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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Testing Gene-Environment Interactions In The Presence Of Measurement Error, Chongzhi Di, Li Hsu, Charles Kooperberg, Alex Reiner, Ross Prentice
Testing Gene-Environment Interactions In The Presence Of Measurement Error, Chongzhi Di, Li Hsu, Charles Kooperberg, Alex Reiner, Ross Prentice
UW Biostatistics Working Paper Series
Complex diseases result from an interplay between genetic and environmental risk factors, and it is of great interest to study the gene-environment interaction (GxE) to understand the etiology of complex diseases. Recent developments in genetics field allows one to study GxE systematically. However, one difficulty with GxE arises from the fact that environmental exposures are often measured with error. In this paper, we focus on testing GxE when the environmental exposure E is subject to measurement error. Surprisingly, contrast to the well-established results that the naive test ignoring measurement error is valid in testing the main effects, we find that …
Personalized Evaluation Of Biomarker Value: A Cost-Benefit Perspective, Ying Huang, Eric Laber
Personalized Evaluation Of Biomarker Value: A Cost-Benefit Perspective, Ying Huang, Eric Laber
UW Biostatistics Working Paper Series
For a patient who is facing a treatment decision, the added value of information provided by a biomarker depends on the individual patient’s expected response to treatment with and without the biomarker, as well as his/her tolerance of disease and treatment harm. However, individualized estimators of the value of a biomarker are lacking. We propose a new graphical tool named the subject-specific expected benefit curve for quantifying the personalized value of a biomarker in aiding a treatment decision. We develop semiparametric estimators for two general settings: i) when biomarker data are available from a randomized trial; and ii) when biomarker …
Nonparametric Identifiability Of Finite Mixture Models With Covariates For Estimating Error Rate Without A Gold Standard, Zheyu Wang, Xiao-Hua Zhou
Nonparametric Identifiability Of Finite Mixture Models With Covariates For Estimating Error Rate Without A Gold Standard, Zheyu Wang, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
Finite mixture models provide a flexible framework to study unobserved entities and have arisen in many statistical applications. The flexibility of these models in adapting various complicated structures makes it crucial to establish model identifiability when applying them in practice to ensure study validity and interpretation. However, researches to establish the identifiability of finite mixture model are limited and are usually restricted to a few specific model configurations. Conditions for model identifiability in the general case have not been established. In this paper, we provide conditions for both local identifiability and global identifiability of a finite mixture model. The former …
Efficiently Identifying Failures Using Quantitative Tests, Matrix-Pooling And The Em-Algorithm, Brett Hanscom, Susanne May, Jim Hughes
Efficiently Identifying Failures Using Quantitative Tests, Matrix-Pooling And The Em-Algorithm, Brett Hanscom, Susanne May, Jim Hughes
UW Biostatistics Working Paper Series
Pooled-testing methods can greatly reduce the number of tests needed to identify failures in a collection of samples. Existing methodology has focused primarily on binary tests, but there is a clear need for improved efficiency when using expensive quantitative tests, such as tests for HIV viral load in resource-limited settings. We propose a matrix-pooling method which, based on pooled-test results, uses the EM algorithm to identify individual samples most likely to be failures. Two hundred datasets for each of a wide range of failure prevalence were simulated to test the method. When the measurement of interest was normally distributed, at …
A Joint Model For Multistate Disease Processes And Random Informative Observation Times, With Applications To Electronic Medical Records Data, Jane M. Lange, Rebecca A. Hubbard, Lurdes Y. T. Inoue, Vladimir Minin
A Joint Model For Multistate Disease Processes And Random Informative Observation Times, With Applications To Electronic Medical Records Data, Jane M. Lange, Rebecca A. Hubbard, Lurdes Y. T. Inoue, Vladimir Minin
UW Biostatistics Working Paper Series
Multistate models are used to characterize individuals' natural histories through diseases with discrete states. Observational data resources based on electronic medical records pose new opportunities for studying such diseases. However, these data consist of observations of the process at discrete sampling times, which may either be pre-scheduled and non-informative, or symptom-driven and informative about an individual's underlying disease status. We have developed a novel joint observation and disease transition model for this setting. The disease process is modeled according to a latent continuous time Markov chain; and the observation process, according to a Markov-modulated Poisson process with observation rates that …
Change Point Testing In Logistic Regression Models With Interaction Term, Youyi Fong, Chongzhi Di, Sallie Permar
Change Point Testing In Logistic Regression Models With Interaction Term, Youyi Fong, Chongzhi Di, Sallie Permar
UW Biostatistics Working Paper Series
The threshold effect takes place in situations where the relationship between an outcome variable and a predictor variable changes as the predictor value crosses a certain threshold/change point. Threshold effects are often plausible in a complex biological system, especially in defining immune responses that are protective against infections such as HIV-1, which motivates the current work. We study two hypothesis testing problems in change point models. We first compare three different approaches to obtaining a p-value for the maximum of scores test in a logistic regression model with change point variable as a main effect. Next, we study the testing …