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https://www.readbyqxmd.com/read/29141110/discussion-on-quantifying-publication-bias-in-meta-analysis-by-lin-and-chu
#1
Nancy L Geller
No abstract text is available yet for this article.
November 15, 2017: Biometrics
https://www.readbyqxmd.com/read/29141108/rejoinder-to-quantifying-publication-bias-in-meta-analysis
#2
Lifeng Lin, Haitao Chu
No abstract text is available yet for this article.
November 15, 2017: Biometrics
https://www.readbyqxmd.com/read/29141099/discussion-on-quantifying-publication-bias-in-meta-analysis
#3
LETTER
Dan Jackson
In this discussion, I will describe some issues that are related to the article presented by Lin and Chu. In particular, I discuss three concerns that should be addressed before their methodology may be accepted for general use.
November 15, 2017: Biometrics
https://www.readbyqxmd.com/read/29141098/discussion-of-quantifying-publication-bias-in-meta-analysis-by-liu-et-al
#4
Christopher H Schmid
Inspection and analysis of funnel plots cannot reliably identify publication and reporting bias, the non-publication of results that are not statistically significant. Instead, researchers should thoroughly and systematically search available information sources such as databases, registries and unpublished reports. Even then, it is not possible to ever know whether a systematic review has uncovered all available studies, but the search can inform attempts to construct plausible statistical models of the missing data mechanism...
November 15, 2017: Biometrics
https://www.readbyqxmd.com/read/29141096/quantifying-publication-bias-in-meta-analysis
#5
Lifeng Lin, Haitao Chu
Publication bias is a serious problem in systematic reviews and meta-analyses, which can affect the validity and generalization of conclusions. Currently, approaches to dealing with publication bias can be distinguished into two classes: selection models and funnel-plot-based methods. Selection models use weight functions to adjust the overall effect size estimate and are usually employed as sensitivity analyses to assess the potential impact of publication bias. Funnel-plot-based methods include visual examination of a funnel plot, regression and rank tests, and the nonparametric trim and fill method...
November 15, 2017: Biometrics
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/29120498/integrated-powered-density-screening-ultrahigh-dimensional-covariates-with-survival-outcomes
#7
Hyokyoung G Hong, Xuerong Chen, David C Christiani, Yi Li
Modern biomedical studies have yielded abundant survival data with high-throughput predictors. Variable screening is a crucial first step in analyzing such data, for the purpose of identifying predictive biomarkers, understanding biological mechanisms, and making accurate predictions. To nonparametrically quantify the relevance of each candidate variable to the survival outcome, we propose integrated powered density (IPOD), which compares the differences in the covariate-stratified distribution functions. The proposed new class of statistics, with a flexible weighting scheme, is general and includes the Kolmogorov statistic as a special case...
November 9, 2017: Biometrics
https://www.readbyqxmd.com/read/29120492/testing-for-gene-environment-interaction-under-exposure-misspecification
#8
Ryan Sun, Raymond J Carroll, David C Christiani, Xihong Lin
Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects...
November 9, 2017: Biometrics
https://www.readbyqxmd.com/read/29099991/discussion-of-data-driven-confounder-selection-via-markov-and-bayesian-networks-by-jenny-h%C3%A3-ggstr%C3%A3-m
#9
Edward H Kennedy, Sivaraman Balakrishnan
No abstract text is available yet for this article.
November 2, 2017: Biometrics
https://www.readbyqxmd.com/read/29096050/discussion-of-data-driven-confounder-selection-via-markov-and-bayesian-networks-by-h%C3%A3-ggstr%C3%A3-m
#10
LETTER
Thomas S Richardson, James M Robins, Linbo Wang
No abstract text is available yet for this article.
November 2, 2017: Biometrics
https://www.readbyqxmd.com/read/29096048/reader-reaction-a-note-on-testing-and-estimation-in-marker-set-association-study-using-semiparametric-quantile-regression-kernel-machine
#11
Xiang Zhan, Michael C Wu
Kong et al. (2016, Biometrics 72, 364-371) presented a quantile regression kernel machine (QRKM) test for robust analysis of genetic marker-set association studies. A potential limitation of QRKM is the permutation-based test design may be unscalable for the massive sizes of modern datasets. In this article, we present an alternative strategy for p-value calculation of QRKM, which is capable of speeding up the QRKM testing procedure dramatically while maintaining the same testing performance as QRKM. The effectiveness of our approach is demonstrated via simulation studies...
November 2, 2017: Biometrics
https://www.readbyqxmd.com/read/29096038/rejoinder-to-a-note-on-testing-and-estimation-in-marker-set-association-study-using-semiparametric-quantile-regression-kernel-machine
#12
Dehan Kong, Arnab Maity, Fang-Chi Hsu, Jung-Ying Tzeng
No abstract text is available yet for this article.
November 2, 2017: Biometrics
https://www.readbyqxmd.com/read/29096036/data-driven-confounder-selection-via-markov-and-bayesian-networks
#13
Jenny Häggström
To unbiasedly estimate a causal effect on an outcome unconfoundedness is often assumed. If there is sufficient knowledge on the underlying causal structure then existing confounder selection criteria can be used to select subsets of the observed pretreatment covariates, X, sufficient for unconfoundedness, if such subsets exist. Here, estimation of these target subsets is considered when the underlying causal structure is unknown. The proposed method is to model the causal structure by a probabilistic graphical model, for example, a Markov or Bayesian network, estimate this graph from observed data and select the target subsets given the estimated graph...
November 2, 2017: Biometrics
https://www.readbyqxmd.com/read/29096035/rejoinder-to-discussions-on-data-driven-confounder-selection-via-markov-and-bayesian-networks
#14
Jenny Häggström
No abstract text is available yet for this article.
November 2, 2017: Biometrics
https://www.readbyqxmd.com/read/29092100/improved-dynamic-predictions-from-joint-models-of-longitudinal-and-survival-data-with-time-varying-effects-using-p-splines
#15
Eleni-Rosalina Andrinopoulou, Paul H C Eilers, Johanna J M Takkenberg, Dimitris Rizopoulos
In the field of cardio-thoracic surgery, valve function is monitored over time after surgery. The motivation for our research comes from a study which includes patients who received a human tissue valve in the aortic position. These patients are followed prospectively over time by standardized echocardiographic assessment of valve function. Loss of follow-up could be caused by valve intervention or the death of the patient. One of the main characteristics of the human valve is that its durability is limited...
November 1, 2017: Biometrics
https://www.readbyqxmd.com/read/29088515/optimal-treatment-assignment-to-maximize-expected-outcome-with-multiple-treatments
#16
Zhilan Lou, Jun Shao, Menggang Yu
When there is substantial heterogeneity of treatment effectiveness, it is crucial to identify individualized treatment assignment rules for comparative treatment selection. Traditional approaches directly model clinical outcome and define optimal treatment rule according to the interactions between treatment and covariates. This approach relies on the success of separating the main effects from the covariate-treatment interaction effects, which may not be easy. To overcome this shortcoming, a recent approach, called outcome weighted learning, focuses on building an optimal treatment rule by maximizing the expected clinical outcome related with differential treatments...
October 31, 2017: Biometrics
https://www.readbyqxmd.com/read/29088497/statistical-inference-in-a-growth-curve-quantile-regression-model-for-longitudinal-data
#17
Hyunkeun Ryan Cho
This article describes a polynomial growth curve quantile regression model that provides a comprehensive assessment about the treatment effects on the changes of the distribution of outcomes over time. The proposed model has the flexibility, as it allows the degree of a polynomial to vary across quantiles. A high degree polynomial model fits the data adequately, yet it is not desirable due to the complexity of the model. We propose the model selection criterion based on an empirical loglikelihood that consistently identifies the optimal degree of a polynomial at each quantile...
October 31, 2017: Biometrics
https://www.readbyqxmd.com/read/29088494/modeling-associations-between-latent-event-processes-governing-time-series-of-pulsing-hormones
#18
Huayu Liu, Nichole E Carlson, Gary K Grunwald, Alex J Polotsky
This work is motivated by a desire to quantify relationships between two time series of pulsing hormone concentrations. The locations of pulses are not directly observed and may be considered latent event processes. The latent event processes of pulsing hormones are often associated. It is this joint relationship we model. Current approaches to jointly modeling pulsing hormone data generally assume that a pulse in one hormone is coupled with a pulse in another hormone (one-to-one association). However, pulse coupling is often imperfect...
October 31, 2017: Biometrics
https://www.readbyqxmd.com/read/29073330/cox-regression-model-with-doubly-truncated-data
#19
Lior Rennert, Sharon X Xie
Truncation is a well-known phenomenon that may be present in observational studies of time-to-event data. While many methods exist to adjust for either left or right truncation, there are very few methods that adjust for simultaneous left and right truncation, also known as double truncation. We propose a Cox regression model to adjust for this double truncation using a weighted estimating equation approach, where the weights are estimated from the data both parametrically and nonparametrically, and are inversely proportional to the probability that a subject is observed...
October 26, 2017: Biometrics
https://www.readbyqxmd.com/read/29073327/estimation-of-cis-eqtl-effect-sizes-using-a-log-of-linear-model
#20
John Palowitch, Andrey Shabalin, Yi-Hui Zhou, Andrew B Nobel, Fred A Wright
The study of expression Quantitative Trait Loci (eQTL) is an important problem in genomics and biomedicine. While detection (testing) of eQTL associations has been widely studied, less work has been devoted to the estimation of eQTL effect size. To reduce false positives, detection methods frequently rely on linear modeling of rank-based normalized or log-transformed gene expression data. Unfortunately, these approaches do not correspond to the simplest model of eQTL action, and thus yield estimates of eQTL association that can be uninterpretable and inaccurate...
October 26, 2017: Biometrics
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