265 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of WashingtonUS61 papers
- Cape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South AfricaZA22 papers
- University of California, BerkeleyUS11 papers
- University of California, Los AngelesUS10 papers
- Cancer Research CenterUS9 papers
- Cancer Research And BiostatisticsUS7 papers
- Howard Hughes Medical InstituteUS7 papers
- University of Wisconsin–MadisonUS7 papers
- Max Planck Institute for Dynamics and Self-OrganizationDE6 papers
- University of California, IrvineUS6 papers
- University of AlbertaCA5 papers
- University of PennsylvaniaUS5 papers
44 papers · 1 filter
Randomized interventional effects in semicompeting risks, with application to a hematopoietic cell transplantation study
Yuhao Deng, Rui Wang, Tao Zhang +1
In clinical studies, the risk of the primary (terminal) event may be modified by intermediate events, resulting in semicompeting risks. To study the treatment effect on the termina…
ipd: An R Package for Conducting Inference on Predicted Data
Stephen Salerno, Jiacheng Miao, Awan Afiaz +5
Summary: ipd is an open-source R software package for the downstream modeling of an outcome and its associated features where a potentially sizable portion of the outcome data has…
Assessing variable importance in survival analysis using machine learning
Charles J. Wolock, Peter B. Gilbert, Noah Simon +1
Given a collection of features available for inclusion in a predictive model, it may be of interest to quantify the relative importance of a subset of features for the prediction t…
Generalized Data Thinning Using Sufficient Statistics
Ameer Dharamshi, Anna Neufeld, Keshav Motwani +3
Our goal is to develop a general strategy to decompose a random variable into multiple independent random variables, without sacrificing any information about unknown parameter…
A framework for leveraging machine learning tools to estimate personalized survival curves
Charles J. Wolock, Peter B. Gilbert, Noah Simon +1
The conditional survival function of a time-to-event outcome subject to censoring and truncation is a common target of estimation in survival analysis. This parameter may be of sci…
Flexible variable selection in the presence of missing data
B. D. Williamson, Y. Huang
In many applications, it is of interest to identify a parsimonious set of features, or panel, from multiple candidates that achieves a desired level of performance in predicting a…
Efficient nonparametric estimation of the covariate-adjusted threshold-response function, a support-restricted stochastic intervention
Lars van der Laan, Wenbo Zhang, Peter B. Gilbert
Identifying a biomarker or treatment-dose threshold that marks a specified level of risk is an important problem, especially in clinical trials. This risk, viewed as a function of…
Statistical Considerations for Cross-Sectional HIV Incidence Estimation Based on Recency Test
Fei Gao, Marlena S. Bannick
Longitudinal cohorts to determine the incidence of HIV infection are logistically challenging, so researchers have sought alternative strategies. Recency test methods use biomarker…
Sample Size Calculation for Active-Arm Trial with Counterfactual Incidence Based on Recency Assay
Fei Gao, David V. Glidden, James P. Hughes +1
The past decade has seen tremendous progress in the development of biomedical agents that are effective as pre-exposure prophylaxis (PrEP) for HIV prevention. To expand the choice…
Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score
Muxuan Liang, Young-Geun Choi, Yang Ning +2
With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules, which recommend treatments according to pat…
Efficient nonparametric statistical inference on population feature importance using Shapley values
Brian D. Williamson, Jean Feng
The true population-level importance of a variable in a prediction task provides useful knowledge about the underlying data-generating mechanism and can help in deciding which meas…
Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials
Nima S. Hejazi, Mark J. van der Laan, Holly E. Janes +2
The advent and subsequent widespread availability of preventive vaccines has altered the course of public health over the past century. Despite this success, effective vaccines to…
A general framework for inference on algorithm-agnostic variable importance
Brian D. Williamson, Peter B. Gilbert, Noah R. Simon +1
In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response -- in other words, to gaug…
An Approximate Quasi-Likelihood Approach for Error-Prone Failure Time Outcomes and Exposures
Lillian A. Boe, Lesley F. Tinker, Pamela A. Shaw
Measurement error arises commonly in clinical research settings that rely on data from electronic health records or large observational cohorts. In particular, self-reported outcom…
Risk Projection for Time-to-event Outcome Leveraging Summary Statistics With Source Individual-level Data
Zheng Jiayin, Zheng Yingye, Hsu Li
Predicting risks of chronic diseases has become increasingly important in clinical practice. When a prediction model is developed in a given source cohort, there is often a great i…
Robust Estimation and Variable Selection for the Accelerated Failure Time Model
Yi Li, Muxuan Liang, Lu Mao +1
This paper considers robust modeling of the survival time for cancer patients. Accurate prediction can be helpful for developing therapeutic and care strategies. We propose a unifi…
Estimands and Inference in Cluster-Randomized Vaccine Trials
Kayla W. Kilpatrick, Michael G. Hudgens, M. Elizabeth Halloran
Cluster-randomized trials are often conducted to assess vaccine effects. Defining estimands of interest before conducting a trial is integral to the alignment between a study's obj…
A Bayesian Phylogenetic Hidden Markov Model for B Cell Receptor Sequence Analysis
Amrit Dhar, Duncan K. Ralph, Vladimir N. Minin +1
The human body is able to generate a diverse set of high affinity antibodies, the soluble form of B cell receptors (BCRs), that bind to and neutralize invading pathogens. The natur…
Efficient augmentation and relaxation learning for individualized treatment rules using observational data
Ying-Qi Zhao, Eric B. Laber, Yang Ning +2
Individualized treatment rules aim to identify if, when, which, and to whom treatment should be applied. A globally aging population, rising healthcare costs, and increased access…
-sample omnibus non-proportional hazards tests based on right-censored data
Malka Gorfine, Matan Schlesinger, Li Hsu
This work presents novel and powerful tests for comparing non-proportional hazard functions, based on sample-space partitions. Right censoring introduces two major difficulties whi…
Gaussian process regression for survival time prediction with genome-wide gene expression
Aaron J. Molstad, Li Hsu, Wei Sun
Predicting the survival time of a cancer patient based on his/her genome-wide gene expression remains a challenging problem. For certain types of cancer, the effects of gene expres…
Constructing Stabilized Dynamic Treatment Regimes for Censored Data
Ying-Qi Zhao, Ruoqing Zhu, Guanhua Chen +1
Stabilized dynamic treatment regimes are sequential decision rules for individual patients that not only adaptive throughout the disease progression but also remain consistent over…
Simultaneous disease mapping and hot spot detection with application to childhood obesity surveillance from electronic health records
Young-Geun Choi, Lawrence P. Hanrahan, Derek Norton +1
Electronic health records (EHRs) have become a platform for data-driven surveillance on a granular level in recent years. In this paper, we make use of EHRs for early prevention of…
A Semiparametric Approach to Model Effect Modification
Muxuan Liang, Menggang Yu
One fundamental statistical question for research areas such as precision medicine and health disparity is about discovering effect modification of treatment or exposure by observe…