Shared frailty model based on reversed hazard rate for left censored data

Frailty models are used in the survival analysis to account for the unobserved heterogeneity in the 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. In this article, we introduce the shared gamma frailty models with the reversed hazard rate. We develop the Bayesian estimation procedure using the Markov chain Monte Carlo (MCMC) technique to estimate the parameters involved in the model. We present a simulation study to compare the true values of the parameters with the estimated values. We apply the model to a real life bivariate survival dataset.     

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.     

Gamma shared frailty model based on reversed hazard rate

Abstract

Frailty models are used in survival analysis to account for unobserved heterogeneity in individual risks to disease and death. To analyze bivariate data on related survival times (e.g., matched pairs experiments, twin, or family data), shared frailty models were suggested. Shared frailty models are frequently used to model heterogeneity in survival analysis. The most common shared frailty model is a model in which hazard function is a product of random factor(frailty) and baseline hazard function which is common to all individuals. There are certain assumptions about the baseline distribution and distribution of frailty. In this paper, we introduce shared gamma frailty models with reversed hazard rate. We introduce Bayesian estimation procedure using Markov Chain Monte Carlo (MCMC) technique to estimate the parameters involved in the model. We present a simulation study to compare the true values of the parameters with the estimated values. Also, we apply the proposed model to the Australian twin data set.     

A Simpler Approximation for Areas under the Standard Normal Curve

A simple approximation for areas under the standard normal curve is presented that is suitable for use when tables and/or calculators are not available or not permitted.     

Pricing and Replenishment of Competing Perishable Product Variants under Dynamic Demand Substitution

I consider pricing and ordering decisions faced by a retailer selling a perishable product with a two‐period shelf life over an infinite horizon. In the first period, the product is “new”; in the next, it becomes “old.” The new product is perceived by customers to have a higher quality than the old product. Every period, the retailer makes three decisions: prices for the new and old products and how much new product to order. I first show, with some simple cases, that demand uncertainty can make the sale of the old product profitable. I then consider a more realistic case with dynamic demand substitution among customers. I recognize that the retailer's decisions may be constant or may vary across different periods, under different contexts. For instance, varying the price of the new product can sometimes be difficult due to the negative impact it generates among customers. I find that (i) the benefit obtained from selling the old product with constant decisions is much higher than the benefit from allowing all the decisions to vary; (ii) the former benefit increases with a higher procurement cost, a higher quality of the new product, and higher demand volatility; however, the latter benefit is non‐monotone in these parameters; (iii) most of the latter benefit can be obtained by just changing the order quantity; and (iv) as the inventory of the old product increases, when all the decisions vary, the optimal price of the new product may increase or decrease.     

Likelihood Ratio Tests for Interval Hypotheses with Applications

In this article, we derive the likelihood ratio tests (LRTs) for simultaneously testing interval hypotheses for normal means with known and unknown variances, and also with unknown but equal variance. Special cases when the interval hypotheses boil down to a point hypothesis are also discussed. Remarks regarding comparison of the LRT with tests based on combination of p-values are made, and several applications based on real data are mentioned.     

Shared frailty models based on reversed hazard rate for modified inverse Weibull distribution as baseline distribution

The unknown or unobservable risk factors in the survival analysis cause heterogeneity between individuals. 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, the shared frailty models were suggested. The most common shared frailty model is a model in which frailty act multiplicatively on the hazard function. In this paper, we introduce the shared gamma frailty model and the inverse Gaussian frailty model with the reversed hazard rate. We introduce the Bayesian estimation procedure using Markov chain Monte Carlo (MCMC) technique to estimate the parameters involved in the model. We present a simulation study to compare the true values of the parameters with the estimated values. We also apply the proposed models to the Australian twin data set and a better model is suggested.     

Analysis of binary data for the balanced one-way classification

A test is proposed for testing the equality of proportions based on the data available from a one-way classification having t treatment conditions and n binary observations per treatment. The test statistic B is a constant multiple of the F-statistic which results when the analysis of variance procedure for the one-way classification is applied to the data and, hence, is computationally simple. The statistic B from this binary analysis of variance (BIANOVA) is distributed asymptotically as a chi-square random variable. The proposed test is uniformly more powerful than either the F-test indicated above or the Pearson chi-square test; however, the attained empirical level of significance is frequently higher than for either of these competitors and usually higher than the stated level of significance for smaller values of n (say n ≤ 20).