Precedence-type Test based on Progressively Censored Samples

In this paper, we introduce precedence-type tests for testing the hypothesis that two distribution functions are equal, which is an extension of the precedence life-test rst proposed by Nelson (1963), when the two samples are progressively Type-II censored. The null distributions of the test statistics are derived. Critical values for some combination of sample sizes and censoring schemes for the proposed tests are presented. Then, we present the exact power functions under the Lehmann alternative, and compare the exact power as well as simulated power (under location-shift) of the proposed precedence test based on nonparametric estimates of CDF with other precedence-type tests. We then examine the power properties of the proposed test procedures through Monte Carlo simulations. Two examples are presented to illustrate all the test procedures discussed here. Finally, we make some concluding remarks.Precedence test; Product-limit estimator; Type-II progressive censoring; Life-testing; level of significance; power; Lehmann alternative; Monte Carlo simulations

Advances in ranking and selection, multiple comparisons, and reliability: methodology and applications

S. Panchapakesan has made significant contributions to ranking and selection and has published in many other areas of statistics, including order statistics, reliability theory, stochastic inequalities, and inference. Written in his honor, the twenty invited articles in this volume reflect recent advances in these areas and form a tribute to Panchapakesan's influence and impact on these areas. Thematically organized, the chapters cover a broad range of topics from: Inference; Ranking and Selection; Multiple Comparisons and Tests; Agreement Assessment; Reliability; and Biostatistics. Featurin

Bivariate positive stable frailty models

This article describes inference for dependent multivariate times-to-events using a bivariate positive stable frailty model with a Weibull baseline hazard. Suitable Markov chain Monte Carlo algorithms facilitate Bayesian inference. The method is illustrated using a study conducted by the Air Force Research Laboratory on times to symptoms of decompression sickness in human subjects.

On the Joint Distribution of Placement Statistics under Progressive Censoring and Applications to Precedence Test

In reliability and survival analysis, comparison of two or more populations is an important problem. For example, while comparing a treatment group with a control group, one may be interested in determining whether the observations in the treatment group have a longer lifetime than those from the control group, that is, whether the treatment is effective or not. In such a studym it would be extremenly valuable to make a decision based on early failures. In this paper, we consider independent progressively Type-II censored random samples from two populations with cumulative distribution function's (cdf) F(.) and G(.) respectively, and discuss a precedence test for testing the equality of the two distributions based on placements. For this purpose, we derive the joint distribution of the first l placement statistics from the progressively censored sample from F(.). We then derive the exact null distribution of the precedence test statistic which is simply the sum of the placements. We provide the rejection regions for fixed levels of significance and various sample sizes and different progressive censoring schemes.Progressove Type-II censoring, placements, precedence and exceedance statistics, nonparametric tests of homogeneity, Wilcoxon rank-sum test.

Aprepitant for Cough Suppression in Advanced Lung Cancer:A Randomized Trial

BackgroundAlthough cough is a common and distressing symptom in patients with lung cancer, there is almost no evidence to guide treatment. Aprepitant, a centrally acting neurokinin-1 inhibitor, significantly decreased cough frequency in a pilot study.MethodsPatients with advanced lung cancer and cough lasting over 2 weeks despite a cough suppressant were randomized 1:1 to aprepitant 125 mg orally on day 1 and then 80 mg orally on days 2 to 7 with physician's choice of antitussive; or to physician's choice of antitussive alone. Evaluation was at baseline and on days 3, 7, 9, and 12. The primary end point was subjective cough improvement on day 9, measured by the Visual Analog Scale and Manchester Cough in Lung Cancer Scale. Secondary end points included quality of life (QoL) as measured by the European Organization for Research and Treatment of Cancer (EORTC) Core Quality of Life Questionnaire and the EORTC Lung Cancer-Specific Quality of Life Questionnaire and toxicity.ResultsBetween 2017 and 2018, 128 patients were randomized. Median baseline cough duration was 90 days. Mean Visual Analog Scale scores (in mm) at baseline and day 9 were 68 and 39 in the aprepitant arm and 62 and 49 in the control arm, respectively (P < .001); mean Manchester Cough in Lung Cancer Scale scores at baseline and day 9 were 33 and 23 in the aprepitant arm and 30 and 25 in the control arm, respectively (P < .001). Overall QoL was not significantly different between the two arms; however, aprepitant led to a significant improvement in the cough-specific QoL domain (P = .017). Aprepitant did not increase severe adverse events.ConclusionsAprepitant led to a significant improvement in cough in advanced lung cancer, without increasing severe side effects