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Dynamic subgroup identification in covariate-adjusted response-adaptive randomization experiments 24

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Dynamic subgroup identification in covariate-adjusted respon...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yanping Li Jingshen Wang Waverly Wei School of Statistics and Data Science Nankai University Division of Biostatistics University of California Berkeley Department of Data Sciences and Operations University of Southern California

ISBN: (纸本)9798331314385

Identifying subgroups with differential responses to treatment is pivotal in randomized clinical trials, as tailoring treatments to specific subgroups can advance personalized medicine. Upon trial completion, identifying best-performing sub-groups-those with the most beneficial treatment effects-is crucial for optimizing resource allocation or mitigating adverse treatment effects. However, traditional clinical trials are not customized for the goal of identifying best-performing subgroups because they typically pre-define subgroups at the beginning of the trial and adhere to a fixed subgroup treatment allocation rule, leading to inefficient use of experimental efforts. While some adaptive experimental strategies exist for the identification of the single best subgroup, they commonly do not enable the identification of the best set of subgroups. To address these challenges, we propose a dynamic subgroup identification covariate-adjusted response-adaptive randomization (CARA) design strategy with the following key features: (i) Our approach is an adaptive experimental strategy that allows the dynamic identification of the best subgroups and the revision of treatment allocation towards the goal of correctly identifying the best subgroups based on collected experimental data. (ii) Our design handles ties between subgroups effectively, merging those with similar treatment effects to maximize experimental efficiency. In the theoretical investigations, we demonstrate that our design has a higher probability of correctly identifying the best set of subgroups compared to conventional designs. Additionally, we prove the statistical validity of our estimator for the best subgroup treatment effect, demonstrating its asymptotic normality and semiparametric efficiency. Finally, we validate our design using synthetic data from a clinical trial on cirrhosis.

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On testing proportional odds assumptions for proportional odds models

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General Psychiatry 2023年第3期36卷 214-219页

作者： Anqi Liu Hua He Xin M Tu Wan Tang Department of Bostaistis and Data Science Tulane UniversityNew OrleansLouisianaUSA Department of Epidemiology Tulane UniversityNew OrleansLouisianaUSA Division of Biostatistics and Bioinformatics Herbert Wertheim School of Public Health and Human Longevity ScienceUC San DiegoLa JollaCaliforniaUSA

Proportional odds models are commonly used to model ordinal responses,but the proportional odds assumption may not hold in practice,leading to biased *** such as score,Wald and likelihood ratio(LR)have been proposed to evaluate the proportional odds assumption based on models without the *** has proposed an independent binary model-based Wald-type test,and Wolfe and Gould have extended the idea to propose an LR-type *** paper provides a brief review of the Brant and Wolfe-Gould tests for evaluating the proportional odds assumption and evaluates their performance through simulation studies and a real data *** programs are provided in SAS,SPSS and Stata to facilitate the implementation of these tests using standard statistical software *** study highlights the importance of evaluating the proportional odds assumption when using proportional odds models for ordinal *** sample programs provided in this paper make it easy for researchers to apply these tests in their own analyses using standard statistical software packages.

关键词： Gould testing apply

Transportability of Principal Causal Effects

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arXiv 2024年

作者： Clark, Justin M. Rott, Kollin W. Hodges, James S. Huling, Jared D. Division of Biostatistics and Health Data Science University of Minnesota School of Public Health MN United States

Recent research in causal inference has made important progress in addressing challenges to the external validity of trial findings. Such methods weight trial participant data to more closely resemble the distribution of effect-modifying covariates in a well-defined target population. In the presence of participant non-adherence to study medication, these methods effectively transport an intention-to-treat effect that averages over heterogeneous compliance behaviors. In this paper, we develop a principal stratification framework to identify causal effects conditioning on both compliance behavior and membership in the target population. We also develop non-parametric efficiency theory for and construct efficient estimators of such "transported" principal causal effects and characterize their finite-sample performance in simulation experiments. While this work focuses on treatment non-adherence, the framework is applicable to a broad class of estimands that target effects in clinically-relevant, possibly latent subsets of a target population. © 2024, CC BY.

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A RANDOM EFFECTS MODEL-BASED METHOD OF MOMENTS ESTIMATION OF CAUSAL EFFECT IN MENDELIAN RANDOMIZATION STUDIES

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arXiv 2023年

作者： Cao, Wenhao Basu, Saonli Division of Biostatistics and Health Data Science University of Minnesota MinneapolisMN United States

Recent advances in genotyping technology have delivered a wealth of genetic data, which is rapidly advancing our understanding of the underlying genetic architecture of complex diseases. Mendelian Randomization (MR) leverages such genetic data to estimate the causal effect of an exposure factor on an outcome from observational studies. In this paper, we utilize genetic correlations to summarize information on a large set of genetic variants associated with the exposure factor. Our proposed approach is a generalization of the MR-inverse variance weighting (IVW) approach where we can accommodate many weak and pleiotropic effects. Our approach quantifies the variation explained by all valid instrumental variables (IVs) instead of estimating the individual effects and thus could accommodate weak IVs. This is particularly useful for performing MR estimation in small studies, or minority populations where the selection of valid IVs is unreliable and thus has a large influence on the MR estimation. Through simulation and real data analysis, we demonstrate that our approach provides a robust alternative to the existing MR methods. We illustrate the robustness of our proposed approach under the violation of MR assumptions and compare the performance with several existing approaches. © 2023, CC BY.

关键词： Method of moments

MODIFIED TREATMENT POLICY EFFECT ESTIMATION WITH WEIGHTED ENERGY DISTANCE

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arXiv 2023年

作者： Jiang, Ziren Huling, Jared D. Division of Biostatistics and Health Data Science University of Minnesota MinneapolisMN United States

The causal effects of continuous treatments are often characterized through the average dose response function, which is challenging to estimate from observational data due to confounding and positivity violations. Modified treatment policies (MTPs) are an alternative approach that aim to assess the effect of a modification to observed treatment values and work under relaxed assumptions. Estimators for MTPs generally focus on estimating the conditional density of treatment given covariates and using it to construct weights. However, weighting using conditional density models has well-documented challenges. Further, MTPs with larger treatment modifications have stronger confounding and no tools exist to help choose an appropriate modification magnitude. This paper investigates the role of weights for MTPs showing that to control confounding, weights should balance the weighted data to an unobserved hypothetical target population that can be characterized with observed data. Leveraging this insight, we present a versatile set of tools to enhance estimation for MTPs. We introduce a distance that measures imbalance of covariate distributions under the MTP and use it to develop new weighting methods and tools to aid in the estimation of MTPs. Using our methods we study the effect of mechanical power of ventilation on in-hospital mortality. © 2023, CC BY.

关键词： Population statistics

Empirical Bayes Linked Matrix Decomposition

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arXiv 2024年

作者： Lock, Eric F. Division of Biostatistics and Health Data Science School of Public Health University of Minnesota MinneapolisMN55455 United States

data for several applications in diverse fields can be represented as multiple matrices that are linked across rows or columns. This is particularly common in molecular biomedical research, in which multiple molecular "omics" technologies may capture different feature sets (e.g., corresponding to rows in a matrix) and/or different sample populations (corresponding to columns). This has motivated a large body of work on integrative matrix factorization approaches that identify and decompose low-dimensional signal that is shared across multiple matrices or specific to a given matrix. We propose an empirical variational Bayesian approach to this problem that has several advantages over existing techniques, including the flexibility to accommodate shared signal over any number of row or column sets (i.e., bidimensional integration), an intuitive model-based objective function that yields appropriate shrinkage for the inferred signals, and a relatively efficient estimation algorithm with no tuning parameters. A general result establishes conditions for the uniqueness of the underlying decomposition for a broad family of methods that includes the proposed approach. For scenarios with missing data, we describe an associated iterative imputation approach that is novel for the single-matrix context and a powerful approach for "blockwise" imputation (in which an entire row or column is missing) in various linked matrix contexts. Extensive simulations show that the method performs very well under different scenarios with respect to recovering underlying low-rank signal, accurately decomposing shared and specific signals, and accurately imputing missing data. The approach is applied to gene expression and miRNA data from breast cancer tissue and normal breast tissue, for which it gives an informative decomposition of variation and outperforms alternative strategies for missing data imputation. © 2024, CC BY.

关键词： Matrix factorization

A Robust Score Test in G-computation for Covariate Adjustment in Randomized Clinical Trials Leveraging Different Variance Estimators via Influence Functions

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arXiv 2025年

作者： Zhang, Xin Chu, Haitao Liu, Lin Roychoudhury, Satrajit Data Sciences and Analytics Pfizer Inc Division of Biostatistics and Health Data Science University of Minnesota United States Institute of Natural Sciences MOE-LSC School of Mathematical Sciences CMA-Shanghai SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University China

G-computation has become a widely used robust method for estimating unconditional (marginal) treatment effects with covariate adjustment in the analysis of randomized clinical trials. Statistical inference in this context typically relies on the Wald test or Wald interval, which can be easily implemented using a consistent variance estimator. However, existing literature suggests that when sample sizes are small or when parameters of interest are near boundary values, Wald-based methods may be less reliable due to type I error rate inflation and insufficient interval coverage. In this article, we propose a robust score test for g-computation estimators in the context of two-sample treatment comparisons. The proposed test is asymptotically valid under simple and stratified (biased-coin) randomization schemes, even when regression models are misspecified. These test statistics can be conveniently computed using existing variance estimators, and the corresponding confidence intervals have closed-form expressions, making them convenient to implement. Through extensive simulations, we demonstrate the superior finite-sample performance of the proposed method. Finally, we apply the proposed method to reanalyze a completed randomized clinical trial. The new analysis using our proposed score test achieves statistical significance, whilst reducing the issue of type I error inflation. Copyright © 2025, The Authors. All rights reserved.

关键词： Regression analysis

A survey of Bayesian statistical methods in biomarker discovery and early clinical development

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Communications in Statistics Case Studies data Analysis and Applications 2024年第1期10卷 67-93页

作者： Paul, Erina Afzal, Avid M. Brown, Roland Cao, Shanshan Liu, Yushi Matejovicova, Tatiana Porwal, Anupreet Sun, Zhe Baumgartner, Richard Mallick, Himel Biostatistics and Research Decision Sciences Merck & Co. Inc Rahway NJ United States Data Sciences & Quantitative Biology Discovery Sciences R&D AstraZeneca Cambridge United Kingdom Nonclinical and Biomarker Biostatistics Biogen Inc Cambridge MA United States Global Statistical Science Lilly Research Laboratories Eli Lilly and Company Indianapolis IN United States Department of Statistics University of Washington Seattle WA United States Division of Biostatistics Department of Population Health Sciences Weill Cornell Medicine Cornell University New York NY United States Department of Statistics and Data Science Cornell University Ithaca NY United States

The increasing importance of uncertainty quantification in the regulatory evaluation of pharmaceutical products has triggered an explosion of Bayesian methods in recent years. In biomarker discovery and early clinical development, Bayesian methods have established a foothold in developing new drugs, in part due to the increasing availability of greater computational power, often complementing both traditional pharmaceutical statistics and classical statistical methods. In this paper, we present a selective survey of the recent efforts that have been made toward the development and application of effective statistical and computational models in early development statistics. The survey introduces four such case studies and methods that can be used for end-to-end biomarker discovery that includes pre-clinical and early clinical development, from unsupervised clustering to supervised machine learning to modern statistical inference, including but not limited to Bayesian clustering, Bayesian additive regression trees, Bayesian neural networks, and empirical Bayes procedures with the overarching goal of promoting their use and applications among pharmaceutical statisticians. Finally, we present some open issues in Bayesian early clinical methods to help guide the future advancement and wide adoption of Bayesian applications in early clinical pharmaceutical statistics. © 2023 Taylor & Francis Group, LLC.

关键词： Bayesian additive regression trees Bayesian clustering Bayesian discovery Bayesian neural networks early development statistics empirical Bayes

Multi-Study Causal Forest (MCF): A flexible framework for data borrowing in the presence of varying treatment effect heterogeneity

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arXiv 2025年

作者： Venkatasubramaniam, Ashwini Wolfson, Julian GlaxoSmithKline Stevenage United Kingdom Division of Biostatistics & Health Data Science School of Public Health University of Minnesota Minneapolis United States

Tailoring treatment assignment to specific individuals can improve the health outcomes, but a single study may offer inadequate information for this purpose. The ability to leverage information from an auxiliary data source deemed to be ‘most similar’ to a primary data source has been shown to improve estimates of treatment effects. In this paper, we introduce a framework, the Multi-Study Causal Forest (MCF), to borrow individual patient-level data from an auxiliary data source in the presence of ‘varying sources’ of treatment effect heterogeneity. We utilise a simulation study to demonstrate the superiority of the MCF in the presence of varying treatment allocation models (between-study heterogeneity) in addition to being able to account for the presence of within-study heterogeneity. This approach can combine data from randomised controlled trials, observational studies or a combination of both. We illustrate using Breast cancer data that the MCF performs favourably compared to an existing methodology in the presence of varying sources of (both between and within) heterogeneity. © 2025, CC BY.

关键词： data assimilation