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.     

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.     

Developing and testing a satisfaction survey for nursing home residents: the Ohio Experience

Input from consumers has become an important part of quality improvement in long-term care and for consumer decision-making. This paper documents the development of the Ohio Nursing Home Resident Satisfaction Survey (ONHRSS) through a partnership of state government, research, and industry experts. The instrument was tested and refined through two waves of data--a pretest phase and later with statewide data. Exploratory and confirmatory analyses with statewide data identified eight primary factors along with an underlying, secondary Global Satisfaction factor. Reliability of the domains ranged from .69 to .95. Recommendations for further refinement and testing of the instrument are discussed along with policy and practice implications.     

The Veils of Clio: Dimensions of a Behavioral Narratology

This paper outlines a behavior-analysis approach to the field of narratology, the study of the narrative or story, with emphasis on nonfiction stories and the processes by which such stories distort the world they describe. Stories are described in terms of (a) a behavior-analysis adaptation of Todorov''s (1977) analysis of the plots of stories in terms of states of equilibrium/disequilibrium and (b) Grant''s (2005) analysis of the structure of stories in terms of establishing operations and escape contingencies. These two sets of concepts are applied to understanding how and why stories lead to distortions of the events they report.     

Proteins' promise--progress and challenges in ovarian cancer proteomics

Ovarian cancer is the leading cause of gynaecological cancer death. The mortality rate of ovarian cancer could be greatly decreased if there were a screening test which was able to detect the disease at an early stage, resulting in an increased probability of cure. The most promising prospect for the early detection of ovarian cancer comes from the rapidly advancing field of clinical proteomics. An increasing number of reports on the potential clinical application of proteomics research for early detection as well as risk assessment and management of ovarian cancer are being published. Although the research is very promising, major technical challenges are still preventing new discoveries in ovarian cancer proteomics from being translated into clinical practice.