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https://www.readbyqxmd.com/read/29286533/regression-analysis-for-secondary-response-variable-in-a-case-cohort-study
#1
Yinghao Pan, Jianwen Cai, Sangmi Kim, Haibo Zhou
Case-cohort study design has been widely used for its cost-effectiveness. In any real study, there are always other important outcomes of interest beside the failure time that the original case-cohort study is based on. How to utilize the available case-cohort data to study the relationship of a secondary outcome with the primary exposure obtained through the case-cohort study is not well studied. In this article, we propose a non-parametric estimated likelihood approach for analyzing a secondary outcome in a case-cohort study...
December 29, 2017: Biometrics
https://www.readbyqxmd.com/read/29280183/semiparametric-bayesian-models-for-evaluating-time-variant-driving-risk-factors-using-naturalistic-driving-data-and-case-crossover-approach
#2
Feng Guo, Inyong Kim, Sheila G Klauer
Driver behavior is a major contributing factor for traffic crashes, a leading cause of death and injury in the United States. The naturalistic driving study (NDS) revolutionizes driver behavior research by using sophisticated nonintrusive in-vehicle instrumentation to continuously record driving data. This paper uses a case-crossover approach to evaluate driver-behavior risk. To properly model the unbalanced and clustered binary outcomes, we propose a semiparametric hierarchical mixed-effect model to accommodate both among-strata and within-stratum variations...
December 26, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29239496/two-phase-designs-for-joint-quantitative-trait-dependent-and-genotype-dependent-sampling-in-post-gwas-regional-sequencing
#3
Osvaldo Espin-Garcia, Radu V Craiu, Shelley B Bull
We evaluate two-phase designs to follow-up findings from genome-wide association study (GWAS) when the cost of regional sequencing in the entire cohort is prohibitive. We develop novel expectation-maximization-based inference under a semiparametric maximum likelihood formulation tailored for post-GWAS inference. A GWAS-SNP (where SNP is single nucleotide polymorphism) serves as a surrogate covariate in inferring association between a sequence variant and a normally distributed quantitative trait (QT). We assess test validity and quantify efficiency and power of joint QT-SNP-dependent sampling and analysis under alternative sample allocations by simulations...
December 14, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29232301/robotic-mitral-valve-repair-the-learning-curve
#4
Avi Goodman, Marijan Koprivanac, Marta Kelava, Stephanie L Mick, A Marc Gillinov, Jeevanantham Rajeswaran, Anna Brzezinski, Eugene H Blackstone, Tomislav Mihaljevic
OBJECTIVE: Adoption of robotic mitral valve surgery has been slow, likely in part because of its perceived technical complexity and a poorly understood learning curve. We sought to correlate changes in technical performance and outcome with surgeon experience in the "learning curve" part of our series. METHODS: From 2006 to 2011, two surgeons undertook robotically assisted mitral valve repair in 458 patients (intent-to-treat); 404 procedures were completed entirely robotically (as-treated)...
November 2017: Innovations: Technology and Techniques in Cardiothoracic and Vascular Surgery
https://www.readbyqxmd.com/read/29200600/simultaneous-treatment-of-unspecified-heteroskedastic-model-error-distribution-and-mismeasured-covariates-for-restricted-moment-models
#5
Tanya P Garcia, Yanyuan Ma
We develop consistent and efficient estimation of parameters in general regression models with mismeasured covariates. We assume the model error and covariate distributions are unspecified, and the measurement error distribution is a general parametric distribution with unknown variance-covariance. We construct root-n consistent, asymptotically normal and locally efficient estimators using the semiparametric efficient score. We do not estimate any unknown distribution or model error heteroskedasticity. Instead, we form the estimator under possibly incorrect working distribution models for the model error, error-prone covariate, or both...
October 2017: Journal of Econometrics
https://www.readbyqxmd.com/read/29131931/covariate-adjusted-spearman-s-rank-correlation-with-probability-scale-residuals
#6
Qi Liu, Chun Li, Valentine Wanga, Bryan E Shepherd
It is desirable to adjust Spearman's rank correlation for covariates, yet existing approaches have limitations. For example, the traditionally defined partial Spearman's correlation does not have a sensible population parameter, and the conditional Spearman's correlation defined with copulas cannot be easily generalized to discrete variables. We define population parameters for both partial and conditional Spearman's correlation through concordance-discordance probabilities. The definitions are natural extensions of Spearman's rank correlation in the presence of covariates and are general for any orderable random variables...
November 13, 2017: Biometrics
https://www.readbyqxmd.com/read/29094375/collaborative-targeted-learning-using-regression-shrinkage
#7
Mireille E Schnitzer, Matthew Cefalu
Causal inference practitioners are routinely presented with the challenge of model selection and, in particular, reducing the size of the covariate set with the goal of improving estimation efficiency. Collaborative targeted minimum loss-based estimation (CTMLE) is a general framework for constructing doubly robust semiparametric causal estimators that data-adaptively limit model complexity in the propensity score to optimize a preferred loss function. This stepwise complexity reduction is based on a loss function placed on a strategically updated model for the outcome variable through which the error is assessed using cross-validation...
November 2, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/29071733/two-stage-orthogonality-based-estimation-for-semiparametric-varying-coefficient-models-and-its-applications-in-analyzing-aids-data
#8
Yan-Yong Zhao, Jin-Guan Lin, Xu-Guo Ye, Hong-Xia Wang, Xing-Fang Huang
Semiparametric smoothing methods are usually used to model longitudinal data, and the interest is to improve efficiency for regression coefficients. This paper is concerned with the estimation in semiparametric varying-coefficient models (SVCMs) for longitudinal data. By the orthogonal projection method, local linear technique, quasi-score estimation, and quasi-maximum likelihood estimation, we propose a two-stage orthogonality-based method to estimate parameter vector, coefficient function vector, and covariance function...
October 26, 2017: Biometrical Journal. Biometrische Zeitschrift
https://www.readbyqxmd.com/read/28983388/estimation-and-inference-of-error-prone-covariate-effect-in-the-presence-of-confounding-variables
#9
Jianxuan Liu, Yanyuan Ma, Liping Zhu, Raymond J Carroll
We introduce a general single index semiparametric measurement error model for the case that the main covariate of interest is measured with error and modeled parametrically, and where there are many other variables also important to the modeling. We propose a semiparametric bias-correction approach to estimate the effect of the covariate of interest. The resultant estimators are shown to be root-n consistent, asymptotically normal and locally efficient. Comprehensive simulations and an analysis of an empirical data set are performed to demonstrate the finite sample performance and the bias reduction of the locally efficient estimators...
2017: Electronic Journal of Statistics
https://www.readbyqxmd.com/read/28959115/semiparametric-regression-analysis-of-repeated-current-status-data
#10
Baosheng Liang, Xingwei Tong, Donglin Zeng, Yuanjia Wang
In many clinical studies, patients may be asked to report their medication adherence, presence of side effects, substance use, and hospitalization information during the study period. However, the exact occurrence time of these recurrent events may not be available due to privacy protection, recall difficulty, or incomplete medical records. Instead, the only available information is whether the events of interest have occurred during the past period. In this paper, we call these incomplete recurrent events as repeated current status data...
July 2017: Statistica Sinica
https://www.readbyqxmd.com/read/28943684/joint-scale-change-models-for-recurrent-events-and-failure-time
#11
Gongjun Xu, Sy Han Chiou, Chiung-Yu Huang, Mei-Cheng Wang, Jun Yan
Recurrent event data arise frequently in various fields such as biomedical sciences, public health, engineering, and social sciences. In many instances, the observation of the recurrent event process can be stopped by the occurrence of a correlated failure event, such as treatment failure and death. In this article, we propose a joint scale-change model for the recurrent event process and the failure time, where a shared frailty variable is used to model the association between the two types of outcomes. In contrast to the popular Cox-type joint modeling approaches, the regression parameters in the proposed joint scale-change model have marginal interpretations...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/28936916/bayesian-quantile-regression-based-partially-linear-mixed-effects-joint-models-for-longitudinal-data-with-multiple-features
#12
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
#13
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...
November 30, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28853158/a-pairwise-likelihood-augmented-cox-estimator-for-left-truncated-data
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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