Study of n-fold Weibull competing risk model

This paper deals with an n-fold Weibull competing risk model. A characterisation of the WPP plot is given along with estimation of model parameters when modelling a given data set. These are illustrated through two examples. A study of the different possible shapes for the density and failure rate functions is also presented. (C) 2003 Elsevier Ltd. All rights reserved.

Risk stratification of patients with chronic kidney disease: Results of screening strategies incorporating clinical risk scoring and dobutamine stress echocardiography

Background Cardiac disease is the principal cause of death in patients with chronic kidney disease (CKD). Ischemia at dobutamine stress echocardiography (DSE) is associated with adverse events in these patients. We sought the efficacy of combining clinical risk evaluation with DSE. Methods We allocated 244 patients with CKD (mean age 54 years, 140 men, 169 dialysis-dependent at baseline) into low- and high-risk groups based on two disease-specific scores and the Framingham risk model. All underwent DSE and were further stratified according to DSE results. Patients were followed over 20 +/- 14 months for events (death, myocardial infarction, acute coronary syndrome). Results There were 49 deaths and 32 cardiac events. Using the different clinical scores, allocation of high risk varied from 34% to 79% of patients, and 39% to 50% of high-risk patients had an abnormal DSE. In the high-risk groups, depending on the clinical score chosen, 25% to 44% with an abnormal DSE had a cardiac event, compared with 8% to 22% with a.normal DSE. Cardiac events occurred in 2.0%, 3.1 %, and 9.7% of the low-risk patients, using the two disease-specific and Framingham scores, respectively, and DSE results did not add to risk evaluation in this subgroup. Independent DSE predictors of cardiac events were a lower resting diastolic blood pressure, angina during the test, and the combination of ischemia with resting left ventricular dysfunction. Conclusion In CKD patients, high-risk findings by DSE can predict outcome. A stepwise strategy of combining clinical risk scores with DSE for CAD screening in CKD reduces the number of tests required and identifies a high-risk subgroup among whom DSE results more effectively stratify high and low risk.

Models involving two inverse Weibull distributions

In this paper, we look at three models (mixture, competing risk and multiplicative) involving two inverse Weibull distributions. We study the shapes of the density and failure-rate functions and discuss graphical methods to determine if a given data set can be modelled by one of these models. (C) 2001 Elsevier Science Ltd. All rights reserved.

A model for predicting the future incidence of coronary heart disease within percentiles of coronary heart disease risk

Background We present a method (The CHD Prevention Model) for modelling the incidence of fatal and nonfatal coronary heart disease (CHD) within various CHD risk percentiles of an adult population. The model provides a relatively simple tool for lifetime risk prediction for subgroups within a population. It allows an estimation of the absolute primary CHD risk in different populations and will help identify subgroups of the adult population where primary CHD prevention is most appropriate and cost-effective. Methods The CHD risk distribution within the Australian population was modelled, based on the prevalence of CHD risk, individual estimates of integrated CHD risk, and current CHD mortality rates. Predicted incidence of first fatal and nonfatal myocardial infarction within CHD risk strata of the Australian population was determined. Results Approximately 25% of CHD deaths were predicted to occur amongst those in the top 10 percentiles of integrated CHD risk, regardless of age group or gender. It was found that while all causes survival did not differ markedly between percentiles of CHD risk before the ages of around 50-60, event-free survival began visibly to differ about 5 years earlier. Conclusions The CHD Prevention Model provides a means of predicting future CHD incidence amongst various strata of integrated CHD risk within an adult population. It has significant application both in individual risk counselling and in the identification of subgroups of the population where drug therapy to reduce CHD risk is most cost-effective. J Cardiovasc Risk 8:31-37 (C) 2001 Lippincott Williams & Wilkins.

Adaptation to being at-risk for Huntington's disease and the availability of genetic testing: Application of a stress and coping model

This study examined the utility of a stress/coping model in explaining adaptation in two groups of people at-risk for Huntington's Disease (HD): those who have not approached genetic testing services (non-testees) and those who have engaged a testing service (testees). The aims were (1) to compare testees and non-testees on stress/coping variables, (2) to examine relations between adjustment and the stress/coping predictors in the two groups, and (3) to examine relations between the stress/coping variables and testees' satisfaction with their first counselling session. Participants were 44 testees and 40 non-testees who completed questionnaires which measured the stress/coping variables: adjustment (global distress, depression, health anxiety, social and dyadic adjustment), genetic testing concerns, testing context (HD contact, experience, knowledge), appraisal (control, threat, self-efficacy), coping strategies (avoidance, self-blame, wishful thinking, seeking support, problem solving), social support and locus of control. Testees also completed a genetic counselling session satisfaction scale. As expected, non-testees reported lower self-efficacy and control appraisals, higher threat and passive avoidant coping than testees. Overall, results supported the hypothesis that within each group poorer adjustment would be related to higher genetic testing concerns, contact with HD, threat appraisals, passive avoidant coping and external locus of control, and lower levels of positive experiences with HD, social support, internal locus of control, self-efficacy, control appraisals, problem solving, emotional approach and seeking social support coping. Session satisfaction scores were positively correlated with dyadic adjustment, problem solving and positive experience with HD, and inversely related to testing concerns, and threat and control appraisals. Findings support the utility of the stress/coping model in explaining adaptation in people who have decided not to seek genetic testing for HD and those who have decided to engage a genetic testing service.