A group sequential test for treatment effect based on the Fine –Gray model
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - March 13, 2018 Category: Biotechnology Source Type: research
Regularized continuous ‐time Markov Model via elastic net
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - March 13, 2018 Category: Biotechnology Source Type: research
Estimating individualized treatment rules for ordinal treatments
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - March 13, 2018 Category: Biotechnology Source Type: research
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Biometrics, Ahead of Print. (Source: Biometrics)
Source: Biometrics - March 13, 2018 Category: Biotechnology Source Type: research
Estimating individualized treatment rules for ordinal treatments
Summary
Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been proposed recently. One of the primary goals of precision medicine is to obtain an optimal individual treatment rule (ITR), which can help make decisions on treatment selection according to each patient's specific characteristics. Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clin...
Source: Biometrics - March 13, 2018 Category: Biotechnology Authors: Jingxiang Chen, Haoda Fu, Xuanyao He, Michael R. Kosorok, Yufeng Liu Tags: ORIGINAL ARTICLE Source Type: research
Regularized continuous ‐time Markov Model via elastic net
Summary
Continuous‐time Markov models are commonly used to analyze longitudinal transitions between multiple disease states in panel data, where participants’ disease states are only observed at multiple time points, and the exact state paths between observations are unknown. However, when covariate effects are incorporated and allowed to vary for different transitions, the number of potential parameters to estimate can become large even when the number of covariates is moderate, and traditional maximum likelihood estimation and subset model selection procedures can easily become unstable due to overfitting. We propose...
Source: Biometrics - March 13, 2018 Category: Biotechnology Authors: Shuang Huang, Chengcheng Hu, Melanie L. Bell, Dean Billheimer, Stefano Guerra, Denise Roe, Monica M. Vasquez, Edward J. Bedrick Tags: ORIGINAL ARTICLE Source Type: research
Model selection for semiparametric marginal mean regression accounting for within ‐cluster subsampling variability and informative cluster size
Summary
We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is “informative” in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within‐cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121–1134) and accommod...
Source: Biometrics - March 13, 2018 Category: Biotechnology Authors: Chung ‐Wei Shen, Yi‐Hau Chen Tags: ORIGINAL ARTICLE Source Type: research
A group sequential test for treatment effect based on the Fine –Gray model
Summary
Competing risks endpoints arise when patients can fail therapy from several causes. Analyzing these outcomes allows one to assess directly the benefit of treatment on a primary cause of failure in a clinical trial setting. Regression models can be used in clinical trials to adjust for residual imbalances in patient characteristics, improving the power to detect treatment differences. But, none of the competing risks methods currently available for use in group sequential trials adjust for covariates. We propose a group sequential test for treatment effect that, because it is based on the Fine–Gray model, permits ...
Source: Biometrics - March 13, 2018 Category: Biotechnology Authors: Michael J. Martens, Brent R. Logan Tags: ORIGINAL ARTICLE Source Type: research
An approximate joint model for multiple paired longitudinal outcomes and time ‐to‐event data
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - February 28, 2018 Category: Biotechnology Source Type: research
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Biometrics, Ahead of Print. (Source: Biometrics)
Source: Biometrics - February 28, 2018 Category: Biotechnology Source Type: research
A Bayesian nonparametric approach to causal inference on quantiles
Biometrics, EarlyView. (Source: Biometrics)
Source: Biometrics - February 25, 2018 Category: Biotechnology Source Type: research
