Works matching IS 0006341X AND DT 2023 AND VI 79 AND IP 1
Results: 51
Confidence intervals for discrete data in clinical research.
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- Biometrics, 2023, v. 79, n. 1, p. 528, doi. 10.1111/biom.13839
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The effect: An introduction to research design and causality.
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- Biometrics, 2023, v. 79, n. 1, p. 531, doi. 10.1111/biom.13835
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Report of the Editors—2022.
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- Biometrics, 2023, v. 79, n. 1, p. 5, doi. 10.1111/biom.13834
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Gene expression data analysis: a statistical and machine learning perspective.
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- Biometrics, 2023, v. 79, n. 1, p. 526, doi. 10.1111/biom.13817
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Issue Information.
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- Biometrics, 2023, v. 79, n. 1, p. 1, doi. 10.1111/biom.13697
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Reader reaction to "Outcome‐adaptive lasso: Variable selection for causal inference" by Shortreed and Ertefaie (2017).
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- Biometrics, 2023, v. 79, n. 1, p. 514, doi. 10.1111/biom.13683
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Rejoinder to "Reader reaction to 'Outcome‐adaptive Lasso: Variable selection for causal inference' by Shortreed and Ertefaie (2017)".
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- Biometrics, 2023, v. 79, n. 1, p. 521, doi. 10.1111/biom.13681
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Adaptive enrichment designs with a continuous biomarker.
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- Biometrics, 2023, v. 79, n. 1, p. 9, doi. 10.1111/biom.13644
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Discussion on "Adaptive enrichment designs with a continuous biomarker" by Nigel Stallard.
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- Biometrics, 2023, v. 79, n. 1, p. 23, doi. 10.1111/biom.13643
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Discussion on "Adaptive enrichment designs with a continuous biomarker" by N. Stallard.
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- Biometrics, 2023, v. 79, n. 1, p. 26, doi. 10.1111/biom.13642
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Discussion on "Adaptive enrichment designs with a continuous biomarker" by Nigel Stallard.
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- Biometrics, 2023, v. 79, n. 1, p. 31, doi. 10.1111/biom.13641
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Discussion on "Adaptive enrichment designs with a continuous biomarker" by Nigel Stallard.
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- Biometrics, 2023, v. 79, n. 1, p. 20, doi. 10.1111/biom.13640
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Rejoinder to discussion on "Adaptive enrichment designs with a continuous biomarker".
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- Biometrics, 2023, v. 79, n. 1, p. 36, doi. 10.1111/biom.13639
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Covariate adjustment in continuous biomarker assessment.
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- Biometrics, 2023, v. 79, n. 1, p. 39, doi. 10.1111/biom.13601
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Risk prediction with imperfect survival outcome information from electronic health records.
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- Biometrics, 2023, v. 79, n. 1, p. 190, doi. 10.1111/biom.13599
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Sample size considerations for stepped wedge designs with subclusters.
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- Biometrics, 2023, v. 79, n. 1, p. 98, doi. 10.1111/biom.13596
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A smoothed corrected score approach for proportional hazards model with misclassified discretized covariates induced by error‐contaminated continuous time‐dependent exposure.
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- Biometrics, 2023, v. 79, n. 1, p. 437, doi. 10.1111/biom.13595
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Dynamic logistic state space prediction model for clinical decision making.
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- Biometrics, 2023, v. 79, n. 1, p. 73, doi. 10.1111/biom.13593
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Debiased lasso for generalized linear models with a diverging number of covariates.
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- Biometrics, 2023, v. 79, n. 1, p. 344, doi. 10.1111/biom.13587
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Functional additive models for optimizing individualized treatment rules.
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- Biometrics, 2023, v. 79, n. 1, p. 113, doi. 10.1111/biom.13586
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Noniterative adjustment to regression estimators with population‐based auxiliary information for semiparametric models.
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- Biometrics, 2023, v. 79, n. 1, p. 140, doi. 10.1111/biom.13585
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Logistic regression analysis of two‐phase studies using generalized method of moments.
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- Biometrics, 2023, v. 79, n. 1, p. 241, doi. 10.1111/biom.13584
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Improving efficiency of inference in clinical trials with external control data.
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- Biometrics, 2023, v. 79, n. 1, p. 394, doi. 10.1111/biom.13583
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Inference in response‐adaptive clinical trials when the enrolled population varies over time.
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- Biometrics, 2023, v. 79, n. 1, p. 381, doi. 10.1111/biom.13582
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A novel statistical test for treatment differences in clinical trials using a response‐adaptive forward‐looking Gittins Index Rule.
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- Biometrics, 2023, v. 79, n. 1, p. 86, doi. 10.1111/biom.13581
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Integrating sample similarities into latent class analysis: a tree‐structured shrinkage approach.
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- Biometrics, 2023, v. 79, n. 1, p. 264, doi. 10.1111/biom.13580
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An individual level infectious disease model in the presence of uncertainty from multiple, imperfect diagnostic tests.
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- Biometrics, 2023, v. 79, n. 1, p. 426, doi. 10.1111/biom.13579
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A linear mixed model to estimate COVID‐19‐induced excess mortality.
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- Biometrics, 2023, v. 79, n. 1, p. 417, doi. 10.1111/biom.13578
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Spectra in low‐rank localized layers (SpeLLL) for interpretable time–frequency analysis.
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- Biometrics, 2023, v. 79, n. 1, p. 304, doi. 10.1111/biom.13577
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Bayesian nonparametric quantile process regression and estimation of marginal quantile effects.
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- Biometrics, 2023, v. 79, n. 1, p. 151, doi. 10.1111/biom.13576
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CASANOVA: Permutation inference in factorial survival designs.
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- Biometrics, 2023, v. 79, n. 1, p. 203, doi. 10.1111/biom.13575
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Jackknife model averaging for high‐dimensional quantile regression.
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- Biometrics, 2023, v. 79, n. 1, p. 178, doi. 10.1111/biom.13574
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Accounting for post‐randomization variables in meta‐analysis: A joint meta‐regression approach.
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- Biometrics, 2023, v. 79, n. 1, p. 358, doi. 10.1111/biom.13573
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Generalized case‐control sampling under generalized linear models.
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- Biometrics, 2023, v. 79, n. 1, p. 332, doi. 10.1111/biom.13571
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On restricted mean time in favor of treatment.
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- Biometrics, 2023, v. 79, n. 1, p. 61, doi. 10.1111/biom.13570
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Bayesian multiple index models for environmental mixtures.
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- Biometrics, 2023, v. 79, n. 1, p. 462, doi. 10.1111/biom.13569
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Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs.
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- Biometrics, 2023, v. 79, n. 1, p. 449, doi. 10.1111/biom.13568
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Instrumental variable estimation of complier causal treatment effect with interval‐censored data.
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- Biometrics, 2023, v. 79, n. 1, p. 253, doi. 10.1111/biom.13565
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Variable selection in nonlinear function‐on‐scalar regression.
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- Biometrics, 2023, v. 79, n. 1, p. 292, doi. 10.1111/biom.13564
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Evaluating the association between latent classes and competing risks outcomes with multiphenotype data.
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- Biometrics, 2023, v. 79, n. 1, p. 488, doi. 10.1111/biom.13563
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Interim monitoring in sequential multiple assignment randomized trials.
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- Biometrics, 2023, v. 79, n. 1, p. 368, doi. 10.1111/biom.13562
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A matching procedure for sequential experiments that iteratively learns which covariates improve power.
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- Biometrics, 2023, v. 79, n. 1, p. 216, doi. 10.1111/biom.13561
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Modelling publication bias and p‐hacking.
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- Biometrics, 2023, v. 79, n. 1, p. 319, doi. 10.1111/biom.13560
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Estimation of separable direct and indirect effects in continuous time.
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- Biometrics, 2023, v. 79, n. 1, p. 127, doi. 10.1111/biom.13559
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Sensitivity analyses informed by tests for bias in observational studies.
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- Biometrics, 2023, v. 79, n. 1, p. 475, doi. 10.1111/biom.13558
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Latent group detection in functional partially linear regression models.
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- Biometrics, 2023, v. 79, n. 1, p. 280, doi. 10.1111/biom.13557
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Accelerated failure time modeling via nonparametric mixtures.
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- Biometrics, 2023, v. 79, n. 1, p. 165, doi. 10.1111/biom.13556
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SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale.
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- Biometrics, 2023, v. 79, n. 1, p. 230, doi. 10.1111/biom.13555
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Causal inference with outcomes truncated by death in multiarm studies.
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- Biometrics, 2023, v. 79, n. 1, p. 502, doi. 10.1111/biom.13554
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Elastic priors to dynamically borrow information from historical data in clinical trials.
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- Biometrics, 2023, v. 79, n. 1, p. 49, doi. 10.1111/biom.13551
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