Application Of Proportional Hazards Model Research Articles

A Bayesian approach to reliability prediction for one-shot devices

Projectile such as rocket and missiles is one of most important object for military and space industry. In order to measure its reliability accurately, general method which uses binomial distribution often require more than 3,000 sample to evaluate the reliability which can be problematic for further research and development. There have been many researches and studies to predict reliability with small sample size. Many studies suggest Bayesian approach with precise prior distribution as effective method to measure reliability of one shot device. In this research, we suggest application of proportional hazard model with Bayesian approach to estimate reliability of one shot device withsmall sample size.

The Initial Going-concern of Delisting Firms: An Application of Proportional Hazard Model

Normal 0 0 2 false false false EN-US ZH-TW X-NONE MicrosoftInternetExplorer4 This paper examines the survival period and the factors of business failure of firms who have been issued with an initial going concern opinion (IGCO) by auditors. Empirical results show that financial variables are not significant predictors for future delisting crisis, but the corporate governance variables are especially for firms under deteriorating financial condition. Important factors causing the higher rate of delisting risk include shorter listing years, lower rate of retained earnings to total assets, lower rate of market value of equity to total debts, and higher rate of pledged shares of directors’ and supervisors’ within 7.5 quarters after the IGCO issued, the number of delisting firms reaches its peak, consistent with the existence of self-fulfilling prophecy. The hazard delisting function first rises to a peak at the 38 th quarter and then declines rapidly, showing that after the disclosure of IGCO, first nine years is the delisting crisis period for Taiwan public firms.

Analysis of failure times for multiple infections following bone marrow transplantation: an application of the multiple failure time proportional hazards model.

We report an application of the proportional hazards model for multiple failure times in a study arising from the Bone Marrow Transplant Database at the University of Minnesota. The study compared the risk of infections after transplantation for patients who received allogeneic bone marrow transplants from unrelated donors (URD) versus related donors (RD). In 249 patients there was a total of 365 infections in 2.5 years of follow-up. The multiple failure time model uses all the data and provides empirical estimates of standard errors that incorporate the within-person dependencies in the data. The estimate of relative risk associated with URD was 1.4 (naive 95 per cent confidence interval 1.14 to 1.73, empirical 1.08 to 1.79), compared to the estimate 1.6 (naive or empirical, 1.1 to 2.1) from the proportional hazards model on 165 first infections only. The multivariate model gives great flexibility in modelling, for example, in accommodating a separate base hazard function for each type of failure and in allowing analysis of intervals between infections as an alternative to analysis of time from a marker event, here transplantation.

An application of proportional hazards model to study the recurrent time between traffic accidents or infractions and subsequent fatal automobile crashes, 1986–1988

Drivers who have a record of traffic accidents or infractions may be at increased risk of being involved in a subsequent fatal crash. To develop effective intervention programs, it is important for us to quantify the joint effects of suspected covariates that may affect the recurrent time, which we define as the time interval between the date of the last traffic accident or infraction and the date of the subsequent fatal crash. We analyze the data from the Fatal Accident Reporting System (FARS) for the years 1986 through 1988. Because the FARS includes only drivers who already have been involved in a fatal crash and excludes drivers who have had traffic accidents or infractions but have not yet been involved in any fatal crash, the application of the proportional hazards model directly to the recurrent time considered here is not appropriate. We can, however, apply the proportional hazards model with time-dependent risk set defined by using the reverse hazard function. On the basis of the resulting model, we have found that all the covariates, including age, sex, usage of seat belts, and alcohol involvement during the fatal crashes, have significant effects on the recurrent time. In general, a male, unbelted driver between 16 and 25 years of age, under the influence of alcohol during a fatal crash, and without a valid license tends to have relatively short recurrent times.