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Semiparametric efficient

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
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
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...
February 22, 2017: Statistics in Medicine
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
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
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
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
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
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
Ruosha Li, Yu Cheng, Qingxia Chen, Jason Fine
Bivariate survival data arise frequently in familial association studies of chronic disease onset, as well as in clinical trials and observational studies with multiple time to event endpoints. The association between two event times is often scientifically important. In this article, we examine the association via a novel quantile association measure, which describes the dynamic association as a function of the quantile levels. The quantile association measure is free of marginal distributions, allowing direct evaluation of the underlying association pattern at different locations of the event times...
September 9, 2016: Biometrics
John D Rice, Alex Tsodikov
In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one...
August 24, 2016: Biometrics
Yuxue Jin, Tze Leung Lai
An approximate likelihood approach is developed for regression analysis of censored competing-risks data. This approach models directly the cumulative incidence function, instead of the cause-specific hazard function, in terms of explanatory covariates under a proportional subdistribution hazards assumption. It uses a self-consistent iterative procedure to maximize an approximate semiparametric likelihood function, leading to an asymptotically normal and efficient estimator of the vector of regression parameters...
August 8, 2016: Lifetime Data Analysis
Huaihou Chen, Bingxin Zhao, Guanqun Cao, Eric C Proges, Andrew O'Shea, Adam J Woods, Ronald A Cohen
Neuroimaging studies of cognitive and brain aging often yield massive datasets that create many analytic and statistical challenges. In this paper, we discuss and address several limitations in the existing work. (1) Linear models are often used to model the age effects on neuroimaging markers, which may be inadequate in capturing the potential nonlinear age effects. (2) Marginal correlations are often used in brain network analysis, which are not efficient in characterizing a complex brain network. (3) Due to the challenge of high-dimensionality, only a small subset of the regional neuroimaging markers is considered in a prediction model, which could miss important regional markers...
2016: Frontiers in Aging Neuroscience
Yuhang Xu, Yehua Li, Xiao Song
We propose a new class of semiparametric estimators for proportional hazards models in the presence of measurement error in the covariates, where the baseline hazard function, the hazard function for the censoring time, and the distribution of the true covariates are considered as unknown infinite dimensional parameters. We estimate the model components by solving estimating equations based on the semiparametric efficient scores under a sequence of restricted models where the logarithm of the hazard functions are approximated by reduced rank regression splines...
June 2016: Scandinavian Journal of Statistics, Theory and Applications
Kaili Ren, Christopher A Drummond, Pamela S Brewster, Steven T Haller, Jiang Tian, Christopher J Cooper, Biao Zhang
Missing responses are common problems in medical, social, and economic studies. When responses are missing at random, a complete case data analysis may result in biases. A popular debias method is inverse probability weighting proposed by Horvitz and Thompson. To improve efficiency, Robins et al. proposed an augmented inverse probability weighting method. The augmented inverse probability weighting estimator has a double-robustness property and achieves the semiparametric efficiency lower bound when the regression model and propensity score model are both correctly specified...
November 30, 2016: Statistics in Medicine
Yongxiu Cao, Jian Huang, Yanyan Liu, Xingqiu Zhao
This article considers sieve estimation in the Cox model with an unknown regression structure based on right-censored data. We propose a semiparametric pursuit method to simultaneously identify and estimate linear and nonparametric covariate effects based on B-spline expansions through a penalized group selection method with concave penalties. We show that the estimators of the linear effects and the nonparametric component are consistent. Furthermore, we establish the asymptotic normality of the estimator of the linear effects...
December 2016: Biometrics
Xiaoyu Wang, James Y Dai
In randomized clinical trials, identifying baseline genetic or genomic markers for predicting subgroup treatment effects is of rising interest. Outcome-dependent sampling is often employed for measuring markers. The R package TwoPhaseInd implements a number of efficient statistical methods we developed for estimating subgroup treatment effects and gene-treatment interactions, exploiting the gene-treatment independence dictated by randomization, including the case-only estimator, the maximum estimated likelihood estimator and the semiparametric maximum likelihood estimator for parameters in a logistic model...
November 1, 2016: Bioinformatics
Hong Zhu, Jing Ning, Yu Shen, Jing Qin
In this article, we consider methods for assessing covariate effects on survival outcome in the target population when data are collected under prevalent sampling. We investigate a flexible semiparametric density ratio model without the constraints of the constant disease incidence rate and discrete covariates as required in Shen and others 2012. For inference, we introduce two likelihood approaches with distinct computational algorithms. We first develop a full likelihood approach to obtain the most efficient estimators by an iterative algorithm...
January 2017: Biostatistics
Kwun Chuen Gary Chan, Sheung Chi Phillip Yam, Zheng Zhang
The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either functions, we consider a wide class calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control, and the combined group...
June 2016: Journal of the Royal Statistical Society. Series B, Statistical Methodology
Jason Roy, Kirsten J Lum, Michael J Daniels
Marginal structural models (MSMs) are a general class of causal models for specifying the average effect of treatment on an outcome. These models can accommodate discrete or continuous treatments, as well as treatment effect heterogeneity (causal effect modification). The literature on estimation of MSM parameters has been dominated by semiparametric estimation methods, such as inverse probability of treatment weighted (IPTW). Likelihood-based methods have received little development, probably in part due to the need to integrate out confounders from the likelihood and due to reluctance to make parametric modeling assumptions...
January 2017: Biostatistics
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