Nonparametric Estimation of Survivor Function in Bivariate Competing Risk Models’

So far, in the bivariate set up, the analysis of lifetime (failure time) data with multiple causes of failure is done by treating each cause of failure separately. with failures from other causes considered as independent censoring. This approach is unrealistic in many situations. For example, in the analysis of mortality data on married couples one would be interested to compare the hazards for the same cause of death as well as to check whether death due to one cause is more important for the partners’ risk of death from other causes. In reliability analysis. one often has systems with more than one component and many systems. subsystems and components have more than one cause of failure. Design of high-reliability systems generally requires that the individual system components have extremely high reliability even after long periods of time. Knowledge of the failure behaviour of a component can lead to savings in its cost of production and maintenance and. in some cases, to the preservation of human life. For the purpose of improving reliability. it is necessary to identify the cause of failure down to the component level. By treating each cause of failure separately with failures from other causes considered as independent censoring, the analysis of lifetime data would be incomplete. Motivated by this. we introduce a new approach for the analysis of bivariate competing risk data using the bivariate vector hazard rate of Johnson and Kotz (1975).

Modelling and Analysis of Recurrent Event Data with Multiple Causes

This thesis Entitled “modelling and analysis of recurrent event data with multiple causes.Survival data is a term used for describing data that measures the time to occurrence of an event.In survival studies, the time to occurrence of an event is generally referred to as lifetime.Recurrent event data are commonly encountered in longitudinal studies when individuals are followed to observe the repeated occurrences of certain events. In many practical situations, individuals under study are exposed to the failure due to more than one causes and the eventual failure can be attributed to exactly one of these causes.The proposed model was useful in real life situations to study the effect of covariates on recurrences of certain events due to different causes.In Chapter 3, an additive hazards model for gap time distributions of recurrent event data with multiple causes was introduced. The parameter estimation and asymptotic properties were discussed .In Chapter 4, a shared frailty model for the analysis of bivariate competing risks data was presented and the estimation procedures for shared gamma frailty model, without covariates and with covariates, using EM algorithm were discussed. In Chapter 6, two nonparametric estimators for bivariate survivor function of paired recurrent event data were developed. The asymptotic properties of the estimators were studied. The proposed estimators were applied to a real life data set. Simulation studies were carried out to find the efficiency of the proposed estimators.

Age determination in individual wild-caught Drosophila serrata using pteridine concentration

Fluorescence spectrophotometry can reliably detect levels of the pteridine 6-biopterin in the heads of individual Drosophila serrata Malloch 1927. Pteridine content in both laboratory and field captured flies is typically a level of magnitude higher than the minimally detectable level (mean(lab)=0.54 units, mean(field)=0.44 units, minimum detectable level=0.01 units) and can be used to predict individual age in laboratory populations with high certainty (r(2)=57%). Laboratory studies of individuals of known age ( from 1 to 48 days old) indicate that while pteridine level increases linearly with age, they also increase in a linear manner with rearing temperature and ambient light levels, but are independent of sex. As expected, the longevity of laboratory-reared males ( at least 48 days) is higher than the range of predicted ages of wild-caught males based on individual pteridine levels (40 days). However, the predictive equation based on pteridine level alone suggested that a number of wild-caught males were less than 0 days old, and the 95% confidence for these predictions based on the inverse regression broad. The age of the oldest wild-caught male is to fall within the range of 2 to 50 days. The effects of temperature and light intensity determined in laboratory study (effect sizes omega(2)=14.3 and respectively) suggests that the calibration of the prediction equation for field populations would significantly improved when combined with fine scaled studies of habitat temperature and light conditions. ability to determine relative age in individual wild-caught D. serrata presents great opportunities for a variety evolutionary studies on the dynamics of populations.

Non-acute Transient Reversible Adrenal Insufficiency in a Survivor of Critical Illness: A Lesser-known Cause of Primary Adrenal Insufficiency

Objectives: Primary adrenal insufficiency AI is regarded as a progressive disease needing lifelong replacement therapy, but this may not always be the case. Material and methods: A non-acute presentation of AI following a hypotensive episode caused by blood loss was investigated. Results: Adrenal function fully recovered without treatment. Conclusions: There should be a high index of suspicion and a low threshold for performing tests of adrenal function in survivors of critical illness and severe hypotensive episodes.

An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.

Smoothing a Discrete Hazard Function for the Number of Patients Colonized with Methicillin-Resistance Staphylococcus Aureus in an Intensive Care Unit

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.