Inference of Transition Probabilities in Multi-State Models Using Adaptive Inverse Probability Censoring Weighting Technique

2020 ◽  
pp. 449-481
Author(s):  
Ying Zhang ◽  
Mei-Jie Zhang
Keyword(s):  
Transition Probabilities ◽  
Inverse Probability ◽  
State Models ◽  
Weighting Technique

scholarly journals THE IMPORTANCE OF MAINTAINING PHYSICAL ACTIVITY FOR TRANSITIONS BETWEEN COGNITIVE STATES: A COORDINATED ANALYSIS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S911-S911
Author(s):  
Tomiko Yoneda ◽  
Jonathan Rush ◽  
Nathan A Lewis ◽  
Jamie E Knight ◽  
Jinshil Hyun ◽  
...  
Keyword(s):  
Physical Activity ◽  
Life Expectancy ◽  
Transition Probabilities ◽  
Cognitive Changes ◽  
Total N ◽  
Older Adulthood ◽  
Cognitive States ◽  
Total Life Expectancy ◽  
State Models ◽  
The Impact

Abstract Although existing research shows that physical activity (PA) protects against cognitive decline, it is unclear if maintenance of PA throughout older adulthood influences the timing of onset or transitions through cognitive states. Further understanding of modifiable lifestyle factors that protect against cognitive changes characteristic of both normal aging and pathological aging, such as Alzheimer’s disease and other dementias, is imperative. Data were drawn from fourteen longitudinal studies of aging from Europe and America (total N=53,069). Controlling for demographics and chronic conditions, multi-state models were independently fit between datasets to investigate the impact of PA (computed based on Metabolic Equivalent of Task Method) on the likelihood of transitioning through three cognitive states, while also accounting for death as a competing risk factor. Random effects meta-analysis of transition probabilities indicated that more PA was associated with a reduced risk of transitioning from normal cognition to mildly impaired cognition (HR=0.90, CI’s=0.84, 0.97, p=0.007) and death (HR=0.24, CI’s=0.06, 0.92, p=0.04), as well as an increased likelihood of transitioning from severe impairment back to mild impairment (HR=1.09, CI’s=1.01, 1.17, p=0.03). Engagement in national minimum recommendations for PA (~150 minutes/week) increased total life expectancy for 70 year old males and females by 4.08 and 5.47 years, respectively. These results suggest that engaging in at least 150 minutes of physical activity per week in older adulthood contributes to delays in onset of mild cognitive impairment, substantially increases life expectancy, and may also diminish the symptoms that contribute to poor cognitive performance at the severely impaired stage.

scholarly journals A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models

2021 ◽  
Author(s):  
Niklas Maltzahn ◽  
Rune Hoff ◽  
Odd O. Aalen ◽  
Ingrid S. Mehlum ◽  
Hein Putter ◽  
...  
Keyword(s):  
Statistical Power ◽  
Transition Probabilities ◽  
Real Data ◽  
Testing Procedure ◽  
Data Application ◽  
Stratification Method ◽  
Model Complex ◽  
Markov Assumption ◽  
State Models ◽  
Disability Education

AbstractMulti-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumption can often be unrealistic. The Markov assumption is seldomly checked and violations can lead to biased estimation of many parameters of interest. This is a well known problem for the standard Aalen-Johansen estimator of transition probabilities and several alternative estimators, not relying on the Markov assumption, have been suggested. A particularly simple approach known as landmarking have resulted in the Landmark-Aalen-Johansen estimator. Since landmarking is a stratification method a disadvantage of landmarking is data reduction, leading to a loss of power. This is problematic for “less traveled” transitions, and undesirable when such transitions indeed exhibit Markov behaviour. Introducing the concept of partially non-Markov multi-state models, we suggest a hybrid landmark Aalen-Johansen estimator for transition probabilities. We also show how non-Markov transitions can be identified using a testing procedure. The proposed estimator is a compromise between regular Aalen-Johansen and landmark estimation, using transition specific landmarking, and can drastically improve statistical power. We show that the proposed estimator is consistent, but that the traditional variance estimator can underestimate the variance of both the hybrid and landmark estimator. Bootstrapping is therefore recommended. The methods are compared in a simulation study and in a real data application using registry data to model individual transitions for a birth cohort of 184 951 Norwegian men between states of sick leave, disability, education, work and unemployment.

scholarly journals Inactive status is an independent predictor of liver transplant waitlist mortality and is associated with a transplant centers median meld at transplant

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260000
Author(s):  
Jonathan Merola ◽  
Geliang Gan ◽  
Darren Stewart ◽  
Samantha Noreen ◽  
David Mulligan ◽  
...  
Keyword(s):  
Liver Transplant ◽  
Transition Probabilities ◽  
Deceased Donor ◽  
Meld Score ◽  
Status Change ◽  
Transplant Center ◽  
Service Areas ◽  
State Models ◽  
Active Transition ◽  
Waitlist Mortality

Background Approximately 30% of patients on the liver transplant waitlist experience at least one inactive status change which makes them temporarily ineligible to receive a deceased donor transplant. We hypothesized that inactive status would be associated with higher mortality which may differ on a transplant centers’ or donor service areas’ (DSA) Median MELD at Transplant (MMaT). Methods Multi-state models were constructed (OPTN database;06/18/2013-06/08/2018) using DSA-level and transplant center-level data where MMaT were numerically ranked and categorized into tertiles. Hazards ratios were calculated between DSA and transplant center tertiles, stratified by MELD score, to determine differences in inactive to active transition probabilities. Results 7,625 (30.2% of sample registrants;25,216 total) experienced at least one inactive status change in the DSA-level cohort and 7,623 experienced at least one inactive status change in the transplant-center level cohort (30.2% of sample registrants;25,211 total). Inactive patients with MELD≤34 had a higher probability of becoming re-activated if they were waitlisted in a low or medium MMaT transplant center or DSA. Transplant rates were higher and lower re-activation probability was associated with higher mortality for the MELD 26–34 group in the high MMaT tertile. There were no significant differences in re-activation, transplant probability, or waitlist mortality for inactivated patients with MELD≥35 regardless of a DSA’s or center’s MMaT. Conclusion This study shows that an inactive status change is independently associated with waitlist mortality. This association differs by a centers’ and a DSAs’ MMaT. Prioritization through care coordination to resolve issues of inactivity is fundamental to improving access.

scholarly journals First-principles interpretation of electron transport though single-molecule junctions using molecular dynamics of electron attached states

2021 ◽  
Author(s):  
Dávid P. Jelenfi ◽  
Attila Tajti ◽  
Péter G. Szalay
Keyword(s):  
Molecular Dynamics ◽  
Electron Transport ◽  
Single Molecule ◽  
Transition Probabilities ◽  
Normal Mode Analysis ◽  
Mode Analysis ◽  
Bending Vibrations ◽  
Time Resolved ◽  
State Models

The electron transport through the single-molecule junction of 1,4-Diaminobenzene (BDA) is modeled using ab initio quantum-classical molecular dynamics of electron attached states. Observations on the nature of the process are made by time-resolved analysis of energy differences, non-adiabatic transition probabilities and the spatial distribution of the excess electron. The role of molecular vibrations that facilitate the transport by being responsible for the periodic behaviour of these quantities is shown using normal mode analysis. The results support a mechanism involving the electron's direct hopping between the electrodes, without its presence on the molecule, with the prime importance of the bending vibrations that periodically alter the molecule{electrode interactions. No relevant differences are found between results provided by the ADC(2) and SOS-ADC(2) excited state models. Our approach provides an alternative insight into the role of nuclear motions in the electron transport process, one which is more expressive from the chemical perspective.

scholarly journals TPmsm: Estimation of the Transition Probabilities in 3-State Models

2014 ◽  
Vol 62 (4) ◽  
Author(s):  
Artur Araújo ◽  
Luís Meira-Machado ◽  
Javier Roca-Pardiñas
Keyword(s):  
Transition Probabilities ◽  
State Models

scholarly journals Landmark estimation of transition probabilities in non-Markov multi-state models with covariates

2019 ◽  
Vol 25 (4) ◽  
pp. 660-680
Author(s):  
Rune Hoff ◽  
Hein Putter ◽  
Ingrid Sivesind Mehlum ◽  
Jon Michael Gran
Keyword(s):  
Transition Probabilities ◽  
State Models

scholarly journals Estimation of the Transition Probabilities in Multi-state Survival Data: New Developments and Practical Recommendations

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Survival Data ◽  
Transition Probabilities ◽  
Real Data ◽  
Data Set ◽  
Event History Data ◽  
Markov Assumption ◽  
Product Limit ◽  
Local Test ◽  
State Models ◽  
Practical Recommendations

Multi-state models can be successfully used for describing complicated event history data, for example, describing stages in the disease progression of a patient. In these models one important goal is the estimation of the transition probabilities since they allow for long term prediction of the process. Traditionally these quantities have been estimated by the Aalen-Johansen estimator which is consistent if the process is Markovian. Recently, estimators have been proposed that outperform the Aalen-Johansen estimators in non-Markov situations. This paper considers a new proposal for the estimation of the transition probabilities in a multi-state system that is not necessarily Markovian. The proposed product-limit nonparametric estimator is defined in the form of a counting process, counting the number of transitions between states and the risk sets for leaving each state with an inverse probability of censoring weighted form. Advantages and limitations of the different methods and some practical recommendations are presented. We also introduce a graphical local test for the Markov assumption. Several simulation studies were conducted under different data scenarios. The proposed methods are illustrated with a real data set on colon cancer.

scholarly journals Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus

2021 ◽  
Author(s):  
Nikolaos Skourlis ◽  
Michael J. Crowther ◽  
Therese M-L. Andersson ◽  
Paul C. Lambert
Keyword(s):  
Complex Disease ◽  
Transition Probabilities ◽  
State Model ◽  
Web Tool ◽  
Transition Intensity ◽  
Web Tools ◽  
State Models ◽  
Disease Pathways ◽  
Over Time ◽  
Meaningful Communication

Abstract Background: Multi-state models are used in complex disease pathways to describe a process where an individual moves from one state to the next, taking into account competing states during each transition. In a multi-state setting, there are various measures to be estimated that are of great epidemiological importance. However, increased complexity of the multi-state setting and predictions over time for individuals with different covariate patterns may lead to increased difficulty in communicating the estimated measures. The need for easy and meaningful communication of the analysis results motivated the development of a web tool to address these issues. Results: MSMplus is a publicly available web tool, developed in RShiny, with the aim of enhancing the understanding of multi-state model analyses results. The results from any multi-state model analysis are uploaded to the application in a pre-specified format. Through a variety of user-tailored interactive graphs, the application contributes to an improvement in communication, reporting and interpretation of multi-state analysis results as well as comparison between different approaches. The predicted measures that can be supported by MSMplus include, among others, the transition probabilities, the transition intensity rates, the length of stay in each state, the probability of ever visiting a state and user defined measures. Representation of differences, ratios and confidence intervals of the aforementioned measures are also supported. MSMplus is a useful tool that enhances communication and understanding of multi-state model analyses results. Conclusions: Further use and development of web tools should be encouraged in the future as a means to communicate scientific research.

scholarly journals Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nikolaos Skourlis ◽  
Michael J. Crowther ◽  
Therese M-L. Andersson ◽  
Paul C. Lambert
Keyword(s):  
Complex Disease ◽  
Transition Probabilities ◽  
R Package ◽  
State Model ◽  
Web Tool ◽  
Transition Intensity ◽  
Web Tools ◽  
State Models ◽  
Disease Pathways ◽  
Meaningful Communication

Abstract Background Multi-state models are used in complex disease pathways to describe a process where an individual moves from one state to the next, taking into account competing states during each transition. In a multi-state setting, there are various measures to be estimated that are of great epidemiological importance. However, increased complexity of the multi-state setting and predictions over time for individuals with different covariate patterns may lead to increased difficulty in communicating the estimated measures. The need for easy and meaningful communication of the analysis results motivated the development of a web tool to address these issues. Results MSMplus is a publicly available web tool, developed via the Shiny R package, with the aim of enhancing the understanding of multi-state model analyses results. The results from any multi-state model analysis are uploaded to the application in a pre-specified format. Through a variety of user-tailored interactive graphs, the application contributes to an improvement in communication, reporting and interpretation of multi-state analysis results as well as comparison between different approaches. The predicted measures that can be supported by MSMplus include, among others, the transition probabilities, the transition intensity rates, the length of stay in each state, the probability of ever visiting a state and user defined measures. Representation of differences, ratios and confidence intervals of the aforementioned measures are also supported. MSMplus is a useful tool that enhances communication and understanding of multi-state model analyses results. Conclusions Further use and development of web tools should be encouraged in the future as a means to communicate scientific research.

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