Sunday, Aug 3: 2:00 PM - 3:50 PM
4007
Contributed Papers
Music City Center
Room: CC-102B
Main Sponsor
Isolated Statisticians
Presentations
This article presents a comparative analysis of a novel piecewise exponential estimator (NPEE) for censored data against three widely recognized estimators: the Kaplan-Meier estimator (KME), the Nelson estimator (NE), and an empirical Bayes type estimator (EBE). The NPEE, characterized by continuity on [0, ∞) and an exponential tail with a hazard rate derived through a novel nonparametric approach, retains the core advantages of the KME while addressing limitations inherent in the other estimators. These shortcomings restrict the broader applicability of the KME, NE, and EBE. To evaluate model performance, a simulation study was conducted using absolute bias and relative efficiency as quality metrics. Comparisons were performed across three levels of censoring, two sample sizes, and various quantiles. Results demonstrate that the NPEE, which is asymptotically equivalent to the KME, outperforms the other estimators for finite sample sizes, providing a robust alternative in survival function estimation.
Keywords
survival function
censored data
piecewise exponential estimator
Kaplan-Meier estimator
simulation study
nonparametric methods
First Author
Ganesh Malla, University of Cincinnati - Clermont College
Presenting Author
Ganesh Malla, University of Cincinnati - Clermont College
Choosing the optimal dose is critical for drug development. The FDA's Project Optimus emphasizes that dose optimization should be based on the totality of safety, efficacy, PK, and PD. Current methods, such as the MTD approach, focus solely on safety, while others, like the Clinical Utility Score (CUS), combine weighted endpoints with obscure weight assignments. In contrast, the win odds method offers a comprehensive benefit-risk assessment, which integrates different efficacy, safety, and other endpoints into one composite endpoint. Specifically, the win odds can determine a winner by comparing the overall outcomes of pairs of patients on two different doses, from the most to the least important outcome. Additionally, it can assess benefit and risk within multiple candidate doses. Therefore, the win odds test can identify the optimal dose that has significantly more winners than the other doses. Extensive simulations are conducted to explore the robustness of win odds method across various scenarios. It is also compared with the CUS method and applied to a clinical trial. Overall, the win odds method provides an effective and straightforward approach to dose optimization.
Keywords
Dose Optimization
Benefit-Risk Analysis
Win Odds
Co-Author
Rachael Wen, Bristol-Myers Squibb Company
First Author
Cong Cao, Bristol-Myers Squibb Company
Presenting Author
Cong Cao, Bristol-Myers Squibb Company
Mediation analysis is a framework to understand how a treatment affects the outcome through intermediate variables, namely mediators. Over the past decades, large and high-dimensional datasets have become easily stored and publicly available. This has led to many recent advances in mediation analysis, including developing models to fit more complex data structures and methods for mediator selections in high-dimensional settings. The statistical inference procedure following the mediator selection is also an important step in the mediation analysis. We study the effect of different variable selection and inference procedures through simulation studies. In this talk, I will discuss our simulation settings and the findings to provide guidelines that help distinguish among various approaches, highlight the advantages and disadvantages of each, and identify ones that perform better in certain scenarios.
Keywords
Linear structural equation modeling
Penalization
Bootstrap
Latent space models are powerful tools for analyzing relational data, offering low-dimensional representations of interactions. However, many real-world relationships evolve over time, requiring more flexible models. With the increasing availability of dynamic interaction data, capturing these changes is crucial. We extend the latent space model to embed actor trajectories in Euclidean space, enabling better inference of evolving relationships. This framework is particularly useful for studying complex networks, where uncovering latent structures provides critical insights. By tracking how entities' latent positions evolve, we can better understand shifting interaction patterns, emerging structures, and long-term trends, offering valuable perspectives for various domains. This is joint work with Dr. Owen Ward (Simon Fraser University).
Keywords
network science
latent space models
dynamic networks
spatial embeddings
Co-Author
Owen Ward, Simon Fraser University
First Author
Jie Jian, University of Chicago
Presenting Author
Jie Jian, University of Chicago
Order identification for models of big time series data presents computational challenges. Results from previous studies on big univariate time series suggest that methods based on kriging and optimization can reduce the computing time substantially while providing adequately plausible model orders. In today's world, however, one must analyze multiple big time series simultaneously such as multiple stocks or measuring humidity in various rooms of a house. This becomes a much bigger computational challenge to address, as one must take into account the cross-correlation between the individual time series. The goal of this work is to detail a method to fit big multivariate time series. The results show that the proposed technique can substantially decrease computing time while still provide reasonably accurate model orders.
Keywords
Big data
Kriging
Optimization
Order identification
ARMA
First Author
Brian Wu, Xavier University
Presenting Author
Brian Wu, Xavier University
This paper tackles the challenge of integrating sequentially arriving data within the quantile regression framework, where the number of covariates is allowed to grow with the number of observations, the horizon is unknown, and memory is limited. We employ stochastic sub-gradient descent to minimize the empirical check loss and study its statistical properties and regret performance. In our analysis, we unveil the delicate interplay between updating iterates based on individual observations versus batches of observations, revealing distinct regularity properties in each scenario. Our method ensures long-term optimal estimation irrespective of the chosen update strategy. Importantly, our contributions go beyond prior works by achieving exponential-type concentration inequalities and attaining optimal regret and error rates that exhibit only short-term sensitivity to initial errors. A key insight from our study is the delicate statistical analyses and the revelation that appropriate stepsize schemes significantly mitigate the impact of initial errors on subsequent errors and regrets. This underscores the robustness of stochastic sub-gradient descent in handling initial uncertainties,
Keywords
online linear regression
quantile regression
nonsmooth optimization
sub-gradient descent
batch learning
Co-Author(s)
Dong Xia, Hong Kong University of Science and Technology
Wenxin Zhou, University of Illinois Chicago
First Author
Yinan Shen, University of Southern California
Presenting Author
Yinan Shen, University of Southern California