Analysis of Type-II hybrid censored competing risks data

Kundu and Gupta [Analysis of hybrid life-tests in presence of competing risks. Metrica. 2007;65:159–170] provided the analysis of Type-I hybrid censored competing risks data, when the lifetime distributions of the competing cause of failures follows exponential distribution. In this paper, we consider the analysis of Type-II hybrid censored competing risks data. It is assumed that latent lifetime distributions of the competing causes of failures follow independent exponential distributions with different scale parameters. It is observed that the maximum likelihood estimators of the unknown parameters do not always exist. We propose the modified estimators of the scale parameters, which coincide with the corresponding maximum likelihood estimators when they exist, and asymptotically they are equivalent. We obtain the exact distribution of the proposed estimators. Using the exact distributions of the proposed estimators, associated confidence intervals are obtained. The asymptotic and bootstrap confidence intervals of the unknown parameters are also provided. Further, Bayesian inference of some unknown parametric functions under a very flexible Beta-Gamma prior is considered. Bayes estimators and associated credible intervals of the unknown parameters are obtained using the Monte Carlo method. Extensive Monte Carlo simulations are performed to see the effectiveness of the proposed estimators and one real data set has been analysed for the illustrative purposes. It is observed that the proposed model and the method work quite well for this data set.     

Correlated gamma frailty models for bivariate survival data

Frailty models are used in the survival analysis to account for the unobserved heterogeneity in individual risks to disease and death. To analyze the bivariate data on related survival times (e.g., matched pairs experiments, twin or family data) the shared frailty models were suggested. Shared frailty models are used despite their limitations. To overcome their disadvantages correlated frailty models may be used. In this article, we introduce the gamma correlated frailty models with two different baseline distributions namely, the generalized log logistic, and the generalized Weibull. We introduce the Bayesian estimation procedure using Markov chain Monte Carlo (MCMC) technique to estimate the parameters involved in these models. We present a simulation study to compare the true values of the parameters with the estimated values. Also we apply these models to a real life bivariate survival dataset related to the kidney infection data and a better model is suggested for the data.     

The role of time perspective in predicting Iranian farmers’ participatory-based water conservation attitude and behavior

ABSTRACT

In current perspective, farmers’ participatory behavior toward conservation of water resources (FPBCWR) is one of the most important strategies under water resource management in rural Iran. In this regard, understanding the predictors of farmers’ participatory-based water conservation behaviors and attitudes is gaining more importance than earlier. Among different dimensions of farmers’ participatory behavior, the potential of temporal frames was examined rarely. Thus, 322 Iranian farmers were investigated to examine the potential of their time perspectives in predicting their participatory-based water conservation behavior and attitude. According to the study results, the effects of present orientation on attitude and behavior were negatively significant, while the effects of future orientation on attitude and behavior were positively significant, whereas its effects (path coefficients) were stronger than present orientation effects. Past orientation did not have a significant effect on attitude, though attitude positively and significantly affected farmers’ participatory behavior. The results of causal analysis revealed that presented model accounted for 58% of variance in farmers’ ‘behavior’ and 42% of variance in “attitude.” In conclusion, a few demonstrable illustrations of policy implications are presented to enable utilizing the important findings and concluding results of this study that is linked further with water resource management domain.     

Exposure of medical students to body fluids

Three hundred forty-two students at 3 Florida medical schools were surveyed concerning occupational exposures to blood and body fluids during their 3rd-year clerkship. The 16-item questionnaire was anonymously returned by 150 students, and differences among groups were assessed at p < .05. Most of the students complied with universal precautions guidelines (UVPG); 62 reported 101 exposures, including 9 with HIV-positive blood and body fluids. Most of the exposed students knew about the guidelines but regarded the incidents as irrelevant to their safety or supervision training. Noncompliant students reported significantly more exposures than compliant students. Time constraints, inconvenience of using gloves during procedures, and belief that patients were at low HIV risk discouraged adherence to the guidelines. Common practices following exposure were "no action" or "washed area only" without medical follow-up. Medical students' UVPG adherence should be increased by workload modification, user-friendly safety products, and supervised practice training in clinical exposure settings.     

Computer-Generated Change-Over Designs for Correlated Observations

Change-over designs with independently distributed errors in the model have been studied extensively in the literature. Martin and Eccleston (2001 Martin , R. J. , Eccleston , J. A. ( 2001 ). Optimal and near optimal designs for dependent observations . Statist. Applic. 3 : 101 – 116 . [Google Scholar]) gave an algorithm for the generation of efficient change-over designs when the errors are correlated. This article proposes an algorithm for the generation of efficient change-over designs for estimation of direct effects of treatments in the presence of first-order residual effects in the model and when the errors are correlated.     

Bayesian Analysis of Different Hybrid and Progressive Life Tests

Type-I and Type-II censoring schemes are the widely used censoring schemes available for life testing experiments. A mixture of Type-I and Type-II censoring schemes is known as a hybrid censoring scheme. Different hybrid censoring schemes have been introduced in recent years. In the last few years, a progressive censoring scheme has also received considerable attention. In this article, we mainly consider the Bayesian inference of the unknown parameters of two-parameter exponential distribution under different hybrid and progressive censoring schemes. It is observed that in general the Bayes estimate and the associated credible interval of any function of the unknown parameters, cannot be obtained in explicit form. We propose to use the Monte Carlo sampling procedure to compute the Bayes estimate and also to construct the associated credible interval. Monte Carlo Simulation experiments have been performed to see the effectiveness of the proposed method in case of Type-I hybrid censored samples. The performances are quite satisfactory. One data analysis has been performed for illustrative purposes.     

Do Asset Market Prices Reflect Traders' Judgment Biases?

The existence of base rate fallacy (BRF) bias is explored employing: (i) a context treatment with a narrative story applied to asset markets and (ii) an isomorphic abstract setting using balls-and-bingo cages. Probability estimates reflect a BRF bias in both treatments, but is stronger with context. Prices track highest expected dividend values (HEDVs) with context, resulting in strongly biased prices relative to the Bayesian norm when biased traders have HEDVs. In the abstract treatment prices do not track HEDVs nearly as closely, resulting in prices closer to the BRF bias only when most traders hold biased beliefs.     

On classical and Bayesian order restricted inference for multiple exponential step stress model

In this article, we consider the multiple step stress model based on the cumulative exposure model assumption. Here, it is assumed that for a given stress level, the lifetime of the experimental units follows exponential distribution and the expected lifetime decreases as the stress level increases. We mainly focus on the order restricted inference of the unknown parameters of the lifetime distributions. First we consider the order restricted maximum likelihood estimators (MLEs) of the model parameters. It is well known that the order restricted MLEs cannot be obtained in explicit forms. We propose an algorithm that stops in finite number of steps and it provides the MLEs. We further consider the Bayes estimates and the associated credible intervals under the squared error loss function. Due to the absence of explicit form of the Bayes estimates, we propose to use the importance sampling technique to compute Bayes estimates. We provide an extensive simulation study in case of three stress levels mainly to see the performance of the proposed methods. Finally the analysis of one real data set has been provided for illustrative purposes.     

On the one parameter unit-Lindley distribution and its associated regression model for proportion data

In this paper considering an appropriate transformation on the Lindley distribution, we propose the unit-Lindley distribution and investigate some of its statistical properties. An important fact associated with this new distribution is that it is possible to obtain the analytical expression for bias correction of the maximum likelihood estimator. Moreover, it belongs to the exponential family. This distribution allows us to incorporate covariates directly in the mean and consequently to quantify their influences on the average of the response variable. Finally, a practical application is presented to show that our model fits much better than the Beta regression.     

Emergency Department Staff Planning to Improve Patient Care and Reduce Costs

In the face of high staffing costs, uncertain patient arrivals, and patients unsatisfied with long wait times, staffing of medical emergency departments (EDs) is a vexing problem. Using empirical data collected from three active EDs, we develop an analytic model to provide an effective staffing plan for EDs. Patient demand is aggregated into discrete time buckets and used to model the stochastic distribution of patient demand within these buckets, which considerably improves model tractability. This model is capable of scheduling providers with different skill profiles who work either individually or in teams, and with patients of varying acuity levels. We show how our model helps to balance staffing costs and patient service levels, and how it facilitates examination of important ED staffing policies.