keyword
https://read.qxmd.com/read/38497824/fitting-the-cox-proportional-hazards-model-to-big-data
#1
JOURNAL ARTICLE
Jianqiao Wang, Donglin Zeng, Dan-Yu Lin
The semiparametric Cox proportional hazards model, together with the partial likelihood principle, has been widely used to study the effects of potentially time-dependent covariates on a possibly censored event time. We propose a computationally efficient method for fitting the Cox model to big data involving millions of study subjects. Specifically, we perform maximum partial likelihood estimation on a small subset of the whole data and improve the initial estimator by incorporating the remaining data through one-step estimation with estimated efficient score functions...
January 29, 2024: Biometrics
https://read.qxmd.com/read/38465988/simultaneous-variable-selection-and-estimation-in-semiparametric-regression-of-mixed-panel-count-data
#2
JOURNAL ARTICLE
Lei Ge, Tao Hu, Yang Li
Mixed panel count data represent a common complex data structure in longitudinal survey studies. A major challenge in analyzing such data is variable selection and estimation while efficiently incorporating both the panel count and panel binary data components. Analyses in the medical literature have often ignored the panel binary component and treated it as missing with the unknown panel counts, while obviously such a simplification does not effectively utilize the original data information. In this research, we put forward a penalized likelihood variable selection and estimation procedure under the proportional mean model...
January 29, 2024: Biometrics
https://read.qxmd.com/read/38405420/proximal-causal-inference-without-uniqueness-assumptions
#3
JOURNAL ARTICLE
Jeffrey Zhang, Wei Li, Wang Miao, Eric Tchetgen Tchetgen
We consider identification and inference about a counterfactual outcome mean when there is unmeasured confounding using tools from proximal causal inference. Proximal causal inference requires existence of solutions to at least one of two integral equations. We motivate the existence of solutions to the integral equations from proximal causal inference by demonstrating that, assuming the existence of a solution to one of the integral equations, <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msqrt><mml:mi>n</mml:mi></mml:msqrt></mml:mrow></mml:math>-estimability of a mean functional of that solution requires the existence of a solution to the other integral equation...
July 2023: Statistics & Probability Letters
https://read.qxmd.com/read/38405375/efficient-estimation-of-the-maximal-association-between-multiple-predictors-and-a-survival-outcome
#4
JOURNAL ARTICLE
Tzu-Jung Huang, Alex Luedtke, Ian W McKeague
This paper develops a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inference for right-censored outcome data has been investigated in the literature, but much remains to be done to make the methods both reliable and computationally-scalable in high-dimensions. Machine learning tools are commonly used to provide predictions of survival outcomes, but the estimated effect of a selected predictor suffers from confirmation bias unless the selection is taken into account...
October 2023: Annals of Statistics
https://read.qxmd.com/read/38393335/multiply-robust-estimators-in-longitudinal-studies-with-missing-data-under-control-based-imputation
#5
JOURNAL ARTICLE
Siyi Liu, Shu Yang, Yilong Zhang, Guanghan Frank Liu
Longitudinal studies are often subject to missing data. The recent guidance from regulatory agencies, such as the ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classical control-based scenario for the treatment effect evaluation, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group...
January 29, 2024: Biometrics
https://read.qxmd.com/read/38374966/asymptotic-properties-for-cumulative-probability-models-for-continuous-outcomes
#6
JOURNAL ARTICLE
Chun Li, Yuqi Tian, Donglin Zeng, Bryan E Shepherd
Regression models for continuous outcomes frequently require a transformation of the outcome, which is often specified a priori or estimated from a parametric family. Cumulative probability models (CPMs) nonparametrically estimate the transformation by treating the continuous outcome as if it is ordered categorically. They thus represent a flexible analysis approach for continuous outcomes. However, it is difficult to establish asymptotic properties for CPMs due to the potentially unbounded range of the transformation...
December 2, 2023: Mathematics
https://read.qxmd.com/read/38374497/bayesian-semiparametric-longitudinal-inverse-probit-mixed-models-for-category-learning
#7
JOURNAL ARTICLE
Minerva Mukhopadhyay, Jacie R McHaney, Bharath Chandrasekaran, Abhra Sarkar
Understanding how the adult human brain learns novel categories is an important problem in neuroscience. Drift-diffusion models are popular in such contexts for their ability to mimic the underlying neural mechanisms. One such model for gradual longitudinal learning was recently developed in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). In practice, category response accuracies are often the only reliable measure recorded by behavioral scientists to describe human learning. Category response accuracies are, however, often the only reliable measure recorded by behavioral scientists to describe human learning...
February 19, 2024: Psychometrika
https://read.qxmd.com/read/38370508/testing-the-missing-at-random-assumption-in-generalized-linear-models-in-the-presence-of-instrumental-variables
#8
JOURNAL ARTICLE
Rui Duan, C Jason Liang, Pamela A Shaw, Cheng Yong Tang, Yong Chen
Practical problems with missing data are common, and many methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus, there have been longstanding interests on the mechanism governing data missingness, and correctly deciding the appropriate mechanism is crucially relevant for conducting proper practical investigations. In this paper, we present a new hypothesis testing approach for deciding between the conventional notions of missing at random and missing not at random in generalized linear models in the presence of instrumental variables...
March 2024: Scandinavian Journal of Statistics, Theory and Applications
https://read.qxmd.com/read/38368350/smoothed-quantile-residual-life-regression-analysis-with-application-to-the-korea-hiv-aids-cohort-study
#9
JOURNAL ARTICLE
Soo Min Kim, Yunsu Choi, Sangwook Kang, Korea Hiv/Aids Cohort Study
BACKGROUND: The residual life of a patient with human immunodeficiency virus (HIV) is of major interest to patients and their physicians. While existing analyses of HIV patient survival focus mostly on data collected at baseline, residual life analysis allows for dynamic analysis based on additional data collected over a period of time. As survival times typically exhibit a right-skewed distribution, the median provides a more useful summary of the underlying distribution than the mean...
February 17, 2024: BMC Medical Research Methodology
https://read.qxmd.com/read/38364804/efficient-designs-and-analysis-of-two-phase-studies-with-longitudinal-binary-data
#10
JOURNAL ARTICLE
Chiara Di Gravio, Jonathan S Schildcrout, Ran Tao
Researchers interested in understanding the relationship between a readily available longitudinal binary outcome and a novel biomarker exposure can be confronted with ascertainment costs that limit sample size. In such settings, two-phase studies can be cost-effective solutions that allow researchers to target informative individuals for exposure ascertainment and increase estimation precision for time-varying and/or time-fixed exposure coefficients. In this paper, we introduce a novel class of residual-dependent sampling (RDS) designs that select informative individuals using data available on the longitudinal outcome and inexpensive covariates...
January 29, 2024: Biometrics
https://read.qxmd.com/read/38364801/multiobjective-tree-based-reinforcement-learning-for-estimating-tolerant-dynamic-treatment-regimes
#11
JOURNAL ARTICLE
Yao Song, Lu Wang
A dynamic treatment regime (DTR) is a sequence of treatment decision rules that dictate individualized treatments based on evolving treatment and covariate history. It provides a vehicle for optimizing a clinical decision support system and fits well into the broader paradigm of personalized medicine. However, many real-world problems involve multiple competing priorities, and decision rules differ when trade-offs are present. Correspondingly, there may be more than one feasible decision that leads to empirically sufficient optimization...
January 29, 2024: Biometrics
https://read.qxmd.com/read/38015378/a-bayesian-proportional-hazards-mixture-cure-model-for-interval-censored-data
#12
JOURNAL ARTICLE
Chun Pan, Bo Cai, Xuemei Sui
The proportional hazards mixture cure model is a popular analysis method for survival data where a subgroup of patients are cured. When the data are interval-censored, the estimation of this model is challenging due to its complex data structure. In this article, we propose a computationally efficient semiparametric Bayesian approach, facilitated by spline approximation and Poisson data augmentation, for model estimation and inference with interval-censored data and a cure rate. The spline approximation and Poisson data augmentation greatly simplify the MCMC algorithm and enhance the convergence of the MCMC chains...
November 28, 2023: Lifetime Data Analysis
https://read.qxmd.com/read/38007694/efficiency-of-the-breslow-estimator-in-semiparametric-transformation-models
#13
JOURNAL ARTICLE
Theresa P Devasia, Alexander Tsodikov
Semiparametric transformation models for failure time data consist of a parametric regression component and an unspecified cumulative baseline hazard. The nonparametric maximum likelihood estimator (NPMLE) of the cumulative baseline hazard can be summarized in terms of weights introduced into a Breslow-type estimator (Weighted Breslow). At any given time point, the weights invoke an integral over the future of the cumulative baseline hazard, which presents theoretical and computational challenges. A simpler non-MLE Breslow-type estimator (Breslow) was derived earlier from a martingale estimating equation (MEE) setting observed and expected counts of failures equal, conditional on the past history...
November 26, 2023: Lifetime Data Analysis
https://read.qxmd.com/read/37982010/efficient-estimation-under-data-fusion
#14
JOURNAL ARTICLE
Sijia Li, Alex Luedtke
We aim to make inferences about a smooth, finite-dimensional parameter by fusing data from multiple sources together. Previous works have studied the estimation of a variety of parameters in similar data fusion settings, including in the estimation of the average treatment effect and average reward under a policy, with the majority of them merging one historical data source with covariates, actions, and rewards and one data source of the same covariates. In this work, we consider the general case where one or more data sources align with each part of the distribution of the target population, for example, the conditional distribution of the reward given actions and covariates...
December 2023: Biometrika
https://read.qxmd.com/read/37920405/deciphering-the-gut-brain-axis-through-microbiome-diversity
#15
REVIEW
Jinyuan Liu, Ke Xu, Tsungchin Wu, Lydia Yao, Tanya T Nguyen, Dilip Jeste, Xinlian Zhang
Incentivised by breakthroughs and data generated by the high-throughput sequencing technology, this paper proposes a distance-based framework to fulfil the emerging needs in elucidating insights from the high-dimensional microbiome data in psychiatric studies. By shifting focus from traditional methods that focus on the observations from each subject to the between-subject attributes that aggregate two or more subjects' entire feature vectors, the described approach revolutionises the conventional prescription for high-dimensional observations via microbiome diversity...
2023: General Psychiatry
https://read.qxmd.com/read/37895580/optimal-estimation-of-quantum-coherence-by-bell-state-measurement-a-case-study
#16
JOURNAL ARTICLE
Yuan Yuan, Xufeng Huang, Yueping Niu, Shangqing Gong
Quantum coherence is the most distinguished feature of quantum mechanics. As an important resource, it is widely applied to quantum information technologies, including quantum algorithms, quantum computation, quantum key distribution, and quantum metrology, so it is important to develop tools for efficient estimation of the coherence. Bell state measurement plays an important role in quantum information processing. In particular, it can also, as a two-copy collective measurement, directly measure the quantum coherence of an unknown quantum state in the experiment, and does not need any optimization procedures, feedback, or complex mathematical calculations...
October 17, 2023: Entropy
https://read.qxmd.com/read/37855203/functional-proportional-hazards-mixture-cure-model-with-applications-in-cancer-mortality-in-nhanes-and-post-icu-recovery
#17
JOURNAL ARTICLE
Rahul Ghosal, Marcos Matabuena, Jiajia Zhang
We develop a functional proportional hazards mixture cure model with scalar and functional covariates measured at the baseline. The mixture cure model, useful in studying populations with a cure fraction of a particular event of interest is extended to functional data. We employ the expectation-maximization algorithm and develop a semiparametric penalized spline-based approach to estimate the dynamic functional coefficients of the incidence and the latency part. The proposed method is computationally efficient and simultaneously incorporates smoothness in the estimated functional coefficients via roughness penalty...
October 19, 2023: Statistical Methods in Medical Research
https://read.qxmd.com/read/37771511/time-to-event-analysis-with-unknown-time-origins-via-longitudinal-biomarker-registration
#18
JOURNAL ARTICLE
Tianhao Wang, Sarah J Ratcliffe, Wensheng Guo
In observational studies, the time origin of interest for time-to-event analysis is often unknown, such as the time of disease onset. Existing approaches to estimating the time origins are commonly built on extrapolating a parametric longitudinal model, which rely on rigid assumptions that can lead to biased inferences. In this paper, we introduce a flexible semiparametric curve registration model. It assumes the longitudinal trajectories follow a flexible common shape function with person-specific disease progression pattern characterized by a random curve registration function, which is further used to model the unknown time origin as a random start time...
2023: Journal of the American Statistical Association
https://read.qxmd.com/read/37641532/optimizing-treatment-allocation-in-randomized-clinical-trials-by-leveraging-baseline-covariates
#19
JOURNAL ARTICLE
Wei Zhang, Zhiwei Zhang, Aiyi Liu
We consider the problem of optimizing treatment allocation for statistical efficiency in randomized clinical trials. Optimal allocation has been studied previously for simple treatment effect estimators such as the sample mean difference, which are not fully efficient in the presence of baseline covariates. More efficient estimators can be obtained by incorporating covariate information, and modern machine learning methods make it increasingly feasible to approach full efficiency. Accordingly, we derive the optimal allocation ratio by maximizing the design efficiency of a randomized trial, assuming that an efficient estimator will be used for analysis...
August 28, 2023: Biometrics
https://read.qxmd.com/read/37593690/estimating-the-efficiency-gain-of-covariate-adjusted-analyses-in-future-clinical-trials-using-external-data
#20
JOURNAL ARTICLE
Xiudi Li, Sijia Li, Alex Luedtke
We present a framework for using existing external data to identify and estimate the relative efficiency of a covariate-adjusted estimator compared to an unadjusted estimator in a future randomized trial. Under conditions, these relative efficiencies approximate the ratio of sample sizes needed to achieve a desired power. We develop semiparametrically efficient estimators of the relative efficiencies for several treatment effect estimands of interest with either fully or partially observed outcomes, allowing for the application of flexible statistical learning tools to estimate the nuisance functions...
April 2023: Journal of the Royal Statistical Society. Series B, Statistical Methodology
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