Imagen de Portada

Pattern Mixture Sensitivity Analyses via Multiple Imputations for Non‐Ignorable Dropout in Joint Modeling of Cognition and Risk of Dementia

Fuente: Wiley Open Access

Guardado en:

Detalles Bibliográficos
Autores principales:	Tetiana Gorbach, James R. Carpenter, Chris Frost, Maria Josefsson, Jennifer Nicholas, Lars Nyberg
Formato:	Artículo Open Access
Publicado:	Wiley 2025
Materias:	Statistics in Medicine
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood
por: Changchang Xu, et al.
Publicado: (2026)

The Role of Congeniality in Multiple Imputation for Doubly Robust Causal Estimation
por: Lucy D'Agostino McGowan
Publicado: (2026)

A Sensitivity Analysis Framework Using the Proxy Pattern–Mixture Model for Generalization of Experimental Results
por: Rebecca R. Andridge, et al.
Publicado: (2025)

Multiple Imputation of Missing Covariates When Using the Fine–Gray Model
por: Edouard F. Bonneville, et al.
Publicado: (2025)

Analysis of Cohort Stepped Wedge Cluster‐Randomized Trials With Nonignorable Dropout via Joint Modeling
por: Alessandro Gasparini, et al.
Publicado: (2025)

Analyzing Coarsened and Missing Data by Imputation Methods
por: Lars L. J. van der Burg, et al.
Publicado: (2025)

Combining Missing Data Imputation and Internal Validation in Clinical Risk Prediction Models
por: Junhui Mi, et al.
Publicado: (2025)

Random Survival Forest With Multiple Imputation Analysis for Case‐Cohort and Generalized Case‐Cohort Studies
por: Haolin Li, et al.
Publicado: (2025)

A Bayesian Two‐Step Multiple Imputation Approach Based on Mixed Models for Missing EMA Data
por: Yiheng Wei, et al.
Publicado: (2025)

Constructing a T ‐Test for Value Function Comparison of Individualized Treatment Regimes in the Presence of Multiple Imputation for Missing Data
por: Minxin Lu, et al.
Publicado: (2025)

Sensitivity Analysis for Effects of Multiple Exposures in the Presence of Unmeasured Confounding: Non‐Gaussian and Time‐to‐Event Outcomes
por: Seungjae Lee, et al.
Publicado: (2024)

Joint Frailty Mixture Cure Model for Recurrent Event Data With Dependent Censoring: An MCEM Approach
por: Nasrin Sultana, et al.
Publicado: (2026)

Using Quadratic Programming to Reconstruct Data From Published Survival and Competing Risks Analyses
por: Andrew C. Titman
Publicado: (2026)

Missing Value Imputation With Adversarial Random Forests— MissARF
por: Pegah Golchian, et al.
Publicado: (2026)

Handling Missing Outcome Data in Cluster Randomized Trials With Both Individual‐ and Cluster‐Level Dropout
por: Analissa Avila, et al.
Publicado: (2025)

Assessing efficacy in non‐inferiority trials with non‐adherence to interventions: Are intention‐to‐treat and per‐protocol analyses fit for purpose?
por: Matthew Dodd, et al.
Publicado: (2024)

A Fully‐Integrated Bayesian Approach for the Imputation and Analysis of Derived Outcome Variables With Missingness
por: Harlan Campbell, et al.
Publicado: (2026)

A Comparison of Statistical Methods for Time‐To‐Event Analyses in Randomized Controlled Trials Under Non‐Proportional Hazards
por: Florian Klinglmüller, et al.
Publicado: (2025)

Bayesian Expectile Joint Model With Varying Coefficient for Longitudinal and Semi‐Competing Risks Data
por: Feng Gu, et al.
Publicado: (2025)

Comparison of Methods for Sensitivity Analysis of Heterogeneous Treatment Effects in Observational Studies and Application to Alzheimer's Disease and Cognitive Decline
por: Jingqi Duan, et al.
Publicado: (2026)

scRecover : Discriminating True and False Zeros in Single‐Cell RNA ‐Seq Data for Imputation
por: Zhun Miao, et al.
Publicado: (2025)

Frequency Band Analysis of Multiple Stationary Time Series
por: Connor K. Brubaker, et al.
Publicado: (2026)

A Bayesian Parametric and Nonparametric Approach for the Imputation of Multivariate Left‐Censored Data Due to Limit of Detection
por: Federico L. Perlino, et al.
Publicado: (2025)

Clustering‐Informed Shared‐Structure Variational Autoencoder for Missing Data Imputation in Large‐Scale Healthcare Data
por: Yasin Khadem Charvadeh, et al.
Publicado: (2025)

A Precision Mixture Risk Model to Identify Adverse Drug Events in Subpopulations Using a Case‐Crossover Design
por: Yi Shi, et al.
Publicado: (2024)

Cluster‐Level Analyses to Estimate a Risk Difference in a Cluster Randomized Trial With Confounding Individual‐Level Covariates: A Simulation Study
por: Jules Antoine Pereira Macedo, et al.
Publicado: (2025)

Non‐Parametric Estimation for Semi‐Competing Risks Data With Event Misascertainment
por: Ruiqian Wu, et al.
Publicado: (2025)

A Joint Model for (Un)Bounded Longitudinal Markers, Competing Risks, and Recurrent Events Using Patient Registry Data
por: Pedro Miranda Afonso, et al.
Publicado: (2025)

Dirichlet Distribution Parameter Estimation With Applications in Microbiome Analyses
por: Daniel T. Fuller, et al.
Publicado: (2026)

A Model Based on Mixture of Weibull Distributions for Depending Competing Risks Data in the Presence of Long‐Term Survivors, and Its Application to Malignant Melanoma Cancer Data
por: Ayon Ganguly, et al.
Publicado: (2026)

Futility Analyses for the MCP ‐Mod Methodology Based on Longitudinal Models
por: Björn Bornkamp, et al.
Publicado: (2025)

Causal Inference for First Non‐Fatal Events With the Competing Risk of Death: A Principal Stratification Approach
por: Jiren Sun, et al.
Publicado: (2025)

Sample Size Calculation for the ROCI Design
por: Henry Bern, et al.
Publicado: (2026)

Accounting for Misclassification of Cause of Death in Weighted Cumulative Incidence Functions for Causal Analyses
por: Jessie K. Edwards, et al.
Publicado: (2025)

Deep Mixture of Linear Mixed Models for Complex Longitudinal Data
por: Lucas Kock, et al.
Publicado: (2025)

A Bayesian Prevalence‐Incidence Mixture Model for Screening Outcomes With Misclassification
por: Thomas Klausch, et al.
Publicado: (2026)

Frequentist Grouped Weighted Quantile Sum Regression for Correlated Chemical Mixtures
por: Daniel Rud, et al.
Publicado: (2025)

A Bayesian Mixture of Exponential Family Factor Models for Uncovering Disease Progression Subtypes
por: Kai Kang, et al.
Publicado: (2025)

Bayesian Prediction of Event Times Using Mixture Model for Blinded Randomized Controlled Trials
por: Jingyan Fu, et al.
Publicado: (2025)

A Personalized Predictive Model That Jointly Optimizes Discrimination and Calibration
por: Tatiana Krikella, et al.
Publicado: (2025)