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Journal of Applied Statistics

Hsiu-Wen Chen, Weng Kee Wong, Hongquan Xu
Multiple outcomes are increasingly used to assess chronic disease progression. We discuss and show how desirability functions can be used to assess a patient overall response to a treatment using multiple outcome measures and each of them may contribute unequally to the final assessment. Because judgments on disease progression and the relative contribution of each outcome can be subjective, we propose a data-driven approach to minimize the biases by using desirability functions with estimated shapes and weights based on a given gold standard...
April 1, 2016: Journal of Applied Statistics
Miran A Jaffa, Mulugeta Gebregziabher, Deirdre K Luttrell, Louis M Luttrell, Ayad A Jaffa
Statistical approaches tailored to analyzing longitudinal data that have multiple outcomes with different distributions are scarce. This paucity is due to the non-availability of multivariate distributions that jointly model outcomes with different distributions other than the multivariate normal. A plethora of research has been done on the specific combination of binary-Gaussian bivariate outcomes but a more general approach that allows other mixtures of distributions for multiple longitudinal outcomes has not been thoroughly demonstrated and examined...
2016: Journal of Applied Statistics
Folefac D Atem, Jing Qian, Jacqueline E Maye, Keith A Johnson, Rebecca A Betensky
Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model...
2016: Journal of Applied Statistics
Dongbing Lai, Huiping Xu, Daniel Koller, Tatiana Foroud, Sujuan Gao
Dementia patients exhibit considerable heterogeneity in individual trajectories of cognitive decline, with some patients showing rapid decline following diagnoses while others exhibiting slower decline or remaining stable for several years. Dementia studies often collect longitudinal measures of multiple neuropsychological tests aimed to measure patients' decline across a number of cognitive domains. We propose a multivariate finite mixture latent trajectory model to identify distinct longitudinal patterns of cognitive decline simultaneously in multiple cognitive domains, each of which is measured by multiple neuropsychological tests...
2016: Journal of Applied Statistics
Joshua N Sampson, Charles E Matthews, Laurence Freedman, Raymond J Carroll, Victor Kipnis
Sedentary behavior has already been associated with mortality, cardiovascular disease, and cancer. Questionnaires are an affordable tool for measuring sedentary behavior in large epidemiological studies. Here, we introduce and evaluate two statistical methods for quantifying measurement error in questionnaires. Accurate estimates are needed for assessing questionnaire quality. The two methods would be applied to validation studies that measure a sedentary behavior by both questionnaire and accelerometer on multiple days...
2016: Journal of Applied Statistics
H He, W J Wang, J Hu, R Gallop, P Crits-Christoph, Y L Xia
Count reponses with structural zeros are very common in medical and psychosocial research, especially in alcohol and HIV research, and the zero-inflated poisson (ZIP) and zero-inflated negative binomial (ZINB) models are widely used for modeling such outcomes. However, as alcohol drinking outcomes such as days of drinkings are counts within a given period, their distributions are bounded above by an upper limit (total days in the period) and thus inherently follow a binomial or zero-inflated binomial (ZIB) distribution, rather than a Poisson or zero-inflated Poisson (ZIP) distribution, in the presence of structural zeros...
October 1, 2015: Journal of Applied Statistics
Alan D Hutson, Gregory E Wilding, Terry L Mashtare, Albert Vexler
In this note we develop a new multivariate copula model based on epsilon-skew-normal marginal densities for the purpose of examining biomarker dependency structures. We illustrate the flexibility and utility of this model via a variety of graphical tools and a data analysis example pertaining to salivary biomarker. The multivariate normal model is a sub-model of the multivariate epsilon-skew-normal distribution.
2015: Journal of Applied Statistics
Sheng Luo, Xiao Su, Min Yi, Kelly K Hunt
Ipsilateral breast tumor relapse (IBTR) often occurs in breast cancer patients after their breast conservation therapy. The IBTR status' classification (true local recurrence versus new ipsilateral primary tumor) is subject to error and there is no widely-accepted gold standard. Time to IBTR is likely informative for IBTR classification because new primary tumor tends to have a longer mean time to IBTR and is associated with improved survival as compared with the true local recurrence tumor. Moreover, some patients may die from breast cancer or other causes in a competing risk scenario during the follow-up period...
2015: Journal of Applied Statistics
Nilesh H Shah, Alison E Hipwell, Stephanie D Stepp, Chung-Chou H Chang
In clinical research, patient care decisions are often easier to make if patients are classified into a manageable number of groups based on homogeneous risk patterns. Investigators can use latent group-based trajectory modeling to estimate the posterior probabilities that an individual will be classified into a particular group of risk patterns. Although this method is increasingly used in clinical research, there is currently no measure that can be used to determine whether an individual's group assignment has a high level of discrimination...
January 2015: Journal of Applied Statistics
Peter Malave, Arkadiusz Sitek
Kinetic models are used extensively in science, engineering, and medicine. Mathematically, they are a set of coupled differential equations including a source function, otherwise known as an input function. We investigate whether parametric modeling of a noisy input function offers any benefit over the non-parametric input function in estimating kinetic parameters. Our analysis includes four formulations of Bayesian posteriors of model parameters where noise is taken into account in the likelihood functions...
2015: Journal of Applied Statistics
Jelani Wiltshire, Fred W Huffer, William C Parker
Van Valen's Red Queen hypothesis states that within a homogeneous taxonomic group the age is statistically independent of the rate of extinction. The case of the Red Queen hypothesis being addressed here is when the homogeneous taxonomic group is a group of similar species. Since Van Valen's work, various statistical approaches have been used to address the relationship between taxon age and the rate of extinction. We propose a general class of test statistics that can be used to test for the effect of age on the rate of extinction...
September 1, 2014: Journal of Applied Statistics
Michael R Crager, Gong Tang
We propose a method for assessing an individual patient's risk of a future clinical event using clinical trial or cohort data and Cox proportional hazards regression, combining the information from several studies using meta-analysis techniques. The method combines patient-specific estimates of the log cumulative hazard across studies, weighting by the relative precision of the estimates, using either fixed- or random-effects meta-analysis calculations. Risk assessment can be done for any future patient using a few key summary statistics determined once and for all from each study...
2014: Journal of Applied Statistics
Riten Mitra, Peter Müller, Yuan Ji, Yitan Zhu, Gordon Mills, Yiling Lu
We consider inference for functional proteomics experiments that record protein activation over time following perturbation under different dose levels of several drugs. The main inference goal is the dependence structure of the selected proteins. A critical challenge is the lack of sufficient data under any one drug and dose level to allow meaningful inference on dependence structure. We propose a hierarchical model to implement the desired inference. The key element of the model is a shared dependence structure on (latent) binary indicators of protein activation...
2014: Journal of Applied Statistics
Sean L Simpson, Lloyd J Edwards, Martin A Styner, Keith E Muller
Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Valid inference in longitudinal imaging requires enough flexibility of the covariance model to allow reasonable fidelity to the true pattern. On the other hand, the existence of computable estimates demands a parsimonious parameterization of the covariance structure. Separable (Kronecker product) covariance models provide one such parameterization in which the spatial and temporal covariances are modeled separately...
2014: Journal of Applied Statistics
Jiin Choi, Stewart J Anderson, Thomas J Richards, Wesley K Thompson
We implement a joint model for mixed multivariate longitudinal measurements, applied to the prediction of time until lung transplant or death in idiopathic pulmonary fibrosis. Specifically, we formulate a unified Bayesian joint model for the mixed longitudinal responses and time-to-event outcomes. For the longitudinal model of continuous and binary responses, we investigate multivariate generalized linear mixed models using shared random effects. Longitudinal and time-to-event data are assumed to be independent conditional on available covariates and shared parameters...
January 1, 2014: Journal of Applied Statistics
Le Chen, Ao Yuan, Aiyi Liu, Guanjie Chen
The mixed random effect model is commonly used in longitudinal data analysis within either frequentist or Bayesian framework. Here we consider a case, we have prior knowledge on partial-parameters, while no such information on rest. Thus, we use the hybrid approach on the random-effects model with partial-parameters. The parameters are estimated via Bayesian procedure, and the rest of parameters by the frequentist maximum likelihood estimation (MLE), simultaneously on the same model. In practices, we often know partial prior information such as, covariates of age, gender, and etc...
2014: Journal of Applied Statistics
Melody S Goodman, Yi Li, Anne M Stoddard, Glorian Sorensen
We propose a mixture model for data with an ordinal outcome and a longitudinal covariate that is subject to missingness. Data from a tailored telephone delivered, smoking cessation intervention for construction laborers are used to illustrate the method, which considers as an outcome a categorical measure of smoking cessation, and evaluates the effectiveness of the motivational telephone interviews on this outcome. We propose two model structures for the longitudinal covariate, for the case when the missing data are missing at random, and when the missing data mechanism is non-ignorable...
January 1, 2014: Journal of Applied Statistics
Stephanie Sapp, Mark J van der Laan, John Canny
Ensemble methods using the same underlying algorithm trained on different subsets of observations have recently received increased attention as practical prediction tools for massive datasets. We propose Subsemble: a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a clever form of V-fold cross-validation to output a prediction function that combines the subset-specific fits...
January 1, 2014: Journal of Applied Statistics
Ming Zhou, Yongzhao Shao
This paper investigates a new test for normality that is easy for biomedical researchers to understand and easy to implement in all dimensions. In terms of power comparison against a broad range of alternatives, the new test outperforms the best known competitors in the literature as demonstrated by simulation results. In addition, the proposed test is illustrated using data from real biomedical studies.
January 1, 2014: Journal of Applied Statistics
Sung Wan Han, Rickson C Mesquita, Theresa M Busch, Mary E Putt
In a smoothing spline model with unknown change-points, the choice of the smoothing parameter strongly influences the estimation of the change-point locations, and the function at the change-points. In a tumor biology example, where change-points in blood flow in response to treatment were of interest, choosing the smoothing parameter based on minimizing generalized cross validation, GCV, gave unsatisfactory estimates of the change-points. We propose a new method, aGCV, that re-weights the residual sum of squares and generalized degrees of freedom terms from GCV...
2014: Journal of Applied Statistics
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