Heterogeneity and varying effect in hazards regression

In the analysis of survival data, when nonproportional hazards are encountered, the Cox model is often extended to allow for a time-dependent effect by accommodating a varying coefficient. This extension, however, cannot resolve the nonproportionality caused by heterogeneity. In contrast, the heteroscedastic hazards regression (HHR) model is capable of modeling heterogeneity and thus can be applied when dealing with nonproportional hazards. In this paper, we study the application of the HHR model possibly equipped with varying coefficients. An LRR (logarithm of relative risk) plot is suggested when investigating the need to impose varying coefficients. Constancy and degeneration in the plot are used as diagnostic criteria. For the HHR model, a ‘piecewise effect’ (PE) analysis and an ‘average effect’ (AE) analysis are introduced. For the PE setting, we propose a score-type test for covariate-specific varying coefficients. The Stanford Heart Transplant data are analyzed for illustration. In the case of degeneration being destroyed by a polynomial covariate, piecewise constancy and/or monotonicity of the LRRs is considered as an alternative criterion based on the PE analysis. Finally, under the framework of the varying-coefficient HHR model, the meanings of the PE and AE analyses, along with their dynamic interpretation, are discussed.     

Gambling and Financial Stress

Social Indicators Research - In recent years, gambling harm has been considered a significant public health concern due to its increasing socioeconomic costs. Although the adverse effects of...     

Introducing the Disease Outbreak Resilience Index (DORI) Using the Demographic and Health Surveys Data from sub-Saharan Africa

Social Indicators Research - Although most studies on disease emergencies underscore the need for household readiness for shocks associated with disease outbreaks, no study to date has provided a...     

Number of matches and matched people in the birthday problem

The well known birthday problem asks for the probability of at least one match out of a group of n people. Also of interest are the number of matches and the number of matched people. In this paper the means and variances of the number of matches and matched people are obtained. A generalization of the use of these methods to computer storage analysis is discussed.     

Just Who Are You Calling Risk Averse?

This paper estimates individual risk preferences based upon data that are generated by the same individuals acting in different institutions. The results show that the (estimated) numerical values of individuals' implied risk parameters are not stable within individuals across institutions. Furthermore, the ranking across subjects of the numerical values of individuals' implied risk parameters is not preserved across institutions.