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Statistics in Medicine

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https://www.readbyqxmd.com/read/29226352/tutorial-on-kernel-estimation-of-continuous-spatial-and-spatiotemporal-relative-risk
#1
Tilman M Davies, Jonathan C Marshall, Martin L Hazelton
Kernel smoothing is a highly flexible and popular approach for estimation of probability density and intensity functions of continuous spatial data. In this role, it also forms an integral part of estimation of functionals such as the density-ratio or "relative risk" surface. Originally developed with the epidemiological motivation of examining fluctuations in disease risk based on samples of cases and controls collected over a given geographical region, such functions have also been successfully used across a diverse range of disciplines where a relative comparison of spatial density functions has been of interest...
December 11, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29205454/joint-mixed-effects-models-for-causal-inference-with-longitudinal-data
#2
Michelle Shardell, Luigi Ferrucci
Causal inference with observational longitudinal data and time-varying exposures is complicated due to the potential for time-dependent confounding and unmeasured confounding. Most causal inference methods that handle time-dependent confounding rely on either the assumption of no unmeasured confounders or the availability of an unconfounded variable that is associated with the exposure (eg, an instrumental variable). Furthermore, when data are incomplete, validity of many methods often depends on the assumption of missing at random...
December 4, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29205452/an-r2-curve-for-evaluating-the-accuracy-of-dynamic-predictions
#3
Marie-Cécile Fournier, Etienne Dantan, Paul Blanche
In the context of chronic diseases, patient's health evolution is often evaluated through the study of longitudinal markers and major clinical events such as relapses or death. Dynamic predictions of such types of events may be useful to improve patients management all along their follow-up. Dynamic predictions consist of predictions that are based on information repeatedly collected over time, such as measurements of a biomarker, and that can be updated as soon as new information becomes available. Several techniques to derive dynamic predictions have already been suggested, and computation of dynamic predictions is becoming increasingly popular...
December 4, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29205447/multivariate-space-time-modelling-of-multiple-air-pollutants-and-their-health-effects-accounting-for-exposure-uncertainty
#4
Guowen Huang, Duncan Lee, E Marian Scott
The long-term health effects of air pollution are often estimated using a spatio-temporal ecological areal unit study, but this design leads to the following statistical challenges: (1) how to estimate spatially representative pollution concentrations for each areal unit; (2) how to allow for the uncertainty in these estimated concentrations when estimating their health effects; and (3) how to simultaneously estimate the joint effects of multiple correlated pollutants. This article proposes a novel 2-stage Bayesian hierarchical model for addressing these 3 challenges, with inference based on Markov chain Monte Carlo simulation...
December 4, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29205446/induced-smoothing-for-rank-based-regression-with-recurrent-gap-time-data
#5
Tianmeng Lyu, Xianghua Luo, Gongjun Xu, Chiung-Yu Huang
Various semiparametric regression models have recently been proposed for the analysis of gap times between consecutive recurrent events. Among them, the semiparametric accelerated failure time (AFT) model is especially appealing owing to its direct interpretation of covariate effects on the gap times. In general, estimation of the semiparametric AFT model is challenging because the rank-based estimating function is a nonsmooth step function. As a result, solutions to the estimating equations do not necessarily exist...
December 4, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29205445/analysis-of-the-u-s-patient-referral-network
#6
Chuankai An, A James O'Malley, Daniel N Rockmore, Corey D Stock
In this paper, we analyze the US Patient Referral Network (also called the Shared Patient Network) and various subnetworks for the years 2009 to 2015. In these networks, two physicians are linked if a patient encounters both of them within a specified time interval, according to the data made available by the Centers for Medicare and Medicaid Services. We find power law distributions on most state-level data as well as a core-periphery structure. On a national and state level, we discover a so-called small-world structure as well as a "gravity law" of the type found in some large-scale economic networks...
December 4, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29205435/relative-efficiency-of-precision-medicine-designs-for-clinical-trials-with-predictive-biomarkers
#7
Weichung Joe Shih, Yong Lin
Prospective randomized clinical trials addressing biomarkers are time consuming and costly, but are necessary for regulatory agencies to approve new therapies with predictive biomarkers. For this reason, recently, there have been many discussions and proposals of various trial designs and comparisons of their efficiency in the literature. We compare statistical efficiencies between the marker-stratified design and the marker-based precision medicine design regarding testing/estimating 4 hypotheses/parameters of clinical interest, namely, treatment effects in each marker-positive and marker-negative cohorts, marker-by-treatment interaction, and the marker's clinical utility...
December 4, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29205434/bayesian-inference-for-unidirectional-misclassification-of-a-binary-response-trait
#8
Michelle Xia, Paul Gustafson
When assessing association between a binary trait and some covariates, the binary response may be subject to unidirectional misclassification. Unidirectional misclassification can occur when revealing a particular level of the trait is associated with a type of cost, such as a social desirability or financial cost. The feasibility of addressing misclassification is commonly obscured by model identification issues. The current paper attempts to study the efficacy of inference when the binary response variable is subject to unidirectional misclassification...
December 4, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29205425/a-weighted-combined-effect-measure-for-the-analysis-of-a-composite-time-to-first-event-endpoint-with-components-of-different-clinical-relevance
#9
Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg
Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect...
December 4, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29205414/a-joint-marginal-conditional-model-for-multivariate-longitudinal-data
#10
James Proudfoot, Walter Faig, Loki Natarajan, Ronghui Xu
Multivariate longitudinal data frequently arise in biomedical applications; however, their analyses are often performed one outcome at a time, or jointly using existing software in an ad hoc fashion. A main challenge in the proper analysis of such data is the fact that the different outcomes are measured on different unknown scales. Methodology for handling the scale problem has been previously proposed for cross-sectional data, and here we extend it to the longitudinal setting. We consider modeling the longitudinal data using random effects, while leaving the joint distribution of the multiple outcomes unspecified...
December 4, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29205409/inverse-probability-weighting-to-control-confounding-in-an-illness-death-model-for-interval-censored-data
#11
Florence Gillaizeau, Thomas Sénage, Florent Le Borgne, Thierry Le Tourneau, Jean-Christian Roussel, Karen Leffondrè, Raphaël Porcher, Bruno Giraudeau, Etienne Dantan, Yohann Foucher
Multistate models with interval-censored data, such as the illness-death model, are still not used to any considerable extent in medical research regardless of the significant literature demonstrating their advantages compared to usual survival models. Possible explanations are their uncommon availability in classical statistical software or, when they are available, by the limitations related to multivariable modelling to take confounding into consideration. In this paper, we propose a strategy based on propensity scores that allows population causal effects to be estimated: the inverse probability weighting in the illness semi-Markov model with interval-censored data...
December 4, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29193212/meta-analysis-for-the-comparison-of-two-diagnostic-tests-a-new-approach-based-on-copulas
#12
Annika Hoyer, Oliver Kuss
Meta-analysis of diagnostic studies is still field of ongoing biometrical research. Especially, clinical researchers call for methods that allow for a comparison of different diagnostic tests to a common gold standard. Focussing on two diagnostic tests, the main parameters of interest are differences of sensitivities and specificities (with their corresponding confidence intervals) between the two diagnostic tests while accounting for the various associations across the two tests and the single studies. Similar to our previous work using generalized linear mixed models to this task, we propose a model with a quadrivariate response consisting of the two sensitivities and the two specificities of both tests...
November 29, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29193180/considerations-for-analysis-of-time-to-event-outcomes-measured-with-error-bias-and-correction-with-simex
#13
Eric J Oh, Bryan E Shepherd, Thomas Lumley, Pamela A Shaw
For time-to-event outcomes, a rich literature exists on the bias introduced by covariate measurement error in regression models, such as the Cox model, and methods of analysis to address this bias. By comparison, less attention has been given to understanding the impact or addressing errors in the failure time outcome. For many diseases, the timing of an event of interest (such as progression-free survival or time to AIDS progression) can be difficult to assess or reliant on self-report and therefore prone to measurement error...
November 29, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29193194/impact-of-individual-behaviour-change-on-the-spread-of-emerging-infectious-diseases
#14
Q L Yan, S Y Tang, Y N Xiao
Human behaviour plays an important role in the spread of emerging infectious diseases, and understanding the influence of behaviour changes on epidemics can be key to improving control efforts. However, how the dynamics of individual behaviour changes affects the development of emerging infectious disease is a key public health issue. To develop different formula for individual behaviour change and introduce how to embed it into a dynamic model of infectious diseases, we choose A/H1N1 and Ebola as typical examples, combined with the epidemic reported cases and media related news reports...
November 28, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29193179/maximum-likelihood-estimation-of-influenza-vaccine-effectiveness-against-transmission-from-the-household-and-from-the-community
#15
Kylie E C Ainslie, Michael J Haber, Ryan E Malosh, Joshua G Petrie, Arnold S Monto
Influenza vaccination is recommended as the best way to protect against influenza infection and illness. Due to seasonal changes in influenza virus types and subtypes, a new vaccine must be produced, and vaccine effectiveness (VE) must be estimated, annually. Since 2010, influenza vaccination has been recommended universally in the United States, making randomized clinical trials unethical. Recent studies have used a monitored household cohort study design to determine separate VE estimates against influenza transmission from the household and community...
November 28, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29181854/assessing-the-similarity-of-dose-response-and-target-doses-in-2-non-overlapping-subgroups
#16
Frank Bretz, Kathrin Möllenhoff, Holger Dette, Wei Liu, Matthias Trampisch
We consider 2 problems of increasing importance in clinical dose finding studies. First, we assess the similarity of 2 non-linear regression models for 2 non-overlapping subgroups of patients over a restricted covariate space. To this end, we derive a confidence interval for the maximum difference between the 2 given models. If this confidence interval excludes the pre-specified equivalence margin, similarity of dose response can be claimed. Second, we address the problem of demonstrating the similarity of 2 target doses for 2 non-overlapping subgroups, using again an approach based on a confidence interval...
November 27, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29171062/mixture-drug-count-response-model-for-the-high-dimensional-drug-combinatory-effect-on-myopathy
#17
Xueying Wang, Pengyue Zhang, Chien-Wei Chiang, Hengyi Wu, Li Shen, Xia Ning, Donglin Zeng, Lei Wang, Sara K Quinney, Weixing Feng, Lang Li
Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy...
November 23, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29171035/semiparametric-regression-analysis-for-alternating-recurrent-event-data
#18
Chi Hyun Lee, Chiung-Yu Huang, Gongjun Xu, Xianghua Luo
Alternating recurrent event data arise frequently in clinical and epidemiologic studies, where 2 types of events such as hospital admission and discharge occur alternately over time. The 2 alternating states defined by these recurrent events could each carry important and distinct information about a patient's underlying health condition and/or the quality of care. In this paper, we propose a semiparametric method for evaluating covariate effects on the 2 alternating states jointly. The proposed methodology accounts for the dependence among the alternating states as well as the heterogeneity across patients via a frailty with unspecified distribution...
November 23, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29164648/a-state-transition-framework-for-patient-level-modeling-of-engagement-and-retention-in-hiv-care-using-longitudinal-cohort-data
#19
Hana Lee, Joseph W Hogan, Becky L Genberg, Xiaotian K Wu, Beverly S Musick, Ann Mwangi, Paula Braitstein
The human immunodeficiency virus (HIV) care cascade is a conceptual model used to outline the benchmarks that reflects effectiveness of HIV care in the whole HIV care continuum. The models can be used to identify barriers contributing to poor outcomes along each benchmark in the cascade such as disengagement from care or death. Recently, the HIV care cascade has been widely applied to monitor progress towards HIV prevention and care goals in an attempt to develop strategies to improve health outcomes along the care continuum...
November 22, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29164654/exponential-decay-for-binary-time-varying-covariates-in-cox-models
#20
Charles Donald George Keown-Stoneman, Julie Horrocks, Gerarda Darlington
Cox models are commonly used in the analysis of time to event data. One advantage of Cox models is the ability to include time-varying covariates, often a binary covariate that codes for the occurrence of an event that affects an individual subject. A common assumption in this case is that the effect of the event on the outcome of interest is constant and permanent for each subject. In this paper, we propose a modification to the Cox model to allow the influence of an event to exponentially decay over time...
November 21, 2017: Statistics in Medicine
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