimation. Further, instead of assuming independence of voxels (the components that make up each subject's response image at each time point) as many current neuroimaging analysis techniques do, this method 'adaptively smoothes' neuroimaging response data, computing parameter estimates by iteratively building spheres around each voxel and combining observations within the spheres with weights. MA-GMM's development adds to the few available modeling approaches intended for longitudinal imaging data analysis. Simulation studies and an analysis of a real longitudinal imaging dataset from the Alzheimer's Disease Neuroimaging Initiative are used to assess the performance of MA-GMM. Martha Skup, Hongtu Zhu, and Heping Zhang for the Alzheimer's Disease Neuroimaging Initiative. [ABSTRACT FROM AUTHOR]

In the context of item response theory, it is not uncommon that person-by-item data are correlated beyond the correlation that is captured by the model—in other words, there is extra binomial variation. Heterogeneity of the parameters can explain this variation. There is a need for proper statistical methods to indicate possible extra heterogeneity and its location because investigating all different combinations of random parameters is not practical and sometimes even unfeasible. The ignored random person effects are the focus of this study. Considering the random weights linear logistic test model, random effects can occur as a general latent trait and as weights of covariates. A simulation study was conducted with different sources and degrees of heterogeneity to investigate and compare various methods: individual analyses (one per person), marginal modeling, principal component analysis of the raw data, DIMTEST, and DETECT. The methods are illustrated with an application on deductive reasoning. [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]