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https://www.readbyqxmd.com/read/28936916/bayesian-quantile-regression-based-partially-linear-mixed-effects-joint-models-for-longitudinal-data-with-multiple-features
#1
Hanze Zhang, Yangxin Huang, Wei Wang, Henian Chen, Barbara Langland-Orban
In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load...
January 1, 2017: Statistical Methods in Medical Research
https://www.readbyqxmd.com/read/28872693/modeling-continuous-response-variables-using-ordinal-regression
#2
Qi Liu, Bryan E Shepherd, Chun Li, Frank E Harrell
We study the application of a widely used ordinal regression model, the cumulative probability model (CPM), for continuous outcomes. Such models are attractive for the analysis of continuous response variables because they are invariant to any monotonic transformation of the outcome and because they directly model the cumulative distribution function from which summaries such as expectations and quantiles can easily be derived. Such models can also readily handle mixed type distributions. We describe the motivation, estimation, inference, model assumptions, and diagnostics...
September 5, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28853158/a-pairwise-likelihood-augmented-cox-estimator-for-left-truncated-data
#3
Fan Wu, Sehee Kim, Jing Qin, Rajiv Saran, Yi Li
Survival data collected from a prevalent cohort are subject to left truncation and the analysis is challenging. Conditional approaches for left-truncated data could be inefficient as they ignore the information in the marginal likelihood of the truncation times. Length-biased sampling methods may improve the estimation efficiency but only when the underlying truncation time is uniform; otherwise, they may generate biased estimates. We propose a semiparametric method for left-truncated data under the Cox model with no parametric distributional assumption about the truncation times...
August 29, 2017: Biometrics
https://www.readbyqxmd.com/read/28771664/outcome-dependent-sampling-with-interval-censored-failure-time-data
#4
Qingning Zhou, Jianwen Cai, Haibo Zhou
Epidemiologic studies and disease prevention trials often seek to relate an exposure variable to a failure time that suffers from interval-censoring. When the failure rate is low and the time intervals are wide, a large cohort is often required so as to yield reliable precision on the exposure-failure-time relationship. However, large cohort studies with simple random sampling could be prohibitive for investigators with a limited budget, especially when the exposure variables are expensive to obtain. Alternative cost-effective sampling designs and inference procedures are therefore desirable...
August 3, 2017: Biometrics
https://www.readbyqxmd.com/read/28744876/a-semiparametric-method-for-comparing-the-discriminatory-ability-of-biomarkers-subject-to-limit-of-detection
#5
Lixuan Yin, Guoqing Diao, Aiyi Liu
Receiver operating characteristic curves and the area under the curves (AUC) are often used to compare the discriminatory ability of potentially correlated biomarkers. Many biomarkers are subject to limit of detection due to the instrumental limitation in measurements and may not be normally distributed. Standard parametric methods assuming normality can lead to biased results when the normality assumption is violated. We propose new estimation and inference procedures for the AUCs of biomarkers subject to limit of detection by using the semiparametric transformation model allowing for heteroscedasticity...
July 25, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28694745/model-averaging-with-aic-weights-for-hypothesis-testing-of-hormesis-at-low-doses
#6
Steven B Kim, Nathan Sanders
For many dose-response studies, large samples are not available. Particularly, when the outcome of interest is binary rather than continuous, a large sample size is required to provide evidence for hormesis at low doses. In a small or moderate sample, we can gain statistical power by the use of a parametric model. It is an efficient approach when it is correctly specified, but it can be misleading otherwise. This research is motivated by the fact that data points at high experimental doses have too much contribution in the hypothesis testing when a parametric model is misspecified...
April 2017: Dose-response: a Publication of International Hormesis Society
https://www.readbyqxmd.com/read/28653408/evaluating-principal-surrogate-markers-in-vaccine-trials-in-the-presence-of-multiphase-sampling
#7
Ying Huang
This article focuses on the evaluation of vaccine-induced immune responses as principal surrogate markers for predicting a given vaccine's effect on the clinical endpoint of interest. To address the problem of missing potential outcomes under the principal surrogate framework, we can utilize baseline predictors of the immune biomarker(s) or vaccinate uninfected placebo recipients at the end of the trial and measure their immune biomarkers. Examples of good baseline predictors are baseline immune responses when subjects enrolled in the trial have been previously exposed to the same antigen, as in our motivating application of the Zostavax Efficacy and Safety Trial (ZEST)...
June 26, 2017: Biometrics
https://www.readbyqxmd.com/read/28649172/control-function-assisted-ipw-estimation-with-a-secondary-outcome-in-case-control-studies
#8
Tamar Sofer, Marilyn C Cornelis, Peter Kraft, Eric J Tchetgen Tchetgen
Case-control studies are designed towards studying associations between risk factors and a single, primary outcome. Information about additional, secondary outcomes is also collected, but association studies targeting such secondary outcomes should account for the case-control sampling scheme, or otherwise results may be biased. Often, one uses inverse probability weighted (IPW) estimators to estimate population effects in such studies. IPW estimators are robust, as they only require correct specification of the mean regression model of the secondary outcome on covariates, and knowledge of the disease prevalence...
April 2017: Statistica Sinica
https://www.readbyqxmd.com/read/28608412/semiparametric-regression-on-cumulative-incidence-function-with-interval-censored-competing-risks-data
#9
Giorgos Bakoyannis, Menggang Yu, Constantin T Yiannoutsos
Many biomedical and clinical studies with time-to-event outcomes involve competing risks data. These data are frequently subject to interval censoring. This means that the failure time is not precisely observed but is only known to lie between two observation times such as clinical visits in a cohort study. Not taking into account the interval censoring may result in biased estimation of the cause-specific cumulative incidence function, an important quantity in the competing risks framework, used for evaluating interventions in populations, for studying the prognosis of various diseases, and for prediction and implementation science purposes...
October 15, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28608228/joint-analysis-of-interval-censored-failure-time-data-and-panel-count-data
#10
Da Xu, Hui Zhao, Jianguo Sun
Interval-censored failure time data and panel count data are two types of incomplete data that commonly occur in event history studies and many methods have been developed for their analysis separately (Sun in The statistical analysis of interval-censored failure time data. Springer, New York, 2006; Sun and Zhao in The statistical analysis of panel count data. Springer, New York, 2013). Sometimes one may be interested in or need to conduct their joint analysis such as in the clinical trials with composite endpoints, for which it does not seem to exist an established approach in the literature...
June 12, 2017: Lifetime Data Analysis
https://www.readbyqxmd.com/read/28560768/modeling-event-count-data-in-the-presence-of-informative-dropout-with-application-to-bleeding-and-transfusion-events-in-myelodysplastic-syndrome
#11
Guoqing Diao, Donglin Zeng, Kuolung Hu, Joseph G Ibrahim
In many biomedical studies, it is often of interest to model event count data over the study period. For some patients, we may not follow up them for the entire study period owing to informative dropout. The dropout time can potentially provide valuable insight on the rate of the events. We propose a joint semiparametric model for event count data and informative dropout time that allows for correlation through a Gamma frailty. We develop efficient likelihood-based estimation and inference procedures. The proposed nonparametric maximum likelihood estimators are shown to be consistent and asymptotically normal...
May 30, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28529839/estimating-effects-with-rare-outcomes-and-high-dimensional-covariates-knowledge-is-power
#12
Laura Balzer, Jennifer Ahern, Sandro Galea, Mark van der Laan
Many of the secondary outcomes in observational studies and randomized trials are rare. Methods for estimating causal effects and associations with rare outcomes, however, are limited, and this represents a missed opportunity for investigation. In this article, we construct a new targeted minimum loss-based estimator (TMLE) for the effect or association of an exposure on a rare outcome. We focus on the causal risk difference and statistical models incorporating bounds on the conditional mean of the outcome, given the exposure and measured confounders...
December 2016: Epidemiologic Methods
https://www.readbyqxmd.com/read/28504836/simple-and-fast-overidentified-rank-estimation-for-right-censored-length-biased-data-and-backward-recurrence-time
#13
Yifei Sun, Kwun Chuen Gary Chan, Jing Qin
Length-biased survival data subject to right-censoring are often collected from a prevalent cohort. However, informative right censoring induced by the sampling design creates challenges in methodological development. While certain conditioning arguments could circumvent the problem of informative censoring, related rank estimation methods are typically inefficient because the marginal likelihood of the backward recurrence time is not ancillary. Under a semiparametric accelerated failure time model, an overidentified set of log-rank estimating equations is constructed based on the left-truncated right-censored data and backward recurrence time...
May 15, 2017: Biometrics
https://www.readbyqxmd.com/read/28444688/semiparametric-estimation-of-the-accelerated-failure-time-model-with-partly-interval-censored-data
#14
Fei Gao, Donglin Zeng, Dan-Yu Lin
Partly interval-censored (PIC) data arise when some failure times are exactly observed while others are only known to lie within certain intervals. In this article, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with PIC data. We first generalize the Buckley-James estimator for right-censored data to PIC data. Then, we develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We show that under mild regularity conditions the generalized Buckley-James estimator is consistent and asymptotically normal and the one-step estimator is consistent and asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound...
April 25, 2017: Biometrics
https://www.readbyqxmd.com/read/28441139/empirical-likelihood-in-nonignorable-covariate-missing-data-problems
#15
Yanmei Xie, Biao Zhang
Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719-30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model...
April 20, 2017: International Journal of Biostatistics
https://www.readbyqxmd.com/read/28435181/orthogonality-of-the-mean-and-error-distribution-in-generalized-linear-models
#16
Alan Huang, Paul J Rathouz
We show that the mean-model parameter is always orthogonal to the error distribution in generalized linear models. Thus, the maximum likelihood estimator of the mean-model parameter will be asymptotically efficient regardless of whether the error distribution is known completely, known up to a finite vector of parameters, or left completely unspecified, in which case the likelihood is taken to be an appropriate semiparametric likelihood. Moreover, the maximum likelihood estimator of the mean-model parameter will be asymptotically independent of the maximum likelihood estimator of the error distribution...
2017: Communications in Statistics: Theory and Methods
https://www.readbyqxmd.com/read/28375451/multivariate-semiparametric-spatial-methods-for-imaging-data
#17
Huaihou Chen, Guanqun Cao, Ronald A Cohen
Univariate semiparametric methods are often used in modeling nonlinear age trajectories for imaging data, which may result in efficiency loss and lower power for identifying important age-related effects that exist in the data. As observed in multiple neuroimaging studies, age trajectories show similar nonlinear patterns for the left and right corresponding regions and for the different parts of a big organ such as the corpus callosum. To incorporate the spatial similarity information without assuming spatial smoothness, we propose a multivariate semiparametric regression model with a spatial similarity penalty, which constrains the variation of the age trajectories among similar regions...
April 1, 2017: Biostatistics
https://www.readbyqxmd.com/read/28239261/efficient-estimation-of-semiparametric-transformation-models-for-the-cumulative-incidence-of-competing-risks
#18
Lu Mao, D Y Lin
The cumulative incidence is the probability of failure from the cause of interest over a certain time period in the presence of other risks. A semiparametric regression model proposed by Fine and Gray (1999) has become the method of choice for formulating the effects of covariates on the cumulative incidence. Its estimation, however, requires modeling of the censoring distribution and is not statistically efficient. In this paper, we present a broad class of semiparametric transformation models which extends the Fine and Gray model, and we allow for unknown causes of failure...
March 2017: Journal of the Royal Statistical Society. Series B, Statistical Methodology
https://www.readbyqxmd.com/read/28226392/bayesian-semiparametric-variable-selection-with-applications-to-periodontal-data
#19
Bo Cai, Dipankar Bandyopadhyay
A normality assumption is typically adopted for the random effects in a clustered or longitudinal data analysis using a linear mixed model. However, such an assumption is not always realistic, and it may lead to potential biases of the estimates, especially when variable selection is taken into account. Furthermore, flexibility of nonparametric assumptions (e.g., Dirichlet process) on these random effects may potentially cause centering problems, leading to difficulty of interpretation of fixed effects and variable selection...
June 30, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28211951/semiparametric-regression-analysis-of-interval-censored-competing-risks-data
#20
Lu Mao, Dan-Yu Lin, Donglin Zeng
Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution...
February 17, 2017: Biometrics
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