Survival analysis of time-to-event data in respiratory health research studies

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan–Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

SAS macros for point and interval estimation of area under the receiver operating characteristic curve for non-proportional and proportional hazards Weibull models

Aims and objectives  For prediction of risk of cardiovascular end points using survival models the proportional hazards assumption is often not met. Thus, non-proportional hazards models are more appropriate for developing risk prediction equations in such situations. However, computer program for evaluating the prediction performance of such models has been rarely addressed. We therefore developed SAS macro programs for evaluating the discriminative ability of a non-proportional hazards Weibull model developed by Anderson (1991) and that of a proportional hazards Weibull model using the area under receiver operating characteristic (ROC) curve.

Method  Two SAS macro programs for non-proportional hazards Weibull model using Proc NLIN and Proc NLP respectively and model validation using area under ROC curve (with its confidence limits) were written with SAS IML language. A similar SAS macro for proportional hazards Weibull model was also written.

Results  The computer program was applied to data on coronary heart disease incidence for a Framingham population cohort. The five risk factors considered were current smoking, age, blood pressure, cholesterol and obesity. The predictive ability of the non-proportional hazard Weibull model was slightly higher than that of its proportional hazard counterpart. An advantage of SAS Proc NLP in terms of the example provided here is that it provides significance level for the parameter estimates whereas Proc NLIN does not.

Conclusion  The program is very useful for evaluating the predictive performance of non-proportional and proportional hazards Weibull models.

Parametric conditional frailty models for recurrent cardiovascular events in the lipid study

Background Analysis of recurrent event data is frequently needed in clinical and epidemiological studies. An important issue in such analysis is how to account for the dependence of the events in an individual and any unobserved heterogeneity of the event propensity across individuals.Methods We applied a number of conditional frailty and nonfrailty models in an analysis involving recurrent myocardial infarction events in the Long-Term Intervention with Pravastatin in Ischaemic Disease study. A multiple variable risk prediction model was developed for both males and females. Results A Weibull model with a gamma frailty term fitted the data better than other frailty models for each gender. Among nonfrailty models the stratified survival model fitted the data best for each gender. The relative risk estimated by the elapsed time model was close to that estimated by the gap time model. We found that a cholesterol-lowering drug, pravastatin (the intervention being tested in the trial) had significant protective effect against the occurrence of myocardial infarction in men (HR¼0.71, 95% CI0.60–0.83). However, the treatment effect was not significant in women due to smaller sample size (HR¼0.75, 95% CI 0.51–1.10). There were no significant interactions between the treatment effect and each recurrent MI event (p¼0.24 for men and p¼0.55 for women). The risk of developing an MI event for a male who had an MI event during follow-up was about 3.4 (95% CI 2.6–4.4) times the risk compared with those who did not have an MI event. The corresponding relative risk for a female was about 7.8 (95% CI 4.4–13.6). Limitations The number of female patients was relatively small compared with their male counterparts, which may result in low statistical power to find real differences in the effect of treatment and other potential risk factors.Conclusions The conditional frailty model suggested that after accounting for all the risk factors in the model, there was still unmeasured heterogeneity of the risk for myocardial infarction, indicating the effect of subject-specific risk factors. These risk prediction models can be used to classify cardiovascular disease patients into different risk categories and may be useful for the most effective targeting of preventive therapies for cardiovascular disease.

How precarious employment affects health and safety at work : the case of temporary agency workers

International studies indicate temporary agency workers are more likely to be injured at work than other types of employees. However explanations for this have been less forthcoming. This paper seeks to begin filling this gap. A study was undertaken in Victoria, Australia, of occupational health and safety (OHS) amongst temporary agency workers drawing upon workers' compensation claim files for injured agency and directly hired workers from 1995-2001, and focus groups of temporary agency workers conducted in 2003. In analyzing the results, use was made of risk factors identified in a model that has been developed to explain how precarious employment affects OHS--the pressure, disorganization and regulatory failure (PDR) model (Quinlan and Bohle, 2004, 2009). Drawing principally on qualitative data, the paper finds that whilst agency workers share common risk factors with other forms of precarious workers, unique characteristics associated with the triangular nature of agency employment heighten their vulnerability further.

Modelling grouped survival times in toxicological studies using generalized additive models

A method for combining a proportional-hazards survival time model with a bioassay model where the log-hazard function is modelled as a linear or smoothing spline function of log-concentration combined with a smoothing spline function of time is described. The combined model is fitted to mortality numbers, resulting from survival times that are grouped due to a common set of observation times, using Generalized Additive Models (GAMs). The GAM fits mortalities as conditional binomials using an approximation to the log of the integral of the hazard function and is implemented using freely-available, general software for fitting GAMs. Extensions of the GAM are described to allow random effects to be fitted and to allow for time-varying concentrations by replacing time with a calibrated cumulative exposure variable with calibration parameter estimated using profile likelihood. The models are demonstrated using data from a studies of a marine and a, previously published, freshwater taxa. The marine study involved two replicate bioassays of the effect of zinc exposure on survival of an Antarctic amphipod, Orchomenella pinguides. The other example modelled survival of the daphnid, Daphnia magna, exposed to potassium dichromate and was fitted by both the GAM and the process-based DEBtox model. The GAM fitted with a cubic regression spline in time gave a 61 % improvement in fit to the daphnid data compared to DEBtox due to a non-monotonic hazard function. A simulation study using each of these hazard functions as operating models demonstrated that the GAM is overall more accurate in recovering lethal concentration values across the range of forms of the underlying hazard function compared to DEBtox and standard multiple endpoint probit analyses.

FEA simulation of contact and wear between non-metallic materials for accelerated new product development

The conventional approach ie laboratory life testing to examine the reliability of products takes long time and involves tremendous cost as samples are tested till failures. The accelerated life test (ALT) has recently been used as an alternative method. Although ALT reduces the cost of reliability testing through applying more severe environmental conditions than the normal ones, it is no longer sufficient as it does not describe the process of products’ failure explicitly and it is still highly dependent on physical testing. Consequently, novel practices need to be developed for better understanding of the products’ reliability. A novel Finite Element Analysis (FEA) model incorporating mathematical wear equations is developed in the current work and applied to polymer materials. Wear rate, a key parameter, is calculated by using a combinatorial formula that combines a conventional linear equation with a recently published exponential equation. The local wear is firstly calculated and then integrated over the sliding distance. The FEA simulation works in a loop and performs a series of simulation with updated surface geometries. The simulation is in good agreement with the physical testing result.

A stochastic time-domain model for burst data aggregation in IEEE 802.15.4 wireless sensor networks

In many network applications, the nature of traffic is of burst type. Often, the transient response of network to such traffics is the result of a series of interdependant events whose occurrence prediction is not a trivial task. The previous efforts in IEEE 802.15.4 networks often followed top-down approaches to model those sequences of events, i.e., through making top-view models of the whole network, they tried to track the transient response of network to burst packet arrivals. The problem with such approaches was that they were unable to give station-level views of network response and were usually complex. In this paper, we propose a non-stationary analytical model for the IEEE 802.15.4 slotted CSMA/CA medium access control (MAC) protocol under burst traffic arrival assumption and without the optional acknowledgements. We develop a station-level stochastic time-domain method from which the network-level metrics are extracted. Our bottom-up approach makes finding station-level details such as delay, collision and failure distributions possible. Moreover, network-level metrics like the average packet loss or transmission success rate can be extracted from the model. Compared to the previous models, our model is proven to be of lower memory and computational complexity order and also supports contention window sizes of greater than one. We have carried out extensive and comparative simulations to show the high accuracy of our model.

A model of online teaching and learning

Deakin University has a long history of supporting distance education with technology. Such presentations have matured from a mix of remote-login/FTP/email in the 1980s through web mediated access in the 1990s to institution-wide learning management systems which are emerging currently. By the commencement of semester I, 2003, online teaching at the University will be supported by a single, institution-wide, learning management system, which is expected to support approximately 28,000 students, who will each be accessing up to 4 of some 1500 undergraduate and 700 postgraduate courses. In this paper we describe a model for online teaching of both on-campus and off-campus students in the Bachelor of Computing, using various technologies to support different aspects of online teaching and learning. This programme has been running in a web-mediated environment for over six years. Each year the administration of the programme has been modified in a reactive manner, based on student feedback and the identification of failure points during the previous semester, resulting in the model maturing over that time. We discuss how the changes have impacted the model, the academics involved in the teaching of the unit and the students' experience of learning in the online environment. We also discuss the advantages and disadvantages of online teaching and learning, as well as some potential pitfalls and how to avoid them, or, at least, minimise their impact.

The brachyspira hyodysenteriae ftnA gene : DNA vaccination and real-time PCR quantification of bacteria in a mouse model of disease

The nucleotide sequence of the Brachyspira hyodysenteriae ftnA gene, encoding a putative ferritin protein (FtnA), was determined. Analysis of the sequence predicted that this gene encoded a protein of 180 amino acids. RT-PCR and Western blot showed that the ftnA gene was expressed in B. hyodysenteriae, and evidence suggests that FtnA stores iron rather than haem. ftnA was delivered as DNA and recombinant protein vaccines in a mouse model of B. hyodysenteriae infection. Vaccine efficacy was monitored by caecal pathology and quantification of B. hyodysenteriae numbers in the caeca of infected mice by real-time PCR.

Time for a change : applying the Good Lives Model of rehabilitation to a high-risk violent offender

In this article we operationalise the theoretical concepts of the Good Lives Model (GLM) of offender rehabilitation by providing a step-by-step framework for assessment, formulation, treatment planning, and monitoring with a high-risk violent offender residing in the community. The case study illustrates how the GLM can be applied to complement and enhance traditional Risk-Management interventions and shows how the GLM's clinical relevance extends from sex offending to broader offending typologies.

Damage identification of civil infrastructure in the time domain using FEM calibrated ARMAX model

Due to environmental loads, mechanical damages, structural aging and human factors, civil infrastructure inevitably deteriorate during their service lives. Since their damage may claim human lives and cause significant economic losses, how to identify damages and assess structural conditions timely and accurately has drawn increasingly more attentions from structural engineering community worldwide. In this study, a fast and sensitive time domain damage identification method will be developed. First, a high quality finite element model is built and the structural responses are simulated under different damage scenarios. Based on the simulated data, an Auto Regressive Moving Average Exogenous (ARMAX) model is then developed and calibrated. The calibrated ARMAX model can be used to identify damage in different scenarios through model updating process using clonal selection algorithm (CSA). The identification results demonstrate the performance of the proposed methodology, which has the potential to be used for damage identification in practices.