Detecting trend change in hazard functions—an L-statistic approach

Author(s):  
Priyanka Majumder ◽  
Murari Mitra
2017 ◽  
Vol 19 (1) ◽  
pp. 23
Author(s):  
Ahmad Gunawan

Transformation Leadership, Motivation and Satisfiction are the three factors of a few relatively large factors suspected to influence Performance on the PT. Adya Tours. These research aimed to determine the effect of Transformation Leadership, Motivation and Satisfiction toward Performance on the PT. Adya Tours.Research conducted at the PT. Adya Tours by taking 71 employees as the research sample, calculated using the Slovin formula of the total population of 240 employees  at  the  margin  of  error  of  10%.  Data  were collected by questionnaire instruments covered by the five rating scale from strongly disagree to strongly agree. Quantitative research was conducted by describing and analyzing research data. The multiple linier regression analysis and multiple determination coeficient are the statistic approach to data analysis.The study produced four major findings consistent with the hypothesis put forward, that are: 1) Transformation Leadership has a significant effect on Performance  in  a  positive  direction;  2)  Motivation  has  a  significant  effect  on Performance in a positive direction; 3) Satisfiction has a significant effect on Performance in a positive direction; 4) Transformation Leadership, Motivation and Satisfiction simultaneously influence 92.70% Performance variability.Base on the research finding, in order to increase Performance can be done by increasing Transformation Leadership, Motivation and Satisfiction. Kata kunci:Transformation Leadership, Motivation, Satisfaction, Performance


2021 ◽  
Vol 21 (1-2) ◽  
pp. 56-71
Author(s):  
Janet van Niekerk ◽  
Haakon Bakka ◽  
Håvard Rue

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, times-dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model as specific examples.


Technometrics ◽  
1970 ◽  
Vol 12 (2) ◽  
pp. 413-416 ◽  
Author(s):  
Arthur NáDas

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
K. S. Sultan ◽  
A. S. Al-Moisheer

We discuss the two-component mixture of the inverse Weibull and lognormal distributions (MIWLND) as a lifetime model. First, we discuss the properties of the proposed model including the reliability and hazard functions. Next, we discuss the estimation of model parameters by using the maximum likelihood method (MLEs). We also derive expressions for the elements of the Fisher information matrix. Next, we demonstrate the usefulness of the proposed model by fitting it to a real data set. Finally, we draw some concluding remarks.


2010 ◽  
Vol 9 ◽  
pp. CIN.S5460 ◽  
Author(s):  
Tengiz Mdzinarishvili ◽  
Simon Sherman

Mathematical modeling of cancer development is aimed at assessing the risk factors leading to cancer. Aging is a common risk factor for all adult cancers. The risk of getting cancer in aging is presented by a hazard function that can be estimated from the observed incidence rates collected in cancer registries. Recent analyses of the SEER database show that the cancer hazard function initially increases with the age, and then it turns over and falls at the end of the lifetime. Such behavior of the hazard function is poorly modeled by the exponential or compound exponential-linear functions mainly utilized for the modeling. In this work, for mathematical modeling of cancer hazards, we proposed to use the Weibull-like function, derived from the Armitage-Doll multistage concept of carcinogenesis and an assumption that number of clones at age t developed from mutated cells follows the Poisson distribution. This function is characterized by three parameters, two of which ( r and λ) are the conventional parameters of the Weibull probability distribution function, and an additional parameter ( C0) that adjusts the model to the observational data. Biological meanings of these parameters are: r—the number of stages in carcinogenesis, λ—an average number of clones developed from the mutated cells during the first year of carcinogenesis, and C0—a data adjustment parameter that characterizes a fraction of the age-specific population that will get this cancer in their lifetime. To test the validity of the proposed model, the nonlinear regression analysis was performed for the lung cancer (LC) data, collected in the SEER 9 database for white men and women during 1975–2004. Obtained results suggest that: (i) modeling can be improved by the use of another parameter A- the age at the beginning of carcinogenesis; and (ii) in white men and women, the processes of LC carcinogenesis vary by A and C0, while the corresponding values of r and λ are nearly the same. Overall, the proposed Weibull-like model provides an excellent fit of the estimates of the LC hazard function in aging. It is expected that the Weibull-like model can be applicable to fit estimates of hazard functions of other adult cancers as well.


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