Comparison of cumulative incidence functions of current status competing risks data with discrete observation times

Abstract

Competing risks data with current status censoring arise frequently from transversal studies in demography, epidemiology and reliability theory; where the only information about lifetime is whether the event of interest has occurred or not before a monitoring time. In practice, the monitoring times are discrete, but most of the studies consider them as continuous in nature. In the present paper, we propose a non parametric test for comparing cumulative incidence functions of current status competing risks data while the observation (monitoring) times are discrete. Asymptotic distribution of the test statistic is also derived. A simulation study is conducted to assess the finite sample behavior of the test statistic. The practical utility of the procedure is well demonstrated using a real-life data set on menopausal history of 2423 women given in Jewell, van der Laan, and Henneman (2003 Jewell, N. P., M. van der Laan, and T. Henneman. 2003. Nonparametric estimation from current status data with competing risks. Biometrika 90 (1):183–197. doi: 10.1093/biomet/90.1.183.[Crossref], [Web of Science ®] , [Google Scholar]).     

Branch‐and‐Price Methods for Prescribing Profitable Upgrades of High‐Technology Products with Stochastic Demands*

This paper develops a model that can be used as a decision support aid, helping manufacturers make profitable decisions in upgrading the features of a family of high‐technology products over its life cycle. The model integrates various organizations in the enterprise: product design, marketing, manufacturing, production planning, and supply chain management. Customer demand is assumed random and this uncertainty is addressed using scenario analysis. A branch‐and‐price (B&P) solution approach is devised to optimize the stochastic problem effectively. Sets of random instances are generated to evaluate the effectiveness of our solution approach in comparison with that of commercial software on the basis of run time. Computational results indicate that our approach outperforms commercial software on all of our test problems and is capable of solving practical problems in reasonable run time. We present several examples to demonstrate how managers can use our models to answer “what if” questions.