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Byline: Biji. Sankaran, Santam. Chakraborty, Vijay. Patil, Sindhu. Raghavan, Shibimol. Thomas, Subhradev. Sen Aim: To report the prevalence and outcomes of pressure ulcers (PU) seen in a cohort of cancer [...]

In this work, we introduce a new xgamma-Poisson lifetime model called the quasi xgamma- Poisson distribution. Some of its mathematical properties are derived. The proposed model can be motivated with a physical motivation by compounding the quasi xgamma construction with the truncated Poisson distribution. The quasi xgamma-Poisson model also motivated by the wide use of the xgamma distribution in many applied areas as well as for the fact that the new generalization provides more flexibility to analyze real data. We discuss the maximum likelihood estimation of the quasi xgamma-Poisson model parameters. An application to illustrate that the proposed quasi xgamma-Poisson model provides consistently better fit than the other competitive models. [ABSTRACT FROM AUTHOR]

Opposed to the general theory of infinite upper limit for lifetime distributions, Mukherjee and Islam ([4], 1983) proposed and studied a finite range distribution considering the fact that designed lifetime of equipment is actually finite. It is quite rational to consider interdependency of multi-component systems in analyzing reliability characteristics. In this article, we consider two-component coherent dependent systems by developing bivariate finite range distribution following the method of Farlie-Gumbel-Morgenstern copula. We investigate and study different reliability characteristics, such as mean time to system failure, system reliability and failure rate functions for finite range coherent dependent systems. We also compare the dependent finite range systems with independent finite range systems for mentioned reliability characteristics. [ABSTRACT FROM AUTHOR]

The term frailty was introduced by Vaupel et al., 1979, to indicate that different individuals are at risks even though on the surface they may appear to be quite similar with respect to the measurable attributes such as age, sex, habits etc. The term frailty can be utilized to represent an unobservable random effect shared by subjects with similar risks in the analysis of time to event data and/or mortality rates. In this article, we make use of the parametric shared frailty models to a real life data for identifying the distributional form of baseline hazard function. The gamma shared frailty, with disease stages as clusters, with log-logistic baseline hazard model came out to be the best choice for modeling survival data of Head and Neck cancer patients treated with radiotherapy. The suitability of the best-chosen model is justified considering two significant covariates, namely, age of the patients and habit of their alcohol consumption. We obtain the estimates of frailty (or unknown heterogeneity) for five stages of disease taken as clusters for Gamma- log-logistic shared frailty model. [ABSTRACT FROM AUTHOR]

For the purpose of generalizing or extending an existing probability distribution, incorporation of additional parameter to it is very common in the statistical distribution theory and practice. In fact, in most of the times, such extensions provide better fit to the real life situations compared to the existing ones. In this article, we propose and study a two-parameter probability distribution, called quasi xgamma distribution, as an extension or generalization of xgamma distribution (Sen et al. 2016) for modeling lifetime data. Important distributional properties along with survival characteristics and distributions of order statistics are studied in detail. Method of maximum likelihood and method of moments are proposed and described for parameter estimation. A data generation algorithm is proposed supported by a Monte-Carlo simulation study to describe the mean square errors of estimates for different sample sizes. A bladder cancer survival data is used to illustrate the application and suitability of the proposed distribution as a potential survival model. [ABSTRACT FROM AUTHOR]

In clinical studies, subjects or patients might be exposed to a succession of diagnostic tests or medication over time and interest is on determining whether there is progressive remission of conditions, disease or symptoms that have measured collectively as quality of life or outcome scores. In addition, subjects or study participants may be required, perhaps early in an experiment, to improve significantly in their performance rates at the current trial relative to an immediately preceding trial, otherwise the decision of withdrawal or dropping out is ineviTable. The common research interest would then be to determine some critical minimum marginal success rate to guide the management in decision making for implementing certain policies. Success rates lower than the minimum expected value would indicate a need for some remedial actions. In this article, a method of estimating these rates is proposed assuming the requirement is at the second trial of any particular study. Pairwise comparisons of proportions of success or failure by subjects is considered in repeated outcome measure situation to determine which subject or combinations is responsible for the rejection of the null hypothesis. The proposed method is illustrated with the help of a dataset on palliative care outcome scores (POS) of cancer patients. [ABSTRACT FROM AUTHOR]