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A very classical problem in statistics is to test the stochastic superiority of one distribution to another. However, many existing approaches are developed for independent samples and, moreover, do not take censored data into account. We…

Methodology · Statistics 2023-06-19 Dennis Dobler , Kathrin Möllenhoff

The hazard ratio is routinely used as a summary measure to assess the treatment effect in clinical trials with time-to-event endpoints. It is frequently assumed as constant over time although this assumption often does not hold. When the…

Non-proportional hazards (NPH) have been observed in confirmatory clinical trials with time to event outcomes. Under NPH, the hazard ratio does not stay constant over time and the log-rank test is no longer the most powerful test. The…

Methodology · Statistics 2022-09-26 Bharati Kumar , Jonathan W. Bartlett

In the recent COVID-19 pandemic, a wide range of epidemiological modelling approaches have been used to predict the effective reproduction number, R(t), and other COVID-19 related measures such as the daily rate of exponential growth, r(t).…

Applications · Statistics 2021-08-13 T. Maishman , S. Schaap , D. S. Silk , S. J. Nevitt , D. C. Woods , V. E. Bowman

The use of the non-parametric Restricted Mean Survival Time endpoint (RMST) has grown in popularity as trialists look to analyse time-to-event outcomes without the restrictions of the proportional hazards assumption. In this paper, we…

Methodology · Statistics 2023-11-06 Emily Alger , David S. Robertson , Abigail J. Burdon

Health economic evaluations face the issues of non-compliance and missing data. Here, non-compliance is defined as non-adherence to a specific treatment, and occurs within randomised controlled trials (RCTs) when participants depart from…

Applications · Statistics 2019-02-26 Karla DiazOrdaz , Richard Grieve

Despite their numerous successes, there are many scenarios where adversarial risk metrics do not provide an appropriate measure of robustness. For example, test-time perturbations may occur in a probabilistic manner rather than being…

Machine Learning · Statistics 2021-08-03 Benjie Wang , Stefan Webb , Tom Rainforth

In this paper, a competing risks model is analyzed based on improved adaptive type-II progressive censored sample (IAT-II PCS). Two independent competing causes of failures are considered. It is assumed that lifetimes of the competing…

Methodology · Statistics 2023-02-20 Subhankar Dutta , Suchandan Kayal

In this paper, we introduce a doubly doubly robust estimator for the average and heterogeneous treatment effect for left-truncated-right-censored (LTRC) survival data. In causal inference for survival functions in LTRC survival data, two…

General Economics · Economics 2024-09-04 Guanghui Pan

The cause of failure in cohort studies that involve competing risks is frequently incompletely observed. To address this, several methods have been proposed for the semiparametric proportional cause-specific hazards model under a missing at…

Methodology · Statistics 2020-02-24 Giorgos Bakoyannis , Ying Zhang , Constantin T. Yiannoutsos

Survival analysis is a statistical technique used to estimate the time until an event occurs. Although it is applied across a wide range of fields, adjusting for reporting delays under practical constraints remains a significant challenge…

Machine Learning · Statistics 2025-10-27 Yuta Shikuri , Hironori Fujisawa

The lifetimes of subjects which are left-censored lie below a threshold value or a limit of detection. A popular tool used to handle left-censored data is the reversed hazard rate. In this work, we study the properties and develop…

Statistics Theory · Mathematics 2022-12-27 G. Asha , Durga Vasudevan

The average hazard (AH), recently introduced by Uno and Horiguchi, represents a novel summary metric of event time distributions, conceptualized as the general censoring-free average person-time incidence rate on a given time window,…

Applications · Statistics 2024-07-02 Hong Xiong , Jean Connors , Deb Schrag , Hajime Uno

The difference in restricted mean survival time (RMST) is a clinically meaningful measure to quantify treatment effect in randomized controlled trials, especially when the proportional hazards assumption does not hold. Several frequentist…

The classical approach to analyze time-to-event data, e.g. in clinical trials, is to fit Kaplan-Meier curves yielding the treatment effect as the hazard ratio between treatment groups. Afterwards commonly a log-rank test is performed in…

Methodology · Statistics 2020-09-16 Kathrin Möllenhoff , Achim Tresch

A core challenge in survival analysis is to model the distribution of censored time-to-event data, where the event of interest may be a death, failure, or occurrence of a specific event. Previous studies have showed that ranking and maximum…

Machine Learning · Computer Science 2025-01-27 Liwen Zhang , Lianzhen Zhong , Fan Yang , Di Dong , Hui Hui , Jie Tian

The Cox proportional hazards model is often used to analyze data from Randomized Controlled Trials (RCT) with time-to-event outcomes. Random survival forest (RSF) is a machine-learning algorithm known for its high predictive performance. We…

Machine Learning · Statistics 2025-05-28 Ricarda Graf , Susan Todd , M. Fazil Baksh

When plaintiffs prevail in a discrimination case, a major component of the calculation of economic loss is the length of time they would have been in the higher position had they been treated fairly during the period in which the employer…

Applications · Statistics 2013-12-02 Qing Pan , Joseph L. Gastwirth

We propose a restricted win probability estimand for comparing treatments in a randomized trial with a time-to-event outcome. We also propose Bayesian estimators for this summary measure as well as the unrestricted win probability. Bayesian…

Methodology · Statistics 2024-11-06 Michelle Leeberg , Xianghua Luo , Thomas A. Murray

Do two data samples come from different distributions? Recent studies of this fundamental problem focused on embedding probability distributions into sufficiently rich characteristic Reproducing Kernel Hilbert Spaces (RKHSs), to compare…

Machine Learning · Computer Science 2013-05-03 Somayeh Danafar , Paola M. V. Rancoita , Tobias Glasmachers , Kevin Whittingstall , Juergen Schmidhuber
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