Works matching IS 0006341X AND DT 2019 AND VI 75 AND IP 2
Results: 45
Issue Information ‐ Cover.
- Published in:
- Biometrics, 2019, v. 75, n. 2, p. N.PAG, doi. 10.1111/biom.13143
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- Article
Biostatistics for clinical and public health research.
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- Biometrics, 2019, v. 75, n. 2, p. 712, doi. 10.1111/biom.13081
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Quantitative methods for HIV/AIDS research.
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- Biometrics, 2019, v. 75, n. 2, p. 710, doi. 10.1111/biom.13080
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Causal inference in statistics.
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- Biometrics, 2019, v. 75, n. 2, p. 708, doi. 10.1111/biom.13079
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Exact inference for integrated population modelling.
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- Biometrics, 2019, v. 75, n. 2, p. 475, doi. 10.1111/biom.13045
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Errors, Blunders, and Lies.
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- Biometrics, 2019, v. 75, n. 2, p. 713, doi. 10.1111/biom.13041
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Report of the editors—2018.
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- Biometrics, 2019, v. 75, n. 2, p. 367, doi. 10.1111/biom.13033
- Publication type:
- Article
Model confidence bounds for variable selection.
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- Biometrics, 2019, v. 75, n. 2, p. 392, doi. 10.1111/biom.13024
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Discussion on "Model Confidence Bounds for Variable Selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin.
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- Biometrics, 2019, v. 75, n. 2, p. 404, doi. 10.1111/biom.13022
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Discussion on "Model confidence bounds for variable selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin.
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- Biometrics, 2019, v. 75, n. 2, p. 407, doi. 10.1111/biom.13021
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Rejoinder to Discussions on: Model confidence bounds for variable selection.
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- Biometrics, 2019, v. 75, n. 2, p. 411, doi. 10.1111/biom.13020
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Causal comparative effectiveness analysis of dynamic continuous‐time treatment initiation rules with sparsely measured outcomes and death.
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- Biometrics, 2019, v. 75, n. 2, p. 695, doi. 10.1111/biom.13018
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Estimations of the joint distribution of failure time and failure type with dependent truncation.
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- Biometrics, 2019, v. 75, n. 2, p. 428, doi. 10.1111/biom.13017
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- Publication type:
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Distribution‐free estimation of local growth rates around interval censored anchoring events.
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- Biometrics, 2019, v. 75, n. 2, p. 463, doi. 10.1111/biom.13015
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Dependence modeling for recurrent event times subject to right‐censoring with D‐vine copulas.
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- Biometrics, 2019, v. 75, n. 2, p. 439, doi. 10.1111/biom.13014
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Drawing inferences for high‐dimensional linear models: A selection‐assisted partial regression and smoothing approach.
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- Biometrics, 2019, v. 75, n. 2, p. 551, doi. 10.1111/biom.13013
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A modified partial likelihood score method for Cox regression with covariate error under the internal validation design.
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- Biometrics, 2019, v. 75, n. 2, p. 414, doi. 10.1111/biom.13012
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Linked matrix factorization.
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- Biometrics, 2019, v. 75, n. 2, p. 582, doi. 10.1111/biom.13010
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Generalizing causal inferences from individuals in randomized trials to all trial‐eligible individuals.
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- Biometrics, 2019, v. 75, n. 2, p. 685, doi. 10.1111/biom.13009
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Optimal design of multiple‐objective Lot Quality Assurance Sampling (LQAS) plans.
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- Biometrics, 2019, v. 75, n. 2, p. 572, doi. 10.1111/biom.13008
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Nonparametric group sequential methods for evaluating survival benefit from multiple short‐term follow‐up windows.
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- Biometrics, 2019, v. 75, n. 2, p. 494, doi. 10.1111/biom.13007
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Integrative multi‐view regression: Bridging group‐sparse and low‐rank models.
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- Biometrics, 2019, v. 75, n. 2, p. 593, doi. 10.1111/biom.13006
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A smoothing‐based goodness‐of‐fit test of covariance for functional data.
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- Biometrics, 2019, v. 75, n. 2, p. 562, doi. 10.1111/biom.13005
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Convex clustering analysis for histogram‐valued data.
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- Biometrics, 2019, v. 75, n. 2, p. 603, doi. 10.1111/biom.13004
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Composite kernel machine regression based on likelihood ratio test for joint testing of genetic and gene–environment interaction effect.
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- Biometrics, 2019, v. 75, n. 2, p. 625, doi. 10.1111/biom.13003
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A robust removing unwanted variation–testing procedure via γ‐divergence.
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- Biometrics, 2019, v. 75, n. 2, p. 650, doi. 10.1111/biom.13002
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Causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation.
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- Biometrics, 2019, v. 75, n. 2, p. 506, doi. 10.1111/biom.13001
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A Bayesian hidden Markov model for detecting differentially methylated regions.
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- Biometrics, 2019, v. 75, n. 2, p. 663, doi. 10.1111/biom.13000
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The single‐index/Cox mixture cure model.
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- Biometrics, 2019, v. 75, n. 2, p. 452, doi. 10.1111/biom.12999
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Exact inference on the random‐effects model for meta‐analyses with few studies.
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- Biometrics, 2019, v. 75, n. 2, p. 485, doi. 10.1111/biom.12998
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A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints.
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- Biometrics, 2019, v. 75, n. 2, p. 539, doi. 10.1111/biom.12997
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A non‐randomized procedure for large‐scale heterogeneous multiple discrete testing based on randomized tests.
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- Biometrics, 2019, v. 75, n. 2, p. 638, doi. 10.1111/biom.12996
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Log‐ratio lasso: Scalable, sparse estimation for log‐ratio models.
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- Biometrics, 2019, v. 75, n. 2, p. 613, doi. 10.1111/biom.12995
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A hybrid phase I‐II/III clinical trial design allowing dose re‐optimization in phase III.
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- Biometrics, 2019, v. 75, n. 2, p. 371, doi. 10.1111/biom.12994
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Discussion of "A Hybrid Phase I‐II/III Clinical Trial Design Allowing Dose Re‐Optimization in Phase III" by Andrew G. Chapple and Peter F. Thall.
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- Biometrics, 2019, v. 75, n. 2, p. 382, doi. 10.1111/biom.12993
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- Publication type:
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Discussion of "A hybrid phase I‐II/III clinical trial design allowing dose re‐optimization in phase III" by Andrew G. Chapple and Peter F. Thall.
- Published in:
- Biometrics, 2019, v. 75, n. 2, p. 385, doi. 10.1111/biom.12992
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- Publication type:
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Rejoinder to "A hybrid phase I‐II/III clinical trial design allowing dose reoptimization in phase III".
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- Biometrics, 2019, v. 75, n. 2, p. 389, doi. 10.1111/biom.12991
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- Publication type:
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Bagging and deep learning in optimal individualized treatment rules.
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- Biometrics, 2019, v. 75, n. 2, p. 674, doi. 10.1111/biom.12990
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Semiparametric Bayesian latent variable regression for skewed multivariate data.
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- Biometrics, 2019, v. 75, n. 2, p. 528, doi. 10.1111/biom.12989
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Instrumental variable based estimation under the semiparametric accelerated failure time model.
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- Biometrics, 2019, v. 75, n. 2, p. 516, doi. 10.1111/biom.12985
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State Space Methods for Time Series Analysis.
- Published in:
- Biometrics, 2019, v. 75, n. 2, p. 717, doi. 10.1111/biom.12981
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Hidden Markov Models for Time Series.
- Published in:
- Biometrics, 2019, v. 75, n. 2, p. 715, doi. 10.1111/biom.12980
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Issue Information ‐ Ed Board.
- Published in:
- Biometrics, 2019, v. 75, n. 2, p. 366, doi. 10.1111/biom.12933
- Publication type:
- Article
Issue Information ‐ Masthead.
- Published in:
- Biometrics, 2019, v. 75, n. 2, p. 365, doi. 10.1111/biom.12932
- Publication type:
- Article
Issue Information ‐ TOC & Cover Information.
- Published in:
- Biometrics, 2019, v. 75, n. 2, p. 363, doi. 10.1111/biom.12931
- Publication type:
- Article