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Ruitao Lin, Ying Yuan
Two useful strategies to speed up drug development are to increase the patient accrual rate and use novel adaptive designs. Unfortunately, these two strategies often conflict when the evaluation of the outcome cannot keep pace with the patient accrual rate and thus the interim data cannot be observed in time to make adaptive decisions. A similar logistic difficulty arises when the outcome is late-onset. Based on a novel formulation and approximation of the likelihood of the observed data, we propose a general methodology for model-assisted designs to handle toxicity data that are pending due to fast accrual or late-onset toxicity and facilitate seamless decision making in phase I dose-finding trials...
April 15, 2019: Biostatistics
Glen McGee, Marc G Weisskopf, Marianthi-Anna Kioumourtzoglou, Brent A Coull, Sebastien Haneuse
Exposures with multigenerational effects have profound implications for public health, affecting increasingly more people as the exposed population reproduces. Multigenerational studies, however, are susceptible to informative cluster size, occurring when the number of children to a mother (the cluster size) is related to their outcomes, given covariates. A natural question then arises: what if some women bear no children at all? The impact of these potentially informative empty clusters is currently unknown...
April 8, 2019: Biostatistics
Joshua L Warren, Wenjing Kong, Thomas J Luben, Howard H Chang
Understanding the impact that environmental exposure during different stages of pregnancy has on the risk of adverse birth outcomes is vital for protection of the fetus and for the development of mechanistic explanations of exposure-disease relationships. As a result, statistical models to estimate critical windows of susceptibility have been developed for several different reproductive outcomes and pollutants. However, these current methods fail to adequately address the primary objective of this line of research; how to statistically identify a critical window of susceptibility...
April 8, 2019: Biostatistics
Nicholas P Jewell, Suzanne Dufault, Zoe Cutcher, Cameron P Simmons, Katherine L Anders
No abstract text is available yet for this article.
April 3, 2019: Biostatistics
Maryan Morel, Emmanuel Bacry, Stéphane Gaïffas, Agathe Guilloux, Fanny Leroy
With the increased availability of large electronic health records databases comes the chance of enhancing health risks screening. Most post-marketing detection of adverse drug reaction (ADR) relies on physicians' spontaneous reports, leading to under-reporting. To take up this challenge, we develop a scalable model to estimate the effect of multiple longitudinal features (drug exposures) on a rare longitudinal outcome. Our procedure is based on a conditional Poisson regression model also known as self-controlled case series (SCCS)...
March 8, 2019: Biostatistics
Ramon Oller, Guadalupe Gómez Melis
Many biomedical studies focus on the association between a longitudinal measurement and a time-to-event outcome while quantifying this association by means of a longitudinal-survival joint model. In this article we propose using the $LLR$ test - a longitudinal extension of the log-rank test statistic given by Peto and Peto (1972) - to provide evidence of a plausible association between a time-to-event outcome (right- or interval-censored) and a time-dependent covariate. As joint model methods are complex and hard to interpret, it is wise to conduct a preliminary test such as $LLR$ for checking the association between both processes...
February 23, 2019: Biostatistics
Lizbeth Naranjo, Carlos J Pérez, Ruth Fuentes-García, Jacinto Martín
Motivated by a study tracking the progression of Parkinson's disease (PD) based on features extracted from voice recordings, an inhomogeneous hidden Markov model with continuous state-space is proposed. The approach addresses the measurement error in the response, the within-subject variability of the replicated covariates and presumed nondecreasing response. A Bayesian framework is described and an efficient Markov chain Monte Carlo method is developed. The model performance is evaluated through a simulation-based example and the analysis of a PD tracking progression dataset is presented...
February 23, 2019: Biostatistics
Sean W Jewell, Toby Dylan Hocking, Paul Fearnhead, Daniela M Witten
Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exact moment in time at which a neuron spikes, from a calcium imaging data set, amounts to a non-trivial deconvolution problem which is of critical importance for downstream analyses. While a number of formulations have been proposed for this task in the recent literature, in this article, we focus on a formulation recently proposed in Jewell and Witten (2018...
February 8, 2019: Biostatistics
Lucy L Gao, Jacob Bien, Daniela Witten
In the Pioneer 100 (P100) Wellness Project, multiple types of data are collected on a single set of healthy participants at multiple timepoints in order to characterize and optimize wellness. One way to do this is to identify clusters, or subgroups, among the participants, and then to tailor personalized health recommendations to each subgroup. It is tempting to cluster the participants using all of the data types and timepoints, in order to fully exploit the available information. However, clustering the participants based on multiple data views implicitly assumes that a single underlying clustering of the participants is shared across all data views...
February 8, 2019: Biostatistics
Ioannis Vardaxis, Finn Drabløs, Morten B Rye, Bo Henry Lindqvist
We present model-based analysis for ChIA-PET (MACPET), which analyzes paired-end read sequences provided by ChIA-PET for finding binding sites of a protein of interest. MACPET uses information from both tags of each PET and searches for binding sites in a two-dimensional space, while taking into account different noise levels in different genomic regions. MACPET shows favorable results compared with MACS in terms of motif occurrence and spatial resolution. Furthermore, significant binding sites discovered by MACPET are involved in a higher number of significant three-dimensional interactions than those discovered by MACS...
January 30, 2019: Biostatistics
Klaus Kähler Holst, Esben Budtz-Jørgensen
Applications of structural equation models (SEMs) are often restricted to linear associations between variables. Maximum likelihood (ML) estimation in non-linear models may be complex and require numerical integration. Furthermore, ML inference is sensitive to distributional assumptions. In this article, we introduce a simple two-stage estimation technique for estimation of non-linear associations between latent variables. Here both steps are based on fitting linear SEMs: first a linear model is fitted to data on the latent predictor and terms describing the non-linear effect are predicted by their conditional means...
January 29, 2019: Biostatistics
Min Jin Ha, Wei Sun
Directed acyclic graphs (DAGs) have been used to describe causal relationships between variables. The standard method for determining such relations uses interventional data. For complex systems with high-dimensional data, however, such interventional data are often not available. Therefore, it is desirable to estimate causal structure from observational data without subjecting variables to interventions. Observational data can be used to estimate the skeleton of a DAG and the directions of a limited number of edges...
December 31, 2018: Biostatistics
Ziyi Li, Changgee Chang, Suprateek Kundu, Qi Long
Biclustering techniques can identify local patterns of a data matrix by clustering feature space and sample space at the same time. Various biclustering methods have been proposed and successfully applied to analysis of gene expression data. While existing biclustering methods have many desirable features, most of them are developed for continuous data and few of them can efficiently handle -omics data of various types, for example, binomial data as in single nucleotide polymorphism data or negative binomial data as in RNA-seq data...
December 31, 2018: Biostatistics
Yumou Qiu, Xiao-Hua Zhou
Alzheimer's disease (AD) is a chronic neurodegenerative disease that changes the functional connectivity of the brain. The alteration of the strong connections between different brain regions is of particular interest to researchers. In this article, we use partial correlations to model the brain connectivity network and propose a data-driven procedure to recover a $c$-level partial correlation graph based on PET data, which is the graph of the absolute partial correlations larger than a pre-specified constant $c$...
December 31, 2018: Biostatistics
Kyu Ha Lee, Brent A Coull, Anna-Barbara Moscicki, Bruce J Paster, Jacqueline R Starr
Microorganisms play critical roles in human health and disease. They live in diverse communities in which they interact synergistically or antagonistically. Thus for estimating microbial associations with clinical covariates, such as treatment effects, joint (multivariate) statistical models are preferred. Multivariate models allow one to estimate and exploit complex interdependencies among multiple taxa, yielding more powerful tests of exposure or treatment effects than application of taxon-specific univariate analyses...
December 26, 2018: Biostatistics
Elin Shaddox, Christine B Peterson, Francesco C Stingo, Nicola A Hanania, Charmion Cruickshank-Quinn, Katerina Kechris, Russell Bowler, Marina Vannucci
In this article, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms...
December 26, 2018: Biostatistics
Francesca Gasperoni, Francesca Ieva, Anna Maria Paganoni, Christopher H Jackson, Linda Sharples
We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty...
December 26, 2018: Biostatistics
Sixing Chen, Xihong Lin
With the advent of next-generation sequencing, investigators have access to higher quality sequencing data. However, to sequence all samples in a study using next generation sequencing can still be prohibitively expensive. One potential remedy could be to combine next generation sequencing data from cases with publicly available sequencing data for controls, but there could be a systematic difference in quality of sequenced data, such as sequencing depths, between sequenced study cases and publicly available controls...
December 26, 2018: Biostatistics
Lu Wang, Alexander R Luedtke, Ying Huang
In early detection of disease, a single biomarker often has inadequate classification performance, making it important to identify new biomarkers to combine with the existing marker for improved performance. A biologically natural method for combining biomarkers is to use logic rules, e.g., the OR/AND rules. In our motivating example of early detection of pancreatic cancer, the established biomarker CA19-9 is only present in a subclass of cancers; it is of interest to identify new biomarkers present in the other subclasses and declare disease when either marker is positive...
December 26, 2018: Biostatistics
Michal Juraska, Ying Huang, Peter B Gilbert
An objective in randomized clinical trials is the evaluation of "principal surrogates," which consists of analyzing how the treatment effect on a clinical endpoint varies over principal strata subgroups defined by an intermediate response outcome under both or one of the treatment assignments. The latter effect modification estimand has been termed the marginal causal effect predictiveness (mCEP) curve. This objective was addressed in two randomized placebo-controlled Phase 3 dengue vaccine trials for an antibody response biomarker whose sampling design rendered previously developed inferential methods highly inefficient due to a three-phase sampling design...
December 26, 2018: Biostatistics
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