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Fulton Wang, Cynthia Rudin, Tyler H Mccormick, John L Gore
In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery...
May 5, 2018: Biostatistics
Rachel C Nethery, Dale P Sandler, Shanshan Zhao, Lawrence S Engel, Richard K Kwok
With the threat of climate change looming, the public health community has an interest in identifying communities at the highest risk of devastation based not only on geographic features but also on social characteristics. Indices of community social vulnerability can be created by applying a spatial factor analysis to a set of relevant social variables measured for each community; however, current spatial factor analysis methodology is ill-equipped to handle spatially misaligned data. We introduce a joint spatial factor analysis model that can accommodate spatial data from two distinct partitions of a geographic space and identify a common set of latent factors underlying them...
April 5, 2018: Biostatistics
Édouard Chatignoux, Laurent Remontet, Jean Iwaz, Marc Colonna, Zoé Uhry
There is a growing interest in using health care (HC) data to produce epidemiological surveillance indicators such as incidence. Typically, in the field of cancer, incidence is provided by local cancer registries which, in many countries, do not cover the whole territory; using proxy measures from available nationwide HC databases would appear to be a suitable approach to fill this gap. However, in most cases, direct counts from these databases do not provide reliable measures of incidence. To obtain accurate incidence estimations and prediction intervals, these databases need to be calibrated using a registry-based gold standard measure of incidence...
March 29, 2018: Biostatistics
Haben Michael, Lu Tian, Musie Ghebremichael
The receiver operating characteristic (ROC) curve is a commonly used graphical summary of the discriminative capacity of a thresholded continuous scoring system for a binary outcome. Estimation and inference procedures for the ROC curve are well-studied in the cross-sectional setting. However, there is a paucity of research when both biomarker measurements and disease status are observed longitudinally. In a motivating example, we are interested in characterizing the value of longitudinally measured CD4 counts for predicting the presence or absence of a transient spike in HIV viral load, also time-dependent...
March 28, 2018: Biostatistics
Lauren J Beesley, Jeremy M G Taylor
Multistate cure models are multistate models in which transitions into one or more of the states cannot occur for a fraction of the population. In the study of cancer, multistate cure models can be used to identify factors related to the rate of cancer recurrence, the rate of death before and after recurrence, and the probability of being cured by initial treatment. However, the previous method for fitting multistate cure models requires substantial custom programming, making these valuable models less accessible to analysts...
March 23, 2018: Biostatistics
Matthew A Psioda, Joseph G Ibrahim
We consider the problem of Bayesian sample size determination for a clinical trial in the presence of historical data that inform the treatment effect. Our broadly applicable, simulation-based methodology provides a framework for calibrating the informativeness of a prior while simultaneously identifying the minimum sample size required for a new trial such that the overall design has appropriate power to detect a non-null treatment effect and reasonable type I error control. We develop a comprehensive strategy for eliciting null and alternative sampling prior distributions which are used to define Bayesian generalizations of the traditional notions of type I error control and power...
March 14, 2018: Biostatistics
Klemen Pavlic, Maja Pohar Perme
A common goal in the analysis of the long-term survival related to a specific disease is to estimate a measure that is comparable between populations with different general population mortality. When cause of death is unavailable or unreliable, as for example in cancer registry studies, relative survival methodology is used-in addition to the mortality data of the patients, we use the data on the mortality of the general population. In this article, we focus on the marginal relative survival measure that summarizes the information about the disease-specific hazard...
March 13, 2018: Biostatistics
Alix Zollinger, Anthony C Davison, Darlene R Goldstein
Independence of genes is commonly but incorrectly assumed in microarray data analysis; rather, genes are activated in co-regulated sets referred to as modules. In this article, we develop an automatic method to define modules common to multiple independent studies. We use an empirical Bayes procedure to estimate a sparse correlation matrix for all studies, identify modules by clustering, and develop an extreme-value-based method to detect so-called scattered genes, which do not belong to any module. The resulting algorithm is very fast and produces accurate modules in simulation studies...
April 1, 2018: Biostatistics
So Young Park, Ana-Maria Staicu, Luo Xiao, Ciprian M Crainiceanu
We propose simple inferential approaches for the fixed effects in complex functional mixed effects models. We estimate the fixed effects under the independence of functional residuals assumption and then bootstrap independent units (e.g. subjects) to conduct inference on the fixed effects parameters. Simulations show excellent coverage probability of the confidence intervals and size of tests for the fixed effects model parameters. Methods are motivated by and applied to the Baltimore Longitudinal Study of Aging, though they are applicable to other studies that collect correlated functional data...
April 1, 2018: Biostatistics
Sangin Lee, Faming Liang, Ling Cai, Guanghua Xiao
Gaussian graphical models have been widely used to construct gene regulatory networks from gene expression data. Most existing methods for Gaussian graphical models are designed to model homogeneous data, assuming a single Gaussian distribution. In practice, however, data may consist of gene expression studies with unknown confounding factors, such as study cohort, microarray platforms, experimental batches, which produce heterogeneous data, and hence lead to false positive edges or low detection power in resulting network, due to those unknown factors...
April 1, 2018: Biostatistics
Stephanie C Hicks, Kwame Okrah, Joseph N Paulson, John Quackenbush, Rafael A Irizarry, Héctor Corrada Bravo
Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation...
April 1, 2018: Biostatistics
Steffen Ventz, Matteo Cellamare, Giovanni Parmigiani, Lorenzo Trippa
Multi-arm clinical trials use a single control arm to evaluate multiple experimental treatments. In most cases this feature makes multi-arm studies considerably more efficient than two-arm studies. A bottleneck for implementation of a multi-arm trial is the requirement that all experimental treatments have to be available at the enrollment of the first patient. New drugs are rarely at the same stage of development. These limitations motivate our study of statistical methods for adding new experimental arms after a clinical trial has started enrolling patients...
April 1, 2018: Biostatistics
Alexander M Kaizer, Joseph S Koopmeiners, Brian P Hobbs
Bayesian hierarchical models produce shrinkage estimators that can be used as the basis for integrating supplementary data into the analysis of a primary data source. Established approaches should be considered limited, however, because posterior estimation either requires prespecification of a shrinkage weight for each source or relies on the data to inform a single parameter, which determines the extent of influence or shrinkage from all sources, risking considerable bias or minimal borrowing. We introduce multisource exchangeability models (MEMs), a general Bayesian approach for integrating multiple, potentially non-exchangeable, supplemental data sources into the analysis of a primary data source...
April 1, 2018: Biostatistics
Gabrielle Simoneau, Erica E M Moodie, Robert W Platt, Bibhas Chakraborty
A dynamic treatment regime (DTR) is a set of decision rules to be applied across multiple stages of treatments. The decisions are tailored to individuals, by inputting an individual's observed characteristics and outputting a treatment decision at each stage for that individual. Dynamic weighted ordinary least squares (dWOLS) is a theoretically robust and easily implementable method for estimating an optimal DTR. As many related DTR methods, the dWOLS treatment effects estimators can be non-regular when true treatment effects are zero or very small, which results in invalid Wald-type or standard bootstrap confidence intervals...
April 1, 2018: Biostatistics
Candida Geerdens, Elif Fidan Acar, Paul Janssen
This article proposes a modeling strategy to infer the impact of a covariate on the dependence structure of right-censored clustered event time data. The joint survival function of the event times is modeled using a conditional copula whose parameter depends on a cluster-level covariate in a functional way. We use a local likelihood approach to estimate the form of the copula parameter and outline a generalized likelihood ratio-type test strategy to formally test its constancy. A bootstrap procedure is employed to obtain an approximate $p$-value for the test...
April 1, 2018: Biostatistics
Oliver Y Chén, Ciprian Crainiceanu, Elizabeth L Ogburn, Brian S Caffo, Tor D Wager, Martin A Lindquist
Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation model (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic, little work has been done when the intermediate variable (mediator) is a high-dimensional vector...
April 1, 2018: Biostatistics
Keegan Korthauer, Sutirtha Chakraborty, Yuval Benjamini, Rafael A Irizarry
With recent advances in sequencing technology, it is now feasible to measure DNA methylation at tens of millions of sites across the entire genome. In most applications, biologists are interested in detecting differentially methylated regions, composed of multiple sites with differing methylation levels among populations. However, current computational approaches for detecting such regions do not provide accurate statistical inference. A major challenge in reporting uncertainty is that a genome-wide scan is involved in detecting these regions, which needs to be accounted for...
February 22, 2018: Biostatistics
Paul Blanche, Michael W Kattan, Thomas A Gerds
We show that the widely used concordance index for time to event outcome is not proper when interest is in predicting a $t$-year risk of an event, for example 10-year mortality. In the situation with a fixed prediction horizon, the concordance index can be higher for a misspecified model than for a correctly specified model. Impropriety happens because the concordance index assesses the order of the event times and not the order of the event status at the prediction horizon. The time-dependent area under the receiver operating characteristic curve does not have this problem and is proper in this context...
February 16, 2018: Biostatistics
Lan Wen, Graciela Muniz Terrera, Shaun R Seaman
No abstract text is available yet for this article.
February 15, 2018: Biostatistics
Nicholas P Jewell, Suzanne Dufault, Zoe Cutcher, Cameron P Simmons, Katherine L Anders
Intervention trials of vector control methods often require community level randomization with appropriate inferential methods. For many interventions, the possibility of confounding due to the effects of health-care seeking behavior on disease ascertainment remains a concern. The test-negative design, a variant of the case-control method, was introduced to mitigate this issue in the assessment of the efficacy of influenza vaccination (measured at an individual level) on influenza infection. Here, we introduce a cluster-randomized test-negative design that includes randomization of the intervention at a group level...
February 12, 2018: Biostatistics
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