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https://www.readbyqxmd.com/read/28694745/model-averaging-with-aic-weights-for-hypothesis-testing-of-hormesis-at-low-doses
#1
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
#2
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
#3
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
#4
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...
June 12, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28608228/joint-analysis-of-interval-censored-failure-time-data-and-panel-count-data
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
https://www.readbyqxmd.com/read/28211087/semiparametric-pseudoscore-for-regression-with-multidimensional-but-incompletely-observed-regressor
#16
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/28004414/an-expectation-maximization-algorithm-for-fitting-the-generalized-odds-rate-model-to-interval-censored-data
#17
Jie Zhou, Jiajia Zhang, Wenbin Lu
The generalized odds-rate model is a class of semiparametric regression models, which includes the proportional hazards and proportional odds models as special cases. There are few works on estimation of the generalized odds-rate model with interval censored data because of the challenges in maximizing the complex likelihood function. In this paper, we propose a gamma-Poisson data augmentation approach to develop an Expectation Maximization algorithm, which can be used to fit the generalized odds-rate model to interval censored data...
December 21, 2016: Statistics in Medicine
https://www.readbyqxmd.com/read/27966260/statistical-inferences-for-data-from-studies-conducted-with-an-aggregated-multivariate-outcome-dependent-sample-design
#18
Tsui-Shan Lu, Matthew P Longnecker, Haibo Zhou
Outcome-dependent sampling (ODS) scheme is a cost-effective sampling scheme where one observes the exposure with a probability that depends on the outcome. The well-known such design is the case-control design for binary response, the case-cohort design for the failure time data, and the general ODS design for a continuous response. While substantial work has been carried out for the univariate response case, statistical inference and design for the ODS with multivariate cases remain under-developed. Motivated by the need in biological studies for taking the advantage of the available responses for subjects in a cluster, we propose a multivariate outcome-dependent sampling (multivariate-ODS) design that is based on a general selection of the continuous responses within a cluster...
March 15, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/27647948/a-semiparametrically-efficient-estimator-of-the-time-varying-effects-for-survival-data-with-time-dependent-treatment
#19
Huazhen Lin, Zhe Fei, Yi Li
The timing of a time-dependent treatment-e.g., when to perform a kidney transplantation-is an important factor for evaluating treatment efficacy. A naïve comparison between the treated and untreated groups, while ignoring the timing of treatment, typically yields biased results that might favor the treated group because only patients who survive long enough will get treated. On the other hand, studying the effect of a time-dependent treatment is often complex, as it involves modeling treatment history and accounting for the possible time-varying nature of the treatment effect...
September 2016: Scandinavian Journal of Statistics, Theory and Applications
https://www.readbyqxmd.com/read/27622394/connectivity-based-change-point-detection-for-large-size-functional-networks
#20
Seok-Oh Jeong, Chongwon Pae, Hae-Jeong Park
Recent understanding that the brain at rest does not remain in a single state but transiently visits multiple states emphasizes the importance of state changes embedded in the brain network. Due to the effectiveness of larger networks in characterizing brain states, there is an increasing need for a network-based change point detection method that is applicable to large-size networks, particularly those with longer time series. This paper presents a fast and efficient method for detecting change points in the large-size functional networks of resting-state fMRI...
December 2016: NeuroImage
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