Correlated individual frailty: an advantageous approach to survival analysis of bivariate data

"We develop a new model of bivariate survival based on the notion of correlated individual frailty. We analyze the properties of this model and suggest a new approach to the analysis of bivariate data that does not require a parametric specification--but permits estimation--of the form of the hazard function for individuals. We empirically demonstrate the advantages of the model in the statistical analysis of bivariate data." (SUMMARY IN FRE)     

Determinants of infant and child mortality in Kenya: an analysis controlling for frailty effects

In this paper, Weibull unobserved heterogeneity (frailty) survival models are utilized to analyze the determinants of infant and child mortality in Kenya. The results of these models are compared to those of standard Weibull survival models. The study particularly examines the extent to which child survival risks continue to vary net of observed factors and the extent to which nonfrailty models are biased due to the violation of the statistical assumption of independence. The data came from the 1998 Kenya Demographic and Health Survey. The results of the standard Weibull survival models clearly show that biodemographic factors are more important in explaining infant mortality, while socioeconomic, sociocultural and hygienic factors are more important in explaining child mortality. Frailty effects are substantial and highly significant both in infancy and in childhood, but the conclusions remain the same as in the nonfrailty models.     

Individual quality of life: can it be accounted for by psychological or subjective well-being?

There is ongoing discussion in the scientific literature about the need for a more theoretical foundation to underpin quality of life (QoL) measurement. This paper applied Keyes et al.’s [J. Pers. Soc. Psychol. 82 (2002) 1007] model of well-being as a framework to assess whether respondents (n = 136 students) focus on elements of subjective well-being (SWB), such as satisfaction and happiness, or on elements of psychological well-being (PWB), such as meaning and personal growth, when making individual QoL (IQoL) judgments using the Schedue of the Evaluation of Individual Quality of Life (SEIQoL). The Keyes et al.’s model was confirmed and explained 41% of the variance in SEIQoL scores. Both SWB and PWB were correlated with the SEIQoL Index Score and SWB was found to be an important mediating variable in the relationship between PWB and SEIQoL. When analyzing different well-being combinations, respondents with high SWB/high PWB had significantly higher SEIQoL scores than did those with low SWB/low PWB. Respondents with high PWB/high SWB had higher SEIQoL scores than did those with high PWB/low SWB. Longitudinal studies in different patient groups are needed to explore the dynamic relationship between IQoL and well-being. Further investigation of the relationship between PWB and SWB with other instruments purporting to measure QoL would contribute to an enhanced understanding of the underlying nature of QoL.     

A new approach for analyzing population data: log linear models of tables of counts

The use of log linear models for analyzing historical population data is discussed. The method is applied to data from a 1788 census in order to investigate the effect of ethnicity and occupational status on male marriage patterns in Parral, a mining community in northern New Spain.     

Precision,bias, and uncertainty for state population forecasts: an exploratory analysis of time series models

Many researchers have used time series models to construct population forecasts and prediction intervals at the national level, but few have evaluated the accuracy of their forecasts or the out-of-sample validity of their prediction intervals. Fewer still have developed models for subnational areas. In this study, we develop and evaluate six ARIMA time series models for states in the United States. Using annual population estimates from 1900 to 2000 and a variety of launch years, base periods, and forecast horizons, we construct population forecasts for four states chosen to reflect a range of population size and growth rate characteristics. We compare these forecasts with population counts for the corresponding years and find precision, bias, and the width of prediction intervals to vary by state, launch year, model specification, base period, and forecast horizon. Furthermore, we find that prediction intervals based on some ARIMA models provide relatively accurate forecasts of the distribution of future population counts but prediction intervals based on other models do not. We conclude that there is some basis for optimism regarding the possibility that ARIMA models might be able to produce realistic prediction intervals to accompany population forecasts, but a great deal of work remains to be done before we can draw any firm conclusions.