International Journal of Biostatistics最新文献

{"title":"Maximum Likelihood Estimation in a Semicontinuous Survival Model with Covariates Subject to Detection Limits.","authors":"Paul W Bernhardt","doi":"10.1515/ijb-2017-0058","DOIUrl":"https://doi.org/10.1515/ijb-2017-0058","url":null,"abstract":"<p><p>Semicontinuous data are common in biological studies, occurring when a variable is continuous over a region but has a point mass at one or more points. In the motivating Genetic and Inflammatory Markers of Sepsis (GenIMS) study, it was of interest to determine how several biomarkers subject to detection limits were related to survival for patients entering the hospital with community acquired pneumonia. While survival times were recorded for all individuals in the study, the primary endpoint of interest was the binary event of 90-day survival, and no patients were lost to follow-up prior to 90 days. In order to use all of the available survival information, we propose a two-part regression model where the probability of surviving to 90 days is modeled using logistic regression and the survival distribution for those experiencing the event prior to this time is modeled with a truncated accelerated failure time model. We assume a series of mixture of normal regression models to model the joint distribution of the censored biomarkers. To estimate the parameters in this model, we suggest a Monte Carlo EM algorithm where multiple imputations are generated for the censored covariates in order to estimate the expectation in the E-step and then weighted maximization is applied to the observed and imputed data in the M-step. We conduct simulations to assess the proposed model and maximization method, and we analyze the GenIMS data set.</p>","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"14 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36633426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Causal Effects from Nonparanormal Observational Data.","authors":"Seyed Mahdi Mahmoudi,&nbsp;Ernst C Wit","doi":"10.1515/ijb-2018-0030","DOIUrl":"https://doi.org/10.1515/ijb-2018-0030","url":null,"abstract":"<p><p>One of the basic aims of science is to unravel the chain of cause and effect of particular systems. Especially for large systems, this can be a daunting task. Detailed interventional and randomized data sampling approaches can be used to resolve the causality question, but for many systems, such interventions are impossible or too costly to obtain. Recently, Maathuis et al. (2010), following ideas from Spirtes et al. (2000), introduced a framework to estimate causal effects in large scale Gaussian systems. By describing the causal network as a directed acyclic graph it is a possible to estimate a class of Markov equivalent systems that describe the underlying causal interactions consistently, even for non-Gaussian systems. In these systems, causal effects stop being linear and cannot be described any more by a single coefficient. In this paper, we derive the general functional form of a causal effect in a large subclass of non-Gaussian distributions, called the non-paranormal. We also derive a convenient approximation, which can be used effectively in estimation. We show that the estimate is consistent under certain conditions and we apply the method to an observational gene expression dataset of the Arabidopsis thaliana circadian clock system.</p>","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"14 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2018-0030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36452297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Truncation Model for Estimating Species Richness.","authors":"Babagnidé François Koladjo,&nbsp;Mesrob I Ohannessian,&nbsp;Elisabeth Gassiat","doi":"10.1515/ijb-2017-0035","DOIUrl":"https://doi.org/10.1515/ijb-2017-0035","url":null,"abstract":"<p><p>We propose a truncation model for the abundance distribution in species richness estimation. This model is inherently semiparametric and incorporates an unknown truncation threshold between rare and abundant observations. Using the conditional likelihood, we derive a class of estimators for the parameters in this model by stepwise maximization. The species richness estimator is given by the integer maximizing the binomial likelihood, given all other parameters in the model. Under regularity conditions, we show that our estimators of the model parameters are asymptotically efficient. We recover Chaos lower bound estimator of species richness when the parametric part of the model is single-component Poisson. Thus our class of estimators strictly generalized the latter. We illustrate the performance of the proposed method in a simulation study, and compare it favorably to other widely-used estimators. We also give an application to estimating the number of distinct vocabulary words in French playwright Molière's Tartuffe.</p>","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"15 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2018-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36343138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Class of Robust Two-Sample Wald-Type Tests.","authors":"Abhik Ghosh,&nbsp;Nirian Martin,&nbsp;Ayanendranath Basu,&nbsp;Leandro Pardo","doi":"10.1515/ijb-2017-0023","DOIUrl":"https://doi.org/10.1515/ijb-2017-0023","url":null,"abstract":"<p><p>Parametric hypothesis testing associated with two independent samples arises frequently in several applications in biology, medical sciences, epidemiology, reliability and many more. In this paper, we propose robust Wald-type tests for testing such two sample problems using the minimum density power divergence estimators of the underlying parameters. In particular, we consider the simple two-sample hypothesis concerning the full parametric homogeneity as well as the general two-sample (composite) hypotheses involving some nuisance parameters. The asymptotic and theoretical robustness properties of the proposed Wald-type tests have been developed for both the simple and general composite hypotheses. Some particular cases of testing against one-sided alternatives are discussed with specific attention to testing the effectiveness of a treatment in clinical trials. Performances of the proposed tests have also been illustrated numerically through appropriate real data examples.</p>","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"14 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2018-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36326058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Spatio-Temporal Model and Inference Tools for Longitudinal Count Data on Multicolor Cell Growth.","authors":"PuXue Qiao,&nbsp;Christina Mølck,&nbsp;Davide Ferrari,&nbsp;Frédéric Hollande","doi":"10.1515/ijb-2018-0008","DOIUrl":"https://doi.org/10.1515/ijb-2018-0008","url":null,"abstract":"<p><p>Multicolor cell spatio-temporal image data have become important to investigate organ development and regeneration, malignant growth or immune responses by tracking different cell types both in vivo and in vitro. Statistical modeling of image data from common longitudinal cell experiments poses significant challenges due to the presence of complex spatio-temporal interactions between different cell types and difficulties related to measurement of single cell trajectories. Current analysis methods focus mainly on univariate cases, often not considering the spatio-temporal effects affecting cell growth between different cell populations. In this paper, we propose a conditional spatial autoregressive model to describe multivariate count cell data on the lattice, and develop inference tools. The proposed methodology is computationally tractable and enables researchers to estimate a complete statistical model of multicolor cell growth. Our methodology is applied on real experimental data where we investigate how interactions between cancer cells and fibroblasts affect their growth, which are normally present in the tumor microenvironment. We also compare the performance of our methodology to the multivariate conditional autoregressive (MCAR) model in both simulations and real data applications.</p>","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"14 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2018-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2018-0008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36291337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fast and Robust Way to Estimate Overlap of Niches, and Draw Inference.","authors":"Judith H Parkinson,&nbsp;Raoul Kutil,&nbsp;Jonas Kuppler,&nbsp;Robert R Junker,&nbsp;Wolfgang Trutschnig,&nbsp;Arne C Bathke","doi":"10.1515/ijb-2017-0028","DOIUrl":"https://doi.org/10.1515/ijb-2017-0028","url":null,"abstract":"<p><p>The problem of quantifying the overlap of Hutchinsonian niches has received much attention lately, in particular in quantitative ecology, from where it also originates. However, the niche concept has the potential to also be useful in many other application areas, as for example in economics. We are presenting a fully nonparametric, robust solution to this problem, along with exact shortcut formulas based on rank-statistics, and with a rather intuitive probabilistic interpretation. Furthermore, by deriving the asymptotic sampling distribution of the estimators, we are proposing the first asymptotically valid inference method, providing confidence intervals for the niche overlap. The theoretical considerations are supplemented by simulation studies and a real data example.</p>","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"14 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2018-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36226332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unfolding the Genome: The Case Study of P. falciparum.","authors":"Nelle Varoquaux","doi":"10.1515/ijb-2017-0061","DOIUrl":"https://doi.org/10.1515/ijb-2017-0061","url":null,"abstract":"<p><p>The development of new ways to probe samples for the three-dimensional (3D) structure of DNA paves the way for in depth and systematic analyses of the genome architecture. 3C-like methods coupled with high-throughput sequencing can now assess physical interactions between pairs of loci in a genome-wide fashion, thus enabling the creation of genome-by-genome contact maps. The spreading of such protocols creates many new opportunities for methodological development: how can we infer 3D models from these contact maps? Can such models help us gain insights into biological processes? Several recent studies applied such protocols to P. falciparum (the deadliest of the five human malaria parasites), assessing its genome organization at different moments of its life cycle. With its small genomic size, fairly simple (yet changing) genomic organization during its lifecyle and strong correlation between chromatin folding and gene expression, this parasite is the ideal case study for applying and developing methods to infer 3D models and use them for downstream analysis. Here, I review a set of methods used to build and analyse three-dimensional models from contact maps data with a special highlight on P. falciparum's genome organization.</p>","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"15 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2018-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36202193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Generally Efficient Targeted Minimum Loss Based Estimator based on the Highly Adaptive Lasso.","authors":"Mark van der Laan","doi":"10.1515/ijb-2015-0097","DOIUrl":"https://doi.org/10.1515/ijb-2015-0097","url":null,"abstract":"<p><p>Suppose we observe n $n$ independent and identically distributed observations of a finite dimensional bounded random variable. This article is concerned with the construction of an efficient targeted minimum loss-based estimator (TMLE) of a pathwise differentiable target parameter of the data distribution based on a realistic statistical model. The only smoothness condition we will enforce on the statistical model is that the nuisance parameters of the data distribution that are needed to evaluate the canonical gradient of the pathwise derivative of the target parameter are multivariate real valued cadlag functions (right-continuous and left-hand limits, (G. Neuhaus. On weak convergence of stochastic processes with multidimensional time parameter. Ann Stat 1971;42:1285-1295.) and have a finite supremum and (sectional) variation norm. Each nuisance parameter is defined as a minimizer of the expectation of a loss function over over all functions it its parameter space. For each nuisance parameter, we propose a new minimum loss based estimator that minimizes the loss-specific empirical risk over the functions in its parameter space under the additional constraint that the variation norm of the function is bounded by a set constant. The constant is selected with cross-validation. We show such an MLE can be represented as the minimizer of the empirical risk over linear combinations of indicator basis functions under the constraint that the sum of the absolute value of the coefficients is bounded by the constant: i.e., the variation norm corresponds with this L1 $L_1$-norm of the vector of coefficients. We will refer to this estimator as the highly adaptive Lasso (HAL)-estimator. We prove that for all models the HAL-estimator converges to the true nuisance parameter value at a rate that is faster than n-1/4 $n^{-1/4}$ w.r.t. square-root of the loss-based dissimilarity. We also show that if this HAL-estimator is included in the library of an ensemble super-learner, then the super-learner will at minimal achieve the rate of convergence of the HAL, but, by previous results, it will actually be asymptotically equivalent with the oracle (i.e., in some sense best) estimator in the library. Subsequently, we establish that a one-step TMLE using such a super-learner as initial estimator for each of the nuisance parameters is asymptotically efficient at any data generating distribution in the model, under weak structural conditions on the target parameter mapping and model and a strong positivity assumption (e.g., the canonical gradient is uniformly bounded). We demonstrate our general theorem by constructing such a one-step TMLE of the average causal effect in a nonparametric model, and establishing that it is asymptotically efficient.</p>","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"13 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2015-0097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35443475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptation of Chain Event Graphs for use with Case-Control Studies in Epidemiology.","authors":"Claire Keeble,&nbsp;Peter Adam Thwaites,&nbsp;Stuart Barber,&nbsp;Graham Richard Law,&nbsp;Paul David Baxter","doi":"10.1515/ijb-2016-0073","DOIUrl":"https://doi.org/10.1515/ijb-2016-0073","url":null,"abstract":"<p><p>Case-control studies are used in epidemiology to try to uncover the causes of diseases, but are a retrospective study design known to suffer from non-participation and recall bias, which may explain their decreased popularity in recent years. Traditional analyses report usually only the odds ratio for given exposures and the binary disease status. Chain event graphs are a graphical representation of a statistical model derived from event trees which have been developed in artificial intelligence and statistics, and only recently introduced to the epidemiology literature. They are a modern Bayesian technique which enable prior knowledge to be incorporated into the data analysis using the agglomerative hierarchical clustering algorithm, used to form a suitable chain event graph. Additionally, they can account for missing data and be used to explore missingness mechanisms. Here we adapt the chain event graph framework to suit scenarios often encountered in case-control studies, to strengthen this study design which is time and financially efficient. We demonstrate eight adaptations to the graphs, which consist of two suitable for full case-control study analysis, four which can be used in interim analyses to explore biases, and two which aim to improve the ease and accuracy of analyses. The adaptations are illustrated with complete, reproducible, fully-interpreted examples, including the event tree and chain event graph. Chain event graphs are used here for the first time to summarise non-participation, data collection techniques, data reliability, and disease severity in case-control studies. We demonstrate how these features of a case-control study can be incorporated into the analysis to provide further insight, which can help to identify potential biases and lead to more accurate study results.</p>","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"13 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2017-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2016-0073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35557255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix.","authors":"Zongliang Hu,&nbsp;Kai Dong,&nbsp;Wenlin Dai,&nbsp;Tiejun Tong","doi":"10.1515/ijb-2017-0013","DOIUrl":"https://doi.org/10.1515/ijb-2017-0013","url":null,"abstract":"<p><p>The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.</p>","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"13 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2017-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35392381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}