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Biometrics

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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/29286532/semiparametric-estimation-of-the-accelerated-mean-model-with-panel-count-data-under-informative-examination-times
#2
Sy Han Chiou, Gongjun Xu, Jun Yan, Chiung-Yu Huang
Panel count data arise when the number of recurrent events experienced by each subject is observed intermittently at discrete examination times. The examination time process can be informative about the underlying recurrent event process even after conditioning on covariates. We consider a semiparametric accelerated mean model for the recurrent event process and allow the two processes to be correlated through a shared frailty. The regression parameters have a simple marginal interpretation of modifying the time scale of the cumulative mean function of the event process...
December 29, 2017: Biometrics
https://www.readbyqxmd.com/read/29270978/general-single-index-survival-regression-models-for-incident-and-prevalent-covariate-data-and-prevalent-data-without-follow-up
#3
Shih-Wei Chen, Chin-Tsang Chiang
This article mainly focuses on analyzing covariate data from incident and prevalent cohort studies and a prevalent sample with only baseline covariates of interest and truncation times. Our major task in both research streams is to identify the effects of covariates on a failure time through very general single-index survival regression models without observing survival outcomes. With a strict increase of the survival function in the linear predictor, the ratio of incident and prevalent covariate densities is shown to be a non-degenerate and monotonic function of the linear predictor under covariate-independent truncation...
December 21, 2017: Biometrics
https://www.readbyqxmd.com/read/29265179/detecting-treatment-differences-in-group-sequential-longitudinal-studies-with-covariate-adjustment
#4
Neal O Jeffries, James F Troendle, Nancy L Geller
In longitudinal studies comparing two treatments over a series of common follow-up measurements, there may be interest in determining if there is a treatment difference at any follow-up period when there may be a non-monotone treatment effect over time. To evaluate this question, Jeffries and Geller (2015) examined a number of clinical trial designs that allowed adaptive choice of the follow-up time exhibiting the greatest evidence of treatment difference in a group sequential testing setting with Gaussian data...
December 18, 2017: Biometrics
https://www.readbyqxmd.com/read/29238965/sieve-analysis-using-the-number-of-infecting-pathogens
#5
Dean Follmann, Chiung-Yu Huang
Assessment of vaccine efficacy as a function of the similarity of the infecting pathogen to the vaccine is an important scientific goal. Characterization of pathogen strains for which vaccine efficacy is low can increase understanding of the vaccine's mechanism of action and offer targets for vaccine improvement. Traditional sieve analysis estimates differential vaccine efficacy using a single identifiable pathogen for each subject. The similarity between this single entity and the vaccine immunogen is quantified, for example, by exact match or number of mismatched amino acids...
December 14, 2017: Biometrics
https://www.readbyqxmd.com/read/29228509/c-learning-a-new-classification-framework-to-estimate-optimal-dynamic-treatment-regimes
#6
Baqun Zhang, Min Zhang
A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error...
December 11, 2017: Biometrics
https://www.readbyqxmd.com/read/29228504/dynamic-borrowing-through-empirical-power-priors-that-control-type-i-error
#7
Stavros Nikolakopoulos, Ingeborg van der Tweel, Kit C B Roes
In order for historical data to be considered for inclusion in the design and analysis of clinical trials, prospective rules are essential. Incorporation of historical data may be of particular interest in the case of small populations where available data is scarce and heterogeneity is not as well understood, and thus conventional methods for evidence synthesis might fall short. The concept of power priors can be particularly useful for borrowing evidence from a single historical study. Power priors employ a parameter γ∈[0,1] that quantifies the heterogeneity between the historical study and the new study...
December 11, 2017: Biometrics
https://www.readbyqxmd.com/read/29228454/new-semiparametric-method-for-predicting-high-cost-patients
#8
Adam Maidman, Lan Wang
Motivated by the Medical Expenditure Panel Survey containing data from individuals' medical providers and employers across the United States, we propose a new semiparametric procedure for predicting whether a patient will incur high medical expenditure. Problems of the same nature arise in many other important applications where one would like to predict if a future response occurs at the upper (or lower) tail of the response distribution. The common practice is to artificially dichotomize the response variable and then apply an existing classification method such as binomial regression or a classification tree...
December 11, 2017: Biometrics
https://www.readbyqxmd.com/read/29192968/a-regression-framework-for-assessing-covariate-effects-on-the-reproducibility-of-high-throughput-experiments
#9
Qunhua Li, Feipeng Zhang
The outcome of high-throughput biological experiments is affected by many operational factors in the experimental and data-analytical procedures. Understanding how these factors affect the reproducibility of the outcome is critical for establishing workflows that produce replicable discoveries. In this article, we propose a regression framework, based on a novel cumulative link model, to assess the covariate effects of operational factors on the reproducibility of findings from high-throughput experiments. In contrast to existing graphical approaches, our method allows one to succinctly characterize the simultaneous and independent effects of covariates on reproducibility and to compare reproducibility while controlling for potential confounding variables...
November 29, 2017: Biometrics
https://www.readbyqxmd.com/read/29192963/reader-reaction-on-the-fast-small-sample-kernel-independence-test-for-microbiome-community-level-association-analysis
#10
Bin Guo, Baolin Wu
Zhan et al. () presented a kernel RV coefficient (KRV) test to evaluate the overall association between host gene expression and microbiome composition, and showed its competitive performance compared to existing methods. In this article, we clarify the close relation of KRV to the existing generalized RV (GRV) coefficient, and show that KRV and GRV have very similar performance. Although the KRV test could control the type I error rate well at 1% and 5% levels, we show that it could largely underestimate p-values at small significance levels leading to significantly inflated type I errors...
November 29, 2017: Biometrics
https://www.readbyqxmd.com/read/29141110/discussion-on-quantifying-publication-bias-in-meta-analysis-by-lin-and-chu
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
LETTER
Thomas S Richardson, James M Robins, Linbo Wang
No abstract text is available yet for this article.
November 2, 2017: Biometrics
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