Power analysis for clustered non-continuous responses in multicenter trials

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.     

Unified multivariate survival model with a surviving fraction: an application to a Brazilian customer churn data

In this paper we propose a new lifetime model for multivariate survival data in presence of surviving fractions and examine some of its properties. Its genesis is based on situations in which there are m types of unobservable competing causes, where each cause is related to a time of occurrence of an event of interest. Our model is a multivariate extension of the univariate survival cure rate model proposed by Rodrigues et al. [37 J. Rodrigues, V.G. Cancho, M. de Castro, and F. Louzada-Neto, On the unification of long-term survival models, Statist. Probab. Lett. 79 (2009), pp. 753–759. doi: 10.1016/j.spl.2008.10.029[Crossref], [Web of Science ®] , [Google Scholar]]. The inferential approach exploits the maximum likelihood tools. We perform a simulation study in order to verify the asymptotic properties of the maximum likelihood estimators. The simulation study also focus on size and power of the likelihood ratio test. The methodology is illustrated on a real data set on customer churn data.     

Investigating impacts of complex sampling on latent growth curve modelling

We investigate the impacts of complex sampling on point and standard error estimates in latent growth curve modelling of survey data. Methodological issues are illustrated with empirical evidence from the analysis of longitudinal data on life satisfaction trajectories using data from the British Household Panel Survey, a national representative survey in Great Britain. A multi-process second-order latent growth curve model with conditional linear growth is used to study variation in the two perceived life satisfaction latent factors considered. The benefits of accounting for the complex survey design are considered, including obtaining unbiased both point and standard error estimates, and therefore correctly specified confidence intervals and statistical tests. We conclude that, even for the rather elaborated longitudinal data models that were considered, estimation procedures are affected by variance-inflating impacts of complex sampling.     

Home study methods for evaluating prospective resource families: history, current challenges, and promising approaches

Every state requires a home study before the placement of foster children for adoption. This article examines the history of home studies, presents results from expert interviews on the changing processes and purposes of home studies, and explores current challenges for the field. The article also introduces the Structured Analysis Family Evaluation (SAFE), a uniform home study format that encourages consistent family evaluations across workers, agencies, and jurisdictions. The article clarifies how SAFE may address challenges facing foster care and adoption practice.     

Do hostile sexual beliefs affect men's perceptions of sexual-interest messages?

This study tested the distorted cognitions component of Hall and Hirschman's (1991) quadripartite model of sexual aggression. Men (N = 107) with and without hostile sexual beliefs viewed erotic slides with a female partner who provided one of four patterns of feedback: clear disinterest, token disinterest, compliant interest, and clear interest. Hostile men reported fewer differences between women, were unresponsive to their partner's perceived embarrassment, and reported a consistent positive mood regardless of her feedback. Conversely, nonhostile men were more responsive to feedback, mirrored the partner's embarrassment, and experienced a less positive mood when she communicated clear disinterest in the erotica. These findings support the distorted cognitions component of the quadripartite model of sexual aggression. The authors also discuss the strengths and limitations of this study's methodology.