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Record Nr. |
UNISA996466415503316 |
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Titolo |
Modern statistical methods for health research / / edited by Yichuan Zhao and (Din) Ding-Geng Chen |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (506 pages) |
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Collana |
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Emerging Topics in Statistics and Biostatistics Ser. |
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Disciplina |
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Soggetti |
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Medical statistics |
Big data |
Estadística mèdica |
Biometria |
Dades massives |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Part I: Health Data Analysis and Applications to EHR Data (Chaps. 1 -5) -- Part II: Clinical Trials, FDR, and Applications in Health Science (Chaps. 6 -10) -- Part III: Big Data Analytics and Its Applications (Chaps. 11 -15) -- Part IV: Survival Analysis and Functional Data Analysis (Chaps. 16 -18) -- Part V: Statistical Modeling in Genomic Studies (Chaps. 19 -21) -- Contents -- About the Editors -- -- -- List of Contributors -- -- List of Chapter Reviewers -- Part I Health Data Analysis and Applications to EHR Data -- The Effective Sample Size of EHR-Derived Cohorts Under Biased Sampling -- 1 Introduction -- 2 Methods -- 2.1 Notation and Definitions -- 2.2 Bias and Mean-Squared Error of the Simple Random Sample and the EHR-Based Sample -- 2.3 Effective Sample Size of the EHR-Derived Cohort -- 2.4 Simulation Study Design -- 3 Results -- 4 Discussion -- References -- Non-Gaussian Models for Object Motion Analysis with Time-Lapse Fluorescence Microscopy Images -- 1 Introduction -- 2 Method -- 2.1 Particle Tracking Framework -- 2.2 Object Segmentation -- 2.3 Observation and Dynamics Models -- Ellipsoid Model -- Voxel- |
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Based Model -- 2.4 Multiple Object Tracking Management -- 3 Experiments and Results -- 3.1 Validation with Artificial Data -- 3.2 Bacteria Motility Analysis -- 3.3 Tumor Spheroid Study -- 4 Conclusions -- References -- Alternative Capture-Recapture Point and Interval Estimators Based on Two Surveillance Streams -- 1 Introduction -- 2 Methods -- 2.1 The LP Conditions and Their Central Role -- 2.2 Some Cautionary Notes on Alternatives to the LP Estimator -- 2.3 Loglinear Models and a Perspective on the Use of Covariates -- 2.4 A Rationale for Renewed Statistical Interest Under the LP Conditions -- 2.5 Review of Classical Point Estimators in the Two-Capture Case. |
2.6 A Class of Estimators Including LP and Chap as Special Cases -- 2.7 A New Estimator Targeting Median Bias as a Criterion -- 2.8 New Alternatives to the Chapman Estimator Aimed at Reduced Mean Bias -- 2.9 Closed-Form Confidence Interval Estimation in the Two-Capture Case -- 2.10 An Adapted Bayesian Credible Interval Approach -- 3 Motivating Example Data and Results -- 4 Simulation Studies -- 5 Discussion -- References -- A Uniform Shrinkage Prior in Spatiotemporal Poisson Models for Count Data -- 1 Introduction -- 2 Derivation of a USP for the Variance Components in GLMM with Proper CAR and Its Properties -- 2.1 Derivation of the USP -- 2.2 Motivation of the Derived USP -- 2.3 Analytical Properties of the Derived USP -- 3 Application to the Leptospirosis Data -- 4 Simulation Study -- 5 Discussion -- References -- A Review of Multiply Robust Estimation with Missing Data -- 1 Introduction -- 2 Basic Setups -- 3 Multiply Robust Estimation Procedure -- 3.1 Calibration Approach -- 3.2 Projection Approach -- 3.3 Multiple Imputation Approach -- 4 Simulation Study -- 5 Real Application -- 6 Discussion -- References -- Part II Clinical Trials, FDR, and Applications in Health Science -- Approaches to Combining Phase II Proof-of-Conceptand Dose-Finding Trials -- 1 Introduction -- 2 Two Studies-Phase IIa (Proof-of-Concept) and Phase IIb (Dose-Finding) -- 3 A Single Study with Combined Objectives (PoC and DF) -- 3.1 Single Fixed Design -- 3.2 Two-Stage Phase IIa/IIb Adaptive Designs -- 4 Sample Size Comparison and Discussion -- 5 Concluding Remarks -- References -- Designs of Early Phase Cancer Trials with Drug Combinations -- 1 Introduction -- 2 Designs for Phase I Clinical Trials -- 2.1 Phase I Model-based Designs for Drug Combinations -- Model -- Prior and Posterior Distributions -- Trial Design -- Design Operating Characteristics -- Results. |
2.2 Attributing Dose-Limiting Toxicities -- Model -- Trial Design -- Results -- 2.3 Adding a Baseline Covariate -- Model -- Prior and Posterior Distributions -- Trial Design -- Results -- 3 Designs for Phase I-II Clinical Trials -- 3.1 Binary Endpoint -- Model -- Trial Design -- Results -- 3.2 Survival Endpoint -- Introduction -- Model -- Trial Design -- Results -- 4 Discussion -- References -- Controlling the False Discovery Rate of Grouped Hypotheses -- 1 Introduction -- 2 Modeling and Sequential Framework -- 2.1 Notation and Models -- 2.2 A General Framework for Grouped Multiple Testing Procedures -- 3 Procedures for Group Multiple Testing -- 3.1 Conditional Local FDR (CLfdr) -- 3.2 Group-Weighted Benjamini-Hochberg (GBH) -- 3.3 Weighting Fixed Cutoff (WFC) -- 3.4 Structure-Adaptive Benjamini-Hochberg (SABHA) -- 3.5 Independent Hypothesis Weighting (IHWc) -- 3.6 Adaptive p-Value Thresholding (AdaPT) -- 3.7 Linear and Nonlinear Rankings -- 4 Simulation -- 4.1 Results -- 5 Application -- 6 Conclusions and Discussions -- Appendix -- A.1 Two-Parameter AdaPT -- A.2 EM Steps -- A.3 Initialization -- References -- Classic Linear Mediation Analysis of Complex Survey Data Using Balanced Repeated Replication -- 1 Introduction -- 2 Technical Details -- 2.1 Mediation Model -- 2.2 Complex Surveys Using BRR -- 2.3 Mediation |
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Incorporating BRR -- Point Estimate -- Standard Error Estimate -- Significance Test -- 3 SAS Macro and Illustration -- 3.1 Components of %MediationBRR -- 3.2 Application to PISA: A Single-Mediator Model -- 3.3 Application to TUS-CPS: A Multi-Mediator Model -- 4 Discussion -- Appendix -- References -- A Review of Bayesian Optimal Experimental Design on DifferentModels -- 1 Introduction -- 1.1 Pseudo-Bayesian Optimal Design -- 1.2 Fully Bayesian Optimal Design -- 2 Bayesian Designs for Linear Models. |
3 Bayesian Designs for Generalized Linear Models -- 4 Bayesian Designs for Nonlinear Models -- 4.1 Bayesian Optimal Designs for PKPD Models -- 4.2 Bayesian Optimal Designs for Biological and Chemical Models -- 5 Conclusions -- References -- Part III Big Data Analytics and Its Applications -- A Selective Review on Statistical Techniques for Big Data -- 1 Introduction -- 2 Randomized Numerical Linear Algebra -- 2.1 Random Projection -- 2.2 Nonuniform Random Sampling -- 3 Information-Based Optimal Subdata Selection -- 4 Informative Subsampling -- 4.1 Optimal Subsampling -- 4.2 Local Case-Control Subsampling -- 5 Divide-and-Conquer and Updating Methods -- 5.1 Divide-and-Conquer Methods -- 5.2 Updating Methods -- Online Updating Methods -- Stochastic Gradient Descent -- 6 Summary and Discussion -- References -- A Selective Overview of Recent Advances in Spectral Clustering and Their Applications -- 1 Introduction -- 2 Spectral Clustering -- 2.1 The Similarity Matrix -- 2.2 Unnormalized Spectral Clustering -- 2.3 Normalized Spectral Clustering -- 2.4 Equivalence to Weighted Kernel k-Means -- 2.5 Selecting the Total Number of Clusters -- General Clustering-Independent Criteria -- Cluster Selection Criteria Specific to Spectral Clustering -- 3 New Developments of Spectral Clustering -- 3.1 Spectral Biclustering -- 3.2 Multi-View Spectral Clustering -- 3.3 High-Order Spectral Clustering -- 3.4 Constrained Spectral Clustering -- 3.5 Evolutionary Spectral Clustering -- PCQ -- PCM -- Determining the Weight Parameter α -- 3.6 Incremental Spectral Clustering -- 3.7 Sparse Spectral Clustering -- 4 Discussion -- References -- A Review on Modern Computational Optimal Transport Methods with Applications in Biomedical Research -- 1 Introduction -- 2 Background of the Optimal Transport Problem -- 3 Regularization-Based Optimal Transport Methods. |
3.1 Computational Cost for OT Problems -- 3.2 Sinkhorn Distance -- 3.3 Sinkhorn Algorithms with the Nyström Method -- 4 Projection-Based Optimal Transport Methods -- 4.1 Random Projection OT Method -- 4.2 Projection Pursuit OT Method -- 5 Applications in Biomedical Research -- 5.1 Identify Development Trajectories in Reprogramming -- 5.2 Data Augmentation for Biomedical Data -- References -- Variable Selection Approaches in High-Dimensional Space -- 1 Introduction -- 2 Penalized Likelihood Approaches -- 2.1 Penalty Functions -- 2.2 Canonical Models in High Dimension -- Linear Regression Model -- Logistic Regression Model -- Proportional Hazards Model -- 2.3 Algorithm and Implementation -- Penalized Weighted Least Squares -- Penalized Likelihoods -- Tuning Parameter Selection -- 3 Feature Screening for Ultra-High-Dimensional Data -- 3.1 Sure Independence Screening -- Correlation Ranking -- Maximum Marginal Likelihoods -- 3.2 Iterative Sure Independence Screening -- 3.3 Reduction of False Positive Rate -- 4 Real Data Example -- 5 High-Dimensional Inference -- 6 Conclusion -- References -- Estimation Methods for Item Factor Analysis: An Overview -- 1 Introduction -- 2 IFA Models -- 2.1 Modeling Framework -- 2.2 Examples of IFA Models -- 2.3 Exploratory and Confirmatory Analyses -- 3 Estimation Methods -- 3.1 Estimation Based on Joint Likelihood -- 3.2 Estimation Based on |
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Marginal Likelihood -- 3.3 Limited-Information Estimation -- 3.4 Spectral Method -- 4 Computer Implementations -- 5 Conclusions -- References -- Part IV Survival Analysis and Functional Data Analysis -- Functional Data Modeling and Hypothesis Testing for Longitudinal Alzheimer Genome-Wide Association Studies -- 1 Introduction -- 2 Functional Modeling of Longitudinal Phenotype Data and Estimation Procedure -- 2.1 Model Assumptions -- 2.2 Estimation Under the Full Model. |
2.3 Estimation Under the Reduced Model. |
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