scholarly journals Mstatecox: A Package for Simulating Transition Probabilities from Semiparametric Multistate Survival Models

2018 ◽  
Vol 18 (3) ◽  
pp. 533-563
Author(s):  
Shawna K. Metzger ◽  
Benjamin T. Jones
Keyword(s):  
Marrow Transplantation ◽  
Transition Probabilities ◽  
Duration Models ◽  
Survival Models ◽  
Duration Model ◽  
Multistate Models ◽  
Statistical Software ◽  
Blood And Marrow Transplantation ◽  
The Common ◽  
European Registry

Multistate duration models are a valuable tool used in multiple fields to examine how subjects move through a series of discrete phases and stages. The models themselves may be fit using common statistical software, but their broader adoption has been limited because of a lack of software to substantively interpret their results. Transition probabilities are the common postestimation quantity for interpreting multistate duration model results. De Wreede, Fiocco, and Putter's (2011, Journal of Statistical Software 38(7): 1–30) mstate package provides R with the functionality to estimate these quantities from semiparametric multistate models, yet no Stata equivalent exists for semiparametric models. We introduce a new set of Stata commands to meet this need. Our mstatecox suite calculates transition probabilities from semiparametric multistate duration models with simulation. It can accommodate any configuration of stages and also has the ability to accommodate time-interacted covariates. We demonstrate our package's functionality using de Wreede, Fiocco, and Putter‘s European Registry of Blood and Marrow Transplantation example dataset.

Surviving Phases: Introducing Multistate Survival Models

2016 ◽  
Vol 24 (4) ◽  
pp. 457-477 ◽  
Author(s):  
Shawna K. Metzger ◽  
Benjamin T. Jones
Keyword(s):  
Transition Probabilities ◽  
Survival Model ◽  
Survival Models ◽  
Multistate Models ◽  
Multiple Forms ◽  
Political Processes ◽  
Covariate Effects ◽  
Causal Complexity ◽  
Over Time

Many political processes consist of a series of theoretically meaningful transitions across discrete phases that occur through time. Yet political scientists are often theoretically interested in studying not just individual transitions between phases, but also the duration that subjects spend within phases, as well as the effect of covariates on subjects’ trajectories through the process's multiple phases. We introduce the multistate survival model to political scientists, which is capable of modeling precisely this type of situation. The model is appealing because of its ability to accommodate multiple forms of causal complexity that unfold over time. In particular, we highlight three attractive features of multistate models: transition-specific baseline hazards, transition-specific covariate effects, and the ability to estimate transition probabilities. We provide two applications to illustrate these features.

A review of multistate modelling approaches in monitoring disease progression: Bayesian estimation using the Kolmogorov-Chapman forward equations

2021 ◽  
pp. 096228022199750
Author(s):  
Zvifadzo Matsena Zingoni ◽  
Tobias F Chirwa ◽  
Jim Todd ◽  
Eustasius Musenge
Keyword(s):  
Bayesian Estimation ◽  
Transition Probabilities ◽  
Markov Property ◽  
Likelihood Estimation ◽  
Single Individual ◽  
Transition Rates ◽  
Multistate Models ◽  
Time To Event ◽  
Modeling Framework ◽  
Forward Equations

There are numerous fields of science in which multistate models are used, including biomedical research and health economics. In biomedical studies, these stochastic continuous-time models are used to describe the time-to-event life history of an individual through a flexible framework for longitudinal data. The multistate framework can describe more than one possible time-to-event outcome for a single individual. The standard estimation quantities in multistate models are transition probabilities and transition rates which can be mapped through the Kolmogorov-Chapman forward equations from the Bayesian estimation perspective. Most multistate models assume the Markov property and time homogeneity; however, if these assumptions are violated, an extension to non-Markovian and time-varying transition rates is possible. This manuscript extends reviews in various types of multistate models, assumptions, methods of estimation and data features compatible with fitting multistate models. We highlight the contrast between the frequentist (maximum likelihood estimation) and the Bayesian estimation approaches in the multistate modeling framework and point out where the latter is advantageous. A partially observed and aggregated dataset from the Zimbabwe national ART program was used to illustrate the use of Kolmogorov-Chapman forward equations. The transition rates from a three-stage reversible multistate model based on viral load measurements in WinBUGS were reported.

scholarly journals Plegia to walking: AHSCBMT in severe NMOSD relapse

2020 ◽  
Vol 2 (2) ◽  
pp. e000073
Author(s):  
Nicola Carlisle ◽  
Parameswaran Hari ◽  
Staley Brod
Keyword(s):  
Bone Marrow ◽  
Bone Marrow Transplantation ◽  
Optic Neuritis ◽  
Marrow Transplantation ◽  
Transverse Myelitis ◽  
Working Party ◽  
Autoimmune Disorder ◽  
Haematopoietic Stem Cell ◽  
Partial Recovery ◽  
Blood And Marrow Transplantation

ObjectivesNeuromyelitis optica is a devastating, relapsing, inflammatory, autoimmune disorder characterised in large part by attacks of optic neuritis and transverse myelitis causing blindness and plegia in many patients. Eighty-three per cent of patients with transverse myelitic attacks and 67% of patients with optic neuritis attacks have no or a partial recovery.MethodsResults from The European Group for Blood and Marrow Transplantation Autoimmune Diseases Working Party imply failure of autologous haematopoietic stem cell bone marrow transplantation.Results and conclusionWe present a case that despite eventual relapse, made a remarkable functional recovery after bone marrow transplantation which may justify bone marrow transplantation in severe cases.

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