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Statistics in Medicine

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https://www.readbyqxmd.com/read/28215054/semiparametric-modeling-and-analysis-of-longitudinal-method-comparison-data
#1
Lasitha N Rathnayake, Pankaj K Choudhary
Studies comparing two or more methods of measuring a continuous variable are routinely conducted in biomedical disciplines with the primary goal of measuring agreement between the methods. Often, the data are collected by following a cohort of subjects over a period of time. This gives rise to longitudinal method comparison data where there is one observation trajectory for each method on every subject. It is not required that observations from all methods be available at each observation time. The multiple trajectories on the same subjects are dependent...
February 19, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28215052/predicting-pregnancy-outcomes-using-longitudinal-information-a-penalized-splines-mixed-effects-model-approach
#2
Rolando De la Cruz, Claudio Fuentes, Cristian Meza, Dae-Jin Lee, Ana Arribas-Gil
We propose a semiparametric nonlinear mixed-effects model (SNMM) using penalized splines to classify longitudinal data and improve the prediction of a binary outcome. The work is motivated by a study in which different hormone levels were measured during the early stages of pregnancy, and the challenge is using this information to predict normal versus abnormal pregnancy outcomes. The aim of this paper is to compare models and estimation strategies on the basis of alternative formulations of SNMMs depending on the characteristics of the data set under consideration...
February 19, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28215045/a-generalized-partially-linear-mean-covariance-regression-model-for-longitudinal-proportional-data-with-applications-to-the-analysis-of-quality-of-life-data-from-cancer-clinical-trials
#3
Xueying Zheng, Guoyou Qin, Dongsheng Tu
Motivated by the analysis of quality of life data from a clinical trial on early breast cancer, we propose in this paper a generalized partially linear mean-covariance regression model for longitudinal proportional data, which are bounded in a closed interval. Cholesky decomposition of the covariance matrix for within-subject responses and generalized estimation equations are used to estimate unknown parameters and the nonlinear function in the model. Simulation studies are performed to evaluate the performance of the proposed estimation procedures...
February 19, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28211086/variation-of-the-rates-of-necrotising-enterocolitis-in-the-neonatal-networks-in-england
#4
Nicholas T Longford
Necrotising enterocolitis is an oft-fatal disease of the intestinal tract in neonates born prematurely and with low birthweight. We study the variation of its rates across the neonatal networks (groups of hospital-based neonatal care units) in England. We illustrate the problematic nature of hypothesis testing for a key decision, which an analysis is meant to inform, and apply an approach based on decision theory. We emphasise the role of sensitivity analysis in dealing with the ambiguity encountered in the process of eliciting information about the perspective of the client or sponsor for whom the analysis is conducted...
February 17, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28211085/variable-selection-in-the-functional-linear-concurrent-model
#5
Jeff Goldsmith, Joseph E Schwartz
We propose methods for variable selection in the context of modeling the association between a functional response and concurrently observed functional predictors. This data structure, and the need for such methods, is exemplified by our motivating example: a study in which blood pressure values are observed throughout the day, together with measurements of physical activity, location, posture, affect or mood, and other quantities that may influence blood pressure. We estimate the coefficients of the concurrent functional linear model using variational Bayes and jointly model residual correlation using functional principal components analysis...
February 17, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28211076/comparison-of-conditional-bias-adjusted-estimators-for-interim-analysis-in-clinical-trials-with-survival-data
#6
Masashi Shimura, Masahiko Gosho, Akihiro Hirakawa
Group sequential designs are widely used in clinical trials to determine whether a trial should be terminated early. In such trials, maximum likelihood estimates are often used to describe the difference in efficacy between the experimental and reference treatments; however, these are well known for displaying conditional and unconditional biases. Established bias-adjusted estimators include the conditional mean-adjusted estimator (CMAE), conditional median unbiased estimator, conditional uniformly minimum variance unbiased estimator (CUMVUE), and weighted estimator...
February 17, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28211087/semiparametric-pseudoscore-for-regression-with-multidimensional-but-incompletely-observed-regressor
#7
Zonghui Hu, Jing Qin, Dean Follmann
We study the regression fβ (Y|X,Z), where Y is the response, Z∈Rd is a vector of fully observed regressors and X is the regressor with incomplete observation. To handle missing data, maximum likelihood estimation via expectation-maximisation (EM) is the most efficient but is sensitive to the specification of the distribution of X. Under a missing at random assumption, we propose an EM-type estimation via a semiparametric pseudoscore. Like in EM, we derive the conditional expectation of the score function given Y and Z, or the mean score, over the incompletely observed units under a postulated distribution of X...
February 16, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28208229/comparing-the-performance-of-propensity-score-methods-in-healthcare-database-studies-with-rare-outcomes
#8
Jessica M Franklin, Wesley Eddings, Peter C Austin, Elizabeth A Stuart, Sebastian Schneeweiss
Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of events observed in a small number of patients. In addition, studies constructed from electronic healthcare databases, for example, administrative claims data, often adjust for many, possibly hundreds, of potential confounders. Despite the importance of maximizing efficiency when there are many confounders and few observed outcome events, there has been relatively little research on the relative performance of different propensity score methods in this context...
February 16, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28192863/semiparametric-profile-likelihood-estimation-for-continuous-outcomes-with-excess-zeros-in-a-random-threshold-damage-resistance-model
#9
John D Rice, Alex Tsodikov
Continuous outcome data with a proportion of observations equal to zero (often referred to as semicontinuous data) arise frequently in biomedical studies. Typical approaches involve two-part models, with one part a logistic model for the probability of observing a zero and some parametric continuous distribution for modeling the positive part of the data. We propose a semiparametric model based on a biological system with competing damage manifestation and resistance processes. This allows us to derive a closed-form profile likelihood based on the retro-hazard function, leading to a flexible procedure for modeling continuous data with a point mass at zero...
February 13, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28192861/binocular-sensitivity-and-specificity-of-screening-tests-in-cross-sectional-diagnostic-studies-of-paired-organs
#10
Yamuni Perera, Mingchen Ren, Joyce Raymond B Punzalan, Christopher J Rudnisky, Alexander R de Leon
We introduce new binocular accuracy measures as alternatives to conventional marginal measures that can be used to evaluate screening tests in diagnostic studies involving paired organs (e.g. eyes and ears). Specifically, we consider screening studies based on a cross-sectional design, where both diagnosis and disease status are determined after study enrolment or sampling, yielding paired binocular binary data described via two models, namely, the extended common correlation model and the Gaussian copula probit model...
February 13, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28192859/effects-of-contact-network-structure-on-epidemic-transmission-trees-implications-for-data-required-to-estimate-network-structure
#11
Nicole Bohme Carnegie
Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network features from data collected in an epidemic. The approach taken in this paper is to examine the distributions of epidemic outcomes arising from epidemics on networks with particular structural features to assess whether that structure could be measured from epidemic data and what other constraints might be needed to make the problem identifiable...
February 13, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28183157/improve-efficiency-and-reduce-bias-of-cox-regression-models-for-two-stage-randomization-designs-using-auxiliary-covariates
#12
Xue Yang, Yong Zhou
Two-stage randomization designs are broadly accepted and becoming increasingly popular in clinical trials for cancer and other chronic diseases to assess and compare the effects of different treatment policies. In this paper, we propose an inferential method to estimate the treatment effects in two-stage randomization designs, which can improve the efficiency and reduce bias in the presence of chance imbalance of a robust covariate-adjustment without additional assumptions required by Lokhnygina and Helterbrand (Biometrics, 63:422-428)'s inverse probability weighting (IPW) method...
February 9, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28183155/quantifying-the-bias-in-the-estimated-treatment-effect-in-randomized-trials-having-interim-analyses-and-a-rule-for-early-stopping-for-futility
#13
S D Walter, H Han, M Briel, G H Guyatt
In this paper, we consider the potential bias in the estimated treatment effect obtained from clinical trials, the protocols of which include the possibility of interim analyses and an early termination of the study for reasons of futility. In particular, by considering the conditional power at an interim analysis, we derive analytic expressions for various parameters of interest: (i) the underestimation or overestimation of the treatment effect in studies that stop for futility; (ii) the impact of the interim analyses on the estimation of treatment effect in studies that are completed, i...
February 9, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28183151/power-and-sample-size-calculation-for-paired-recurrent-events-data-based-on-robust-nonparametric-tests
#14
Pei-Fang Su, Chia-Hua Chung, Yu-Wen Wang, Yunchan Chi, Ying-Ju Chang
The purpose of this paper is to develop a formula for calculating the required sample size for paired recurrent events data. The developed formula is based on robust non-parametric tests for comparing the marginal mean function of events between paired samples. This calculation can accommodate the associations among a sequence of paired recurrent event times with a specification of correlated gamma frailty variables for a proportional intensity model. We evaluate the performance of the proposed method with comprehensive simulations including the impacts of paired correlations, homogeneous or nonhomogeneous processes, marginal hazard rates, censoring rate, accrual and follow-up times, as well as the sensitivity analysis for the assumption of the frailty distribution...
February 9, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28173610/comparison-of-hypertabastic-survival-model-with-other-unimodal-hazard-rate-functions-using-a-goodness-of-fit-test
#15
M Ramzan Tahir, Quang X Tran, Mikhail S Nikulin
We studied the problem of testing a hypothesized distribution in survival regression models when the data is right censored and survival times are influenced by covariates. A modified chi-squared type test, known as Nikulin-Rao-Robson statistic, is applied for the comparison of accelerated failure time models. This statistic is used to test the goodness-of-fit for hypertabastic survival model and four other unimodal hazard rate functions. The results of simulation study showed that the hypertabastic distribution can be used as an alternative to log-logistic and log-normal distribution...
February 7, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28168731/statistical-methodology-for-estimating-the-mean-difference-in-a-meta-analysis-without-study-specific-variance-information
#16
Patarawan Sangnawakij, Dankmar Böhning, Stephen Adams, Michael Stanton, Heinz Holling
Statistical inference for analyzing the results from several independent studies on the same quantity of interest has been investigated frequently in recent decades. Typically, any meta-analytic inference requires that the quantity of interest is available from each study together with an estimate of its variability. The current work is motivated by a meta-analysis on comparing two treatments (thoracoscopic and open) of congenital lung malformations in young children. Quantities of interest include continuous end-points such as length of operation or number of chest tube days...
February 6, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28152571/bayesian-hierarchical-modeling-of-longitudinal-glaucomatous-visual-fields-using-a-two-stage-approach
#17
Susan R Bryan, Paul H C Eilers, Joost van Rosmalen, Dimitris Rizopoulos, Koenraad A Vermeer, Hans G Lemij, Emmanuel M E H Lesaffre
The Bayesian approach has become increasingly popular because it allows to fit quite complex models to data via Markov chain Monte Carlo sampling. However, it is also recognized nowadays that Markov chain Monte Carlo sampling can become computationally prohibitive when applied to a large data set. We encountered serious computational difficulties when fitting an hierarchical model to longitudinal glaucoma data of patients who participate in an ongoing Dutch study. To overcome this problem, we applied and extended a recently proposed two-stage approach to model these data...
February 2, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28147447/patient-subgroup-identification-for-clinical-drug-development
#18
Xin Huang, Yan Sun, Paul Trow, Saptarshi Chatterjee, Arunava Chakravartty, Lu Tian, Viswanath Devanarayan
Causal mechanism of relationship between the clinical outcome (efficacy or safety endpoints) and putative biomarkers, clinical baseline, and related predictors is usually unknown and must be deduced empirically from experimental data. Such relationships enable the development of tailored therapeutics and implementation of a precision medicine strategy in clinical trials to help stratify patients in terms of disease progression, clinical response, treatment differentiation, and so on. These relationships often require complex modeling to develop the prognostic and predictive signatures...
February 1, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28147439/identification-of-treatment-responders-based-on-multiple-longitudinal-outcomes-with-applications-to-multiple-sclerosis-patients
#19
Yumi Kondo, Yinshan Zhao, John Petkau
Identification of treatment responders is a challenge in comparative studies where treatment efficacy is measured by multiple longitudinally collected continuous and count outcomes. Existing procedures often identify responders on the basis of only a single outcome. We propose a novel multiple longitudinal outcome mixture model that assumes that, conditionally on a cluster label, each longitudinal outcome is from a generalized linear mixed effect model. We utilize a Monte Carlo expectation-maximization algorithm to obtain the maximum likelihood estimates of our high-dimensional model and classify patients according to their estimated posterior probability of being a responder...
February 1, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28132437/graph-based-optimization-of-epitope-coverage-for-vaccine-antigen-design
#20
James Theiler, Bette Korber
Epigraph is a recently developed algorithm that enables the computationally efficient design of single or multi-antigen vaccines to maximize the potential epitope coverage for a diverse pathogen population. Potential epitopes are defined as short contiguous stretches of proteins, comparable in length to T-cell epitopes. This optimal coverage problem can be formulated in terms of a directed graph, with candidate antigens represented as paths that traverse this graph. Epigraph protein sequences can also be used as the basis for designing peptides for experimental evaluation of immune responses in natural infections to highly variable proteins...
January 29, 2017: Statistics in Medicine
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