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Biometrics

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https://www.readbyqxmd.com/read/27893928/multi-parameter-regression-survival-modeling-an-alternative-to-proportional-hazards
#1
K Burke, G MacKenzie
It is standard practice for covariates to enter a parametric model through a single distributional parameter of interest, for example, the scale parameter in many standard survival models. Indeed, the well-known proportional hazards model is of this kind. In this article, we discuss a more general approach whereby covariates enter the model through more than one distributional parameter simultaneously (e.g., scale and shape parameters). We refer to this practice as "multi-parameter regression" (MPR) modeling and explore its use in a survival analysis context...
November 28, 2016: Biometrics
https://www.readbyqxmd.com/read/27893927/model-averaged-double-robust-estimation
#2
Matthew Cefalu, Francesca Dominici, Nils Arvold, Giovanni Parmigiani
Researchers estimating causal effects are increasingly challenged with decisions on how to best control for a potentially high-dimensional set of confounders. Typically, a single propensity score model is chosen and used to adjust for confounding, while the uncertainty surrounding which covariates to include into the propensity score model is often ignored, and failure to include even one important confounder will results in bias. We propose a practical and generalizable approach that overcomes the limitations described above through the use of model averaging...
November 28, 2016: Biometrics
https://www.readbyqxmd.com/read/27893926/estimation-of-the-optimal-regime-in-treatment-of-prostate-cancer-recurrence-from-observational-data-using-flexible-weighting-models
#3
Jincheng Shen, Lu Wang, Jeremy M G Taylor
Prostate cancer patients are closely followed after the initial therapy and salvage treatment may be prescribed to prevent or delay cancer recurrence. The salvage treatment decision is usually made dynamically based on the patient's evolving history of disease status and other time-dependent clinical covariates. A multi-center prostate cancer observational study has provided us data on longitudinal prostate specific antigen (PSA) measurements, time-varying salvage treatment, and cancer recurrence time. These data enable us to estimate the best dynamic regime of salvage treatment, while accounting for the complicated confounding of time-varying covariates present in the data...
November 28, 2016: Biometrics
https://www.readbyqxmd.com/read/27893925/a-risk-based-measure-of-time-varying-prognostic-discrimination-for-survival-models
#4
C Jason Liang, Patrick J Heagerty
Prognostic survival models are commonly evaluated in terms of both their calibration and their discrimination. Comparing observed and predicted survival curves can assess calibration, while discrimination is typically summarized through comparison of the properties of "cases" or subjects who experience an event, and the properties of "controls" represented by event-free individuals. For binary data, discrimination is characterized either by using the relative ranks of cases and controls and a receiver operating characteristic (ROC) curve, or by summarizing the magnitude of risk placed on cases and controls through calculation of the discrimination slope (DS)...
November 28, 2016: Biometrics
https://www.readbyqxmd.com/read/27858978/bayesian-variable-selection-for-post-analytic-interrogation-of-susceptibility-loci
#5
Siying Chen, Sara Nunez, Muredach P Reilly, Andrea S Foulkes
Understanding the complex interplay among protein coding genes and regulatory elements requires rigorous interrogation with analytic tools designed for discerning the relative contributions of overlapping genomic regions. To this aim, we offer a novel application of Bayesian variable selection (BVS) for classifying genomic class level associations using existing large meta-analysis summary level resources. This approach is applied using the expectation maximization variable selection (EMVS) algorithm to typed and imputed SNPs across 502 protein coding genes (PCGs) and 220 long intergenic non-coding RNAs (lncRNAs) that overlap 45 known loci for coronary artery disease (CAD) using publicly available Global Lipids Gentics Consortium (GLGC) (Teslovich et al...
November 17, 2016: Biometrics
https://www.readbyqxmd.com/read/27858965/model-robust-inference-for-continuous-threshold-regression-models
#6
Youyi Fong, Chongzhi Di, Ying Huang, Peter B Gilbert
We study threshold regression models that allow the relationship between the outcome and a covariate of interest to change across a threshold value in the covariate. In particular, we focus on continuous threshold models, which experience no jump at the threshold. Continuous threshold regression functions can provide a useful summary of the association between outcome and the covariate of interest, because they offer a balance between flexibility and simplicity. Motivated by collaborative works in studying immune response biomarkers of transmission of infectious diseases, we study estimation of continuous threshold models in this article with particular attention to inference under model misspecification...
November 17, 2016: Biometrics
https://www.readbyqxmd.com/read/27861717/a-semiparametric-model-for-vqtl-mapping
#7
Chuan Hong, Yang Ning, Peng Wei, Ying Cao, Yong Chen
Quantitative trait locus analysis has been used as an important tool to identify markers where the phenotype or quantitative trait is linked with the genotype. Most existing tests for single locus association with quantitative traits aim at the detection of the mean differences across genotypic groups. However, recent research has revealed functional genetic loci that affect the variance of traits, known as variability-controlling quantitative trait locus. In addition, it has been suggested that many genotypes have both mean and variance effects, while the mean effects or variance effects alone may not be strong enough to be detected...
November 14, 2016: Biometrics
https://www.readbyqxmd.com/read/27792843/integrative-genetic-risk-prediction-using-non-parametric-empirical-bayes-classification
#8
Sihai Dave Zhao
Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain...
October 28, 2016: Biometrics
https://www.readbyqxmd.com/read/27775815/reader-reaction-on-estimation-of-treatment-effects-in-all-comers-randomized-clinical-trials-with-a-predictive-marker
#9
Edward L Korn, Boris Freidlin
For a fallback randomized clinical trial design with a marker, Choai and Matsui (2015, Biometrics 71, 25-32) estimate the bias of the estimator of the treatment effect in the marker-positive subgroup conditional on the treatment effect not being statistically significant in the overall population. This is used to construct and examine conditionally bias-corrected estimators of the treatment effect for the marker-positive subgroup. We argue that it may not be appropriate to correct for conditional bias in this setting...
October 24, 2016: Biometrics
https://www.readbyqxmd.com/read/27775814/a-subgroup-cluster-based-bayesian-adaptive-design-for-precision-medicine
#10
Wentian Guo, Yuan Ji, Daniel V T Catenacci
In precision medicine, a patient is treated with targeted therapies that are predicted to be effective based on the patient's baseline characteristics such as biomarker profiles. Oftentimes, patient subgroups are unknown and must be learned through inference using observed data. We present SCUBA, a Subgroup ClUster-based Bayesian Adaptive design aiming to fulfill two simultaneous goals in a clinical trial, 1) to treatments enrich the allocation of each subgroup of patients to their precision and desirable treatments and 2) to report multiple subgroup-treatment pairs (STPs)...
October 24, 2016: Biometrics
https://www.readbyqxmd.com/read/27775811/alternating-logistic-regressions-with-improved-finite-sample-properties
#11
Jamie Perin, John S Preisser
Alternating logistic regressions is an estimating equations procedure used to model marginal means of correlated binary outcomes while simultaneously specifying a within-cluster association model for log odds ratios of outcome pairs. A recent generalization of alternating logistic regressions, known as orthogonalized residuals, is extended to incorporate finite sample adjustments in the estimation of the log odds ratio model parameters for when there is a moderately small number of clusters. Bias adjustments are made both in the sandwich variance estimators and in the estimating equations for the association parameters...
October 24, 2016: Biometrics
https://www.readbyqxmd.com/read/27753074/distance-sampling-detection-functions-2d-or-not-2d
#12
David Louis Borchers, Martin James Cox
Conventional distance sampling (CDS) methods assume that animals are uniformly distributed in the vicinity of lines or points. But when animals move in response to observers before detection, or when lines or points are not located randomly, this assumption may fail. By formulating distance sampling models as survival models, we show that using time to first detection in addition to perpendicular distance (line transect surveys) or radial distance (point transect surveys) allows estimation of detection probability, and hence density, when animal distribution in the vicinity of lines or points is not uniform and is unknown...
October 17, 2016: Biometrics
https://www.readbyqxmd.com/read/27753071/inference-in-randomized-trials-with-death-and-missingness
#13
Chenguang Wang, Daniel O Scharfstein, Elizabeth Colantuoni, Timothy D Girard, Ying Yan
In randomized studies involving severely ill patients, functional outcomes are often unobserved due to missed clinic visits, premature withdrawal, or death. It is well known that if these unobserved functional outcomes are not handled properly, biased treatment comparisons can be produced. In this article, we propose a procedure for comparing treatments that is based on a composite endpoint that combines information on both the functional outcome and survival. We further propose a missing data imputation scheme and sensitivity analysis strategy to handle the unobserved functional outcomes not due to death...
October 17, 2016: Biometrics
https://www.readbyqxmd.com/read/27706799/survival-model-construction-guided-by-fit-and-predictive-strength
#14
C├ęcile Chauvel, John O'Quigley
Survival model construction can be guided by goodness-of-fit techniques as well as measures of predictive strength. Here, we aim to bring together these distinct techniques within the context of a single framework. The goal is how to best characterize and code the effects of the variables, in particular time dependencies, when taken either singly or in combination with other related covariates. Simple graphical techniques can provide an immediate visual indication as to the goodness-of-fit but, in cases of departure from model assumptions, will point in the direction of a more involved and richer alternative model...
October 5, 2016: Biometrics
https://www.readbyqxmd.com/read/27704531/greedy-outcome-weighted-tree-learning-of-optimal-personalized-treatment-rules
#15
Ruoqing Zhu, Ying-Qi Zhao, Guanhua Chen, Shuangge Ma, Hongyu Zhao
We propose a subgroup identification approach for inferring optimal and interpretable personalized treatment rules with high-dimensional covariates. Our approach is based on a two-step greedy tree algorithm to pursue signals in a high-dimensional space. In the first step, we transform the treatment selection problem into a weighted classification problem that can utilize tree-based methods. In the second step, we adopt a newly proposed tree-based method, known as reinforcement learning trees, to detect features involved in the optimal treatment rules and to construct binary splitting rules...
October 4, 2016: Biometrics
https://www.readbyqxmd.com/read/27704529/an-adaptive-mantel-haenszel-test-for-sensitivity-analysis-in-observational-studies
#16
Paul R Rosenbaum, Dylan S Small
In a sensitivity analysis in an observational study with a binary outcome, is it better to use all of the data or to focus on subgroups that are expected to experience the largest treatment effects? The answer depends on features of the data that may be difficult to anticipate, a trade-off between unknown effect-sizes and known sample sizes. We propose a sensitivity analysis for an adaptive test similar to the Mantel-Haenszel test. The adaptive test performs two highly correlated analyses, one focused analysis using a subgroup, one combined analysis using all of the data, correcting for multiple testing using the joint distribution of the two test statistics...
October 4, 2016: Biometrics
https://www.readbyqxmd.com/read/27704528/a-joint-modeling-approach-for-multivariate-survival-data-with-random-length
#17
Shuling Liu, Amita K Manatunga, Limin Peng, Michele Marcus
In many biomedical studies that involve correlated data, an outcome is often repeatedly measured for each individual subject along with the number of these measurements, which is also treated as an observed outcome. This type of data has been referred as multivariate random length data by Barnhart and Sampson (1995). A common approach to handling such type of data is to jointly model the multiple measurements and the random length. In previous literature, a key assumption is the multivariate normality for the multiple measurements...
October 4, 2016: Biometrics
https://www.readbyqxmd.com/read/27669414/testing-violations-of-the-exponential-assumption-in-cancer-clinical-trials-with-survival-endpoints
#18
Gang Han, Michael J Schell, Heping Zhang, Daniel Zelterman, Lajos Pusztai, Kerin Adelson, Christos Hatzis
Personalized cancer therapy requires clinical trials with smaller sample sizes compared to trials involving unselected populations that have not been divided into biomarker subgroups. The use of exponential survival modeling for survival endpoints has the potential of gaining 35% efficiency or saving 28% required sample size (Miller, 1983), making personalized therapy trials more feasible. However, the use of exponential survival has not been fully accepted in cancer research practice due to uncertainty about whether or not the exponential assumption holds...
September 26, 2016: Biometrics
https://www.readbyqxmd.com/read/27669160/a-bayesian-integrative-approach-for-multi-platform-genomic-data-a-kidney-cancer-case-study
#19
Thierry Chekouo, Francesco C Stingo, James D Doecke, Kim-Anh Do
Integration of genomic data from multiple platforms has the capability to increase precision, accuracy, and statistical power in the identification of prognostic biomarkers. A fundamental problem faced in many multi-platform studies is unbalanced sample sizes due to the inability to obtain measurements from all the platforms for all the patients in the study. We have developed a novel Bayesian approach that integrates multi-regression models to identify a small set of biomarkers that can accurately predict time-to-event outcomes...
September 26, 2016: Biometrics
https://www.readbyqxmd.com/read/27657666/hierarchical-group-testing-for-multiple-infections
#20
Peijie Hou, Joshua M Tebbs, Christopher R Bilder, Christopher S McMahan
Group testing, where individuals are tested initially in pools, is widely used to screen a large number of individuals for rare diseases. Triggered by the recent development of assays that detect multiple infections at once, screening programs now involve testing individuals in pools for multiple infections simultaneously. Tebbs, McMahan, and Bilder (2013, Biometrics) recently evaluated the performance of a two-stage hierarchical algorithm used to screen for chlamydia and gonorrhea as part of the Infertility Prevention Project in the United States...
September 22, 2016: Biometrics
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