Estimation and assessment of Markov multistate models with intermittent observations on individuals
From MaRDI portal
Publication:747353
DOI10.1007/S10985-014-9310-ZzbMath1322.62027OpenAlexW2040839837WikidataQ38261620 ScholiaQ38261620MaRDI QIDQ747353
Publication date: 16 October 2015
Published in: Lifetime Data Analysis (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10012/11013
maximum likelihoodpanel datatransition probabilityrelative efficiencygoodness of fittransition intensityprevalence probability
Related Items (2)
Analysis of Chronic Disease Processes Based on Cohort and Registry Data ⋮ Likelihood function for estimating parameters in multistate disease process with Laplace-transformation-based transition probabilities
Uses Software
Cites Work
- Flexible Nonhomogeneous Markov Models for Panel Observed Data
- Computation of the asymptotic null distribution of goodness-of-fit tests for multi-state models
- Event dependent sampling of recurrent events
- Survival and event history analysis. A process point of view
- Design of panel studies for disease progression with multiple stages
- Validity of the Aalen-Johansen estimators of stage occupation probabilities and Nelson-Aalen estimators of integrated transition hazards for non-Markov models
- The statistical analysis of recurrent events.
- Semi-Markov Models with Phase-Type Sojourn Distributions
- Covariate Adjustment of Event Histories Estimated from Markov Chains: The Additive Approach
- Estimation of Integrated Transition Hazards and Stage Occupation Probabilities for Non-Markov Systems Under Dependent Censoring
- Estimating time-to-event from longitudinal ordinal data using random-effects Markov models: application to multiple sclerosis progression
- The Analysis of Panel Data Under a Markov Assumption
- Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later
- Fitting Semi‐Markov Models to Interval‐Censored Data with Unknown Initiation Times
- Analysis of Longitudinal Data with Irregular, Outcome-Dependent Follow-Up
- Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination: Application to Skeletal Complications in Cancer Metastatic to Bone
- Multi-state models for bone marrow transplantation studies
- Regression Analysis for Multistate Models Based on a Pseudo‐value Approach, with Applications to Bone Marrow Transplantation Studies
- Maximum Likelihood Estimation of Misspecified Models
- Statistical models based on counting processes
This page was built for publication: Estimation and assessment of Markov multistate models with intermittent observations on individuals
