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Statistics in Biosciences

Yunxuan Jiang, Karen N Conneely, Michael P Epstein
Next generation sequencing technology has propelled the development of statistical methods to identify rare polygenetic variation associated with complex traits. The majority of these statistical methods are designed for case-control or population-based studies, with few methods that are applicable to family-based studies. Moreover, existing methods for family-based studies mainly focus on trios or nuclear families; there are far fewer existing methods available for analyzing larger pedigrees of arbitrary size and structure...
December 2018: Statistics in Biosciences
Guosheng Yin, Nan Chen, J Jack Lee
There has been much development in Bayesian adaptive designs in clinical trials. In the Bayesian paradigm, the posterior predictive distribution characterizes the future possible outcomes given the currently observed data. Based on the interim time-to-event data, we develop a new phase II trial design by combining the strength of both Bayesian adaptive randomization and the predictive probability. By comparing the mean survival times between patients assigned to two treatment arms, more patients are assigned to the better treatment on the basis of adaptive randomization...
August 2018: Statistics in Biosciences
Wenqing Li, Ming-Hui Chen, Xiaojing Wangy, Dipak K Dey
We developed a Bayes factor based approach for the design of non-inferiority clinical trials with a focus on controlling type I error and power. Historical data are incorporated in the Bayesian design via the power prior discussed in Ibrahim and Chen (2000). The properties of the proposed method are examined in detail. An efficient simulation-based computational algorithm is developed to calculate the Bayes factor, type I error and power. The proposed methodology is applied to the design of a non-inferiority medical device clinical trial...
August 2018: Statistics in Biosciences
Fei Gao, Donglin Zeng, Helen Wei, Xuena Wang, Joseph G Ibrahim
In many clinical studies, patients may experience the same type of event of interest repeatedly over time. However, the assessment of treatment effects is often complicated by the rescue medication uses due to ethical reasons. For example, in the motivating trial in studying the Immune Thrombocytopenia (ITP), when the interest lies in evaluating the treatment benefit of investigational product (IP) on reducing patient's repeated bleeding, rescue medication such as platelet transfusions may be allowed to raise platelet counts...
August 2018: Statistics in Biosciences
Clemontina A Davenport, Arnab Maity, Patrick F Sullivan, Jung-Ying Tzeng
Evaluating multiple binary outcomes is common in genetic studies of complex diseases. These outcomes are often correlated because they are collected from the same individual and they may share common marker effects. In this paper, we propose a procedure to test for effect of a SNP-set on multiple, possibly correlated, binary responses. We develop a score-based test using a nonparametric modeling framework that jointly models the global effect of the marker set. We account for the nonlinear effects and potentially complicated interaction between markers using reproducing kernels...
April 2018: Statistics in Biosciences
Chunyan Cai, Jin Piao, Jing Ning, Xuelin Huang
Cost-effective yet efficient designs are critical to the success of animal studies. We propose a two-stage design for cost-effectiveness animal studies with continuous outcomes. Given the data from the two-stage design, we derive the exact distribution of the test statistic under null hypothesis to appropriately adjust for the design's adaptiveness. We further generalize the design and inferential procedure to the K -sample case with multiple comparison adjustment. We conduct simulation studies to evaluate the small sample behavior of the proposed design and test procedure...
April 2018: Statistics in Biosciences
Mathieu Bray, Wen Wang, Peter X-K Song, John D Kalbfleisch
In kidney paired donation (KPD), incompatible donor-candidate pairs and non-directed (also known as altruistic) donors are pooled together with the aim of maximizing the total utility of transplants realized via donor exchanges. We consider a setting in which disjoint sets of potential transplants are selected at regular intervals, with fallback options available within each proposed set in the case of individual donor, candidate or match failure. We develop methods for calculating the expected utility for such sets under a realistic probability model for the KPD...
April 2018: Statistics in Biosciences
Yi-Hui Zhou, Paul Brooks, Xiaoshan Wang
It has been recognized that for appropriately ordered data, hidden Markov models (HMM) with local false discovery rate (FDR) control can increase the power to detect significant associations. For many high-throughput technologies, the cost still limits their application. Two-stage designs are attractive, in which a set of interesting features or biomarkers is identified in a first stage, and then followed up in a second stage. However, to our knowledge no two-stage FDR control with HMMs has been developed. In this paper, we study an efficient HMM-FDR based two-stage design, using a simple integrated analysis procedure across the stages...
April 2018: Statistics in Biosciences
Yujie Zhong, Richard J Cook
Studies about the genetic basis for disease are routinely conducted through family studies under response-dependent sampling in which affected individuals called probands are sampled from a disease registry, and their respective family members (non-probands) are recruited for study. The extent to which the dependence in some feature of the disease process (e.g., presence, age of onset, severity) varies according to the kinship of individuals reflects the evidence of a genetic cause for disease. When the probands are selected from a disease registry, it is common for them to provide quite detailed information regarding their disease history, but non-probands often simply provide their disease status at the time of contact...
2018: Statistics in Biosciences
Douglas E Schaubel
No abstract text is available yet for this article.
December 2017: Statistics in Biosciences
Sherri Rose, Julie Shi, Thomas G McGuire, Sharon-Lise T Normand
New state-level health insurance markets, denoted Marketplaces , created under the Affordable Care Act, use risk-adjusted plan payment formulas derived from a population ineligible to participate in the Marketplaces. We develop methodology to derive a sample from the target population and to assemble information to generate improved risk-adjusted payment formulas using data from the Medical Expenditure Panel Survey and Truven MarketScan databases. Our approach requires multi-stage data selection and imputation procedures because both data sources have systemic missing data on crucial variables and arise from different populations...
December 2017: Statistics in Biosciences
Wei Yang, Dawei Xie, Qiang Pan, Harold I Feldman, Wensheng Guo
We are motivated by the Chronic Renal Insufficiency Cohort (CRIC) study to identify risk factors for renal progression in patients with chronic kidney diseases. The CRIC study collects two types of renal outcomes: glomerular filtration rate (GFR) estimated annually and end stage renal disease (ESRD). A related outcome of interest is death which is a competing event for ESRD. A joint modeling approach is proposed to model a longitudinal outcome and two competing survival outcomes. We assume multivariate normality on the joint distribution of the longitudinal and survival outcomes...
December 2017: Statistics in Biosciences
Ling Zhou, Lu Tang, Angela T Song, Diane M Cibrik, Peter X-K Song
Identifying novel biomarkers to predict renal graft survival is important in post-transplant clinical practice. Serum creatinine, currently the most popular surrogate biomarker, offers limited information of the underlying allograft profiles. It is known to perform unsatisfactorily to predict renal function. In this paper, we apply a LASSO machine-learning algorithm in the Cox proportional hazards model to identify promising proteins that are associated with the hazard of allograft loss after renal transplantation, motivated by a clinical pilot study that collected 47 patients receiving renal transplants at the University of Michigan Hospital...
December 2017: Statistics in Biosciences
Yenny Webb-Vargas, Shaojie Chen, Aaron Fisher, Amanda Mejia, Yuting Xu, Ciprian Crainiceanu, Brian Caffo, Martin A Lindquist
Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems...
December 2017: Statistics in Biosciences
Xu Shu, Douglas E Schaubel
In studies featuring a sequence of ordered events, gap times between successive events are often of interest. Despite the rich literature in this area, very few methods for comparing gap times have been developed. We propose methods for estimating a hazard ratio connecting the first and second gap times. Specifically, a two-stage procedure is developed based on estimating equations. At the first stage, a proportional hazards model is fitted for the first gap time. Weighted estimating equations are then solved at the second stage to estimate the hazard ratio between the first and second gap times...
December 2017: Statistics in Biosciences
Hristina Pashova, Michael LeBlanc, Charles Kooperberg
When considering low-dimensional gene-treatment or gene-environment interactions we might suspect groups of genes to interact with treatment or environment in a similar way. For example, genes associated with related biological processes might interact with an environmental factor or a clinical treatment in its effect on a phenotype correspondingly. We use the idea of a structured interaction model together with penalized regression to limit the model complexity in a model in which we believe the interactions might behave in a similar way...
December 2017: Statistics in Biosciences
Liang Li, Sheng Luo, Bo Hu, Tom Greene
In longitudinal studies, prognostic biomarkers are often measured longitudinally. It is of both scientific and clinical interest to predict the risk of clinical events, such as disease progression or death, using these longitudinal biomarkers as well as other time-dependent and time-independent information about the patient. The prediction is dynamic in the sense that it can be made at any time during the follow-up, adapting to the changing at-risk population and incorporating the most recent longitudinal data...
December 2017: Statistics in Biosciences
Wen Wang, Mathieu Bray, Peter X-K Song, John D Kalbfleisch
While there is a growing need for kidney transplants to treat end stage kidney disease, the supply of transplantable kidneys is in serious shortage. Kidney paired donation (KPD) programs serve as platforms for candidates with willing but incompatible donors to assess the possibility of exchanging donors, thus opening up new transplant opportunities for these candidates. In recent years, non-directed (or altruistic) donors (NDDs) have been incorporated into KPD programs beginning chains of transplants that benefit many candidates...
December 2017: Statistics in Biosciences
J Choi, S Ye, K H Eng, K Korthauer, W H Bradley, J S Rader, C Kendziorski
Despite improvements in operative management and therapies, overall survival rates in advanced ovarian cancer have remained largely unchanged over the past three decades. Although it is possible to identify high-risk patients following surgery, the knowledge does not provide information about the genomic aberrations conferring risk, or the implications for treatment. To address these challenges, we developed an integrative pathway-index model and applied it to messenger RNA expression from 458 patients with serous ovarian carcinoma from the Cancer Genome Atlas project...
June 2017: Statistics in Biosciences
Eric Z Chen, Frederic D Bushman, Hongzhe Li
The human microbiome, which includes the collective microbes residing in or on the human body, has a profound influence on the human health. DNA sequencing technology has made the large-scale human microbiome studies possible by using shotgun metagenomic sequencing. One important aspect of data analysis of such metagenomic data is to quantify the bacterial abundances based on the metagenomic sequencing data. Existing methods almost always quantify such abundances one sample at a time, which ignore certain systematic differences in read coverage along the genomes due to GC contents, copy number variation and the bacterial origin of replication...
June 2017: Statistics in Biosciences
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