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.

scholarly journals Developmental State and Ethnic Federalism in Ethiopia:

2019 ◽  
Vol 3 (1) ◽  
pp. 95-127
Author(s):  
Zekarias Beshah Abebe
Keyword(s):  
Cultural Diversity ◽  
Economic Restructuring ◽  
Developmental State ◽  
The Other ◽  
State Model ◽  
Ethnic Federalism ◽  
State Models ◽  
Liberal Approach ◽  
The Right

The ethnic federalization of the post-1991 Ethiopia and the subsequent adoption of developmental state paradigm are the two most important pillars for the country’s political and economic restructuring. An interventionist developmental state model is opted for against the dominant narrative of the non-interventionist neo-liberal approach as the right path to conquer poverty: a source of national humiliation. On the other hand, ethnically federated Ethiopia is considered as an antidote to the historical pervasive mismanagement of the ethno-linguistic and cultural diversity of the polity. The presence of these seemingly paradoxical state models in Ethiopia makes it a captivating case study for analysis. Ethiopia’s experiment of pursuing a developmental state in a decentralized form of governance not only deviates from the prevalent pattern but also is perceived to be inherently incompatible due to the competing approaches that characterize the two systems. This article argues that the way in which the developmental state is being practiced in Ethiopia is eroding the values and the very purposes of ethnic federalism. Its centralized, elitist and authoritarian nature, which are the hallmark of the Ethiopian developmental state, defeats the positive strides that ethnic federalism aspires to achieve, thereby causing discontent and disenfranchisement among a swathe of the society. The article posits that the developmental state can and should be reinvented in a manner that goes in harmony with the ideals of ethnic federalism. The notion of process-based leadership remains one way of reinventing the Ethiopian developmental state model.  

scholarly journals Forecasting the Long-Run Behavior of the Stock Price of Some Selected Companies in the Malaysian Construction Sector: A Markov Chain Approach

2020 ◽  
Vol 5 (2) ◽  
pp. 296-308
Author(s):  
Wajeeh Mustafa Sarsour ◽  
Shamsul Rijal Muhammad Sabri
Keyword(s):  
Markov Model ◽  
Stock Prices ◽  
Stock Price ◽  
Transition Probabilities ◽  
Return Time ◽  
Price Increase ◽  
State Model ◽  
Construction Sector ◽  
Long Run ◽  
Three State Model

The fluctuations in stock prices produce a high risk that makes investors uncertain about their investment decisions. The present paper provides a methodology to forecast the long-term behavior of five randomly selected equities operating in the Malaysian construction sector. The method used in this study involves Markov chains as a stochastic analysis, assuming that the price changes have the proparty of Markov dependency with their transition probabilities. We identified a three-state Markov model (i.e., increase, stable, fall) and a two-state Markov model (i.e., increase and fall). The findings suggested that the chains had limiting distributions. The mean return time was computed for respective equities as well as to determine the average duration to return to a stock price increase. The analysis might aid investors in improving their investment knowledge, and they will be able to make better decisions when an equity portfolio possesses higher transition probabilities, higher limiting distribution, and lowest mean return time in response to a price increase. Finally, our investigations suggest that investors are more likely to invest in the GKent based on the three-state model, while VIZIONE seems to be a better investment choice based on a two-state model.

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 Microsimulation Modeling in Oncology

2018 ◽  
pp. 1-11 ◽  
Author(s):  
Çağlar Çağlayan ◽  
Hiromi Terawaki ◽  
Qiushi Chen ◽  
Ashish Rai ◽  
Turgay Ayer ◽  
...  
Keyword(s):  
Transition Probabilities ◽  
Microsimulation Model ◽  
Alternative Interventions ◽  
Oncology Research ◽  
Optimal Intervention ◽  
Potential Applications ◽  
History Of ◽  
Microsimulation Modeling ◽  
Microsimulation Models ◽  
Over Time

Purpose Microsimulation is a modeling technique that uses a sample size of individual units (microunits), each with a unique set of attributes, and allows for the simulation of downstream events on the basis of predefined states and transition probabilities between those states over time. In this article, we describe the history of the role of microsimulation in medicine and its potential applications in oncology as useful tools for population risk stratification and treatment strategy design for precision medicine. Methods We conducted a comprehensive and methodical search of the literature using electronic databases—Medline, Embase, and Cochrane—for works published between 1985 and 2016. A medical subject heading search strategy was constructed for Medline searches by using a combination of relevant search terms, such as “microsimulation model medicine,” “multistate modeling cancer,” and “oncology.” Results Microsimulation modeling is particularly useful for the study of optimal intervention strategies when randomized control trials may not be feasible, ethical, or practical. Microsimulation models can retain memory of prior behaviors and states. As such, it allows an explicit representation and understanding of how various processes propagate over time and affect the final outcomes for an individual or in a population. Conclusion A well-calibrated microsimulation model can be used to predict the outcome of the event of interest for a new individual or subpopulations, assess the effectiveness and cost effectiveness of alternative interventions, and project the future disease burden of oncologic diseases. In the growing field of oncology research, a microsimulation model can serve as a valuable tool among the various facets of methodology available.

scholarly journals Calculation of LTC Premiums Based on Direct Estimates of Transition Probabilities

2005 ◽  
Vol 35 (2) ◽  
pp. 455-469 ◽  
Author(s):  
Florian Helms ◽  
Claudia Czado ◽  
Susanne Gschlößl
Keyword(s):  
Life History ◽  
Generalized Estimating Equations ◽  
Transition Matrix ◽  
Transition Probabilities ◽  
State Model ◽  
Link Function ◽  
History Of ◽  
The Relationship ◽  
Pseudo Values ◽  
Almost Unbiased

In this paper we model the life-history of LTC-patients using a Markovian multi-state model in order to calculate premiums for a given LTC-plan. Instead of estimating the transition intensities in this model we use the approach suggested by Andersen et al. (2003) for a direct estimation of the transition probabilities. Based on the Aalen-Johansen estimator, an almost unbiased estimator for the transition matrix of a Markovian multi-state model, we calculate so-called pseudo-values, known from Jackknife methods. Further, we assume that the relationship between these pseudo-values and the covariates of our data are given by a GLM with the logit as link-function. Since the GLMs do not allow for correlation between successive observations we use instead the “Generalized Estimating Equations” (GEEs) to estimate the parameters of our regression model. The approach is illustrated using a representative sample from a German LTC portfolio.

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 Antibiotic Exposures and Dietary Intakes Are Associated with Changes in Microbiota Compositions in Allogeneic Hematopoietic Stem Cell Transplant Patients

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 597-597
Author(s):  
Chi L. Nguyen ◽  
Antonio L.C. Gomes ◽  
Jonathan U. Peled ◽  
John B. Slingerland ◽  
Ann E. Slingerland ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Protein Intake ◽  
Research Funding ◽  
Transition Probabilities ◽  
Fat Intake ◽  
Dietary Intakes ◽  
Advisory Committees ◽  
Hematopoietic Stem ◽  
High Diversity ◽  
Over Time

The intestinal microbiota undergoes major perturbations during allogeneic hematopoietic stem cell transplantation (allo-HCT), and low microbiota diversity during this period is associated with an increased risk of graft-versus-host disease and mortality. Identifying the environmental variables that might impact intestinal microbiota could inform strategies to maintain and restore a healthy microbiota state. However, understanding microbial dynamics is challenging due to the high-dimensional nature of microbiota data. Here, we simplified complex microbiota communities into clusters and investigated the dynamics under different conditions in terms of transition probabilities in a large dataset of allo-HCT fecal specimens (Fig. a). The bacterial compositions of 7,930 samples from 1,076 allo-HCT patients were determined by 16S rRNA deep-sequencing. Samples were then clustered into 10 distinct states by k-means clustering of a Bray-Curtis β-diversity matrix (Fig. b). These clusters captured variations in diversity and microbiota compositions (Fig. c-d). Cluster 1 represented a high-diversity state, and Lachnospiraceae and other Clostridiales were the most commonly observed taxa in this cluster. The low-diversity clusters 9 and 10 consisted mostly of Streptococcus-dominated and Enterococcus-dominated samples, respectively. We utilized a regression-based predictive approach to model cluster transition probabilities in terms of a weight for remaining in the same cluster over time (self-weight) and a weight for attracting transitions from other clusters over time (attractor-weight). Controlling for the effect of time, the weights measured the contribution of different environmental exposures to intestinal microbial behaviors. A negative parameter coefficient indicates cluster destabilization or decreased cluster transition likelihood in the case of self-weights and attractor-weights, respectively. We evaluated the impact of the 3 most commonly used non-prophylactic antibacterial drugs using 2359 daily samples from 385 allo-HCT patients collected between day -14 to 7 relative to transplant. High-diversity cluster 1 was significantly destabilized by piperacillin-tazobactam (pip-tazo) exposure (β = -0.87, P < 0.05). Meanwhile, exposure to cefepime and meropenem did not have a significant effect on cluster 1 stability (Fig. e). Exposure to pip-tazo also increased the transition probability to the Streptococcus-dominated cluster 9 (β = 1.83, P < 0.001), while cefepime (β = 2.69, P < 0.05) and meropenem (β = 1.96, P < 0.01) exposure favored transitions to the Enterococcus-dominant cluster 10. These results suggest that antibiotic exposures are associated with different composition outcomes depending on patient microbiota states during transplant period. In a small subset of 242 daily samples from 46 allo-HCT patients with detailed daily dietary information, we observed that an increase in total protein intake (range = 0-137.4g; median = 36g) was associated with low self-maintenance of cluster 1 (β = -1.29, P < 0.05), while an increase in total fat intake (range = 0-183.3g; median = 34.5g) improved cluster 1 stability (β = 1.44, P < 0.05). Overall, dietary intakes could also modulate transition probabilities between microbial communities in allo-HCT patients. While prior studies have assessed specific bacterial taxa or diversity indices as biomarkers of clinical outcomes, here we considered the entire intestinal communities and demonstrated that various environmental exposures were associated with changes in microbiota composition during allo-HCT. Using a regression-based approach that predicts cluster transitions in response to environmental conditions, we found that pip-tazo exposure was associated with destabilization of a high-diversity state and increased transitions to a Streptococcus-dominated state, while cefepime and meropenem exposure did not disrupt high-diversity microbial community. Furthermore, increased protein intake was also associated with disruption to the high-diversity cluster, while increased fat intake strengthened the maintenance of a diverse and healthy microbial community. Ultimately, this computation framework aims to inform strategies to optimize treatment plans for allo-HCT patients to maximize a healthy gut microbiota state and clinical outcomes. Disclosures Gomes: Seres Therapeutics: Other: Part of Salary. Peled:Seres Therapeutics: Research Funding. Slingerland:Seres Therapeutics: Other: Salary supported by Seres funding. Clurman:Seres Therapeutics: Research Funding. Giralt:Celgene: Consultancy, Research Funding; Takeda: Consultancy; Sanofi: Consultancy, Research Funding; Amgen: Consultancy, Research Funding. Perales:Bristol-Meyers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bellicum: Honoraria, Membership on an entity's Board of Directors or advisory committees; NexImmune: Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Nektar Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Omeros: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck: Consultancy, Honoraria; Medigene: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees; Kyte/Gilead: Research Funding; Miltenyi: Research Funding; MolMed: Membership on an entity's Board of Directors or advisory committees. Pamer:MedImmune: Honoraria; Seres Therapeutics: Honoraria, Patents & Royalties; Bristol Myers Squibb: Honoraria; Novartis: Honoraria; Celgene: Honoraria; Ferring Pharmaceuticals: Honoraria. van den Brink:Acute Leukemia Forum (ALF): Consultancy, Honoraria; Seres Therapeutics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Flagship Ventures: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Evelo: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Honoraria; Therakos: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Merck & Co, Inc.: Consultancy, Honoraria; Juno Therapeutics: Other: Licensing; Magenta and DKMS Medical Council: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Stakeholder participation and the use of web technology for MPA management

2013 ◽  
Vol 4 (2) ◽  
pp. 260 ◽  
Author(s):  
Markantonatou Vasiliki ◽  
Manuel Meidinger ◽  
Marcello Sano ◽  
Eleni Oikonomou ◽  
Giuseppe Di Carlo ◽  
...  
Keyword(s):  
Marine Protected Areas ◽  
Stakeholder Participation ◽  
Web Technology ◽  
Web Tool ◽  
Community Based ◽  
Web Tools ◽  
New Information ◽  
Wide Range ◽  
High Quality Information ◽  
Stakeholder Preferences

Stakeholder participation has received increased attention as a key process for enhancing mitigation of conflicts between different interests for the same resources and transparent decision-making in marine protected areas (MPAs). A wide range of advanced web tools is available nowadays that integrate stakeholder participation by generating new information and allow interaction between actors in MPA management. However, such technologies are frequently used without much consideration regarding the complexity of the decision to be made and the heterogeneity of stakeholder preferences and understanding in order to be related to these technologies. In order to understand how technology corresponds to the changing needs of MPA management, we have reviewed a range of different participation strategies adopted by web technology, based on a set of criteria that define a successful participation approach. We start from simple towards more sophisticated tools that have been developed worldwide in order to better inform decisions, and contribute to more effective and efficient MPA management. Finally, we draw a theoretical framework for the development of a community-based web tool with the capacity to incorporate the philosophy of stakeholder participation by generating new and high quality information flow for effective MPA management.

scholarly journals Stochastic imputation for integrated transcriptome association analysis of a longitudinally measured trait

2019 ◽  
Vol 29 (4) ◽  
pp. 1167-1180 ◽  
Author(s):  
Evan L Ray ◽  
Jing Qian ◽  
Regina Brecha ◽  
Muredach P Reilly ◽  
Andrea S Foulkes
Keyword(s):  
Association Analysis ◽  
Complex Disease ◽  
Estimation Error ◽  
Penalized Regression ◽  
Error Rates ◽  
Test Statistic ◽  
Modeling Framework ◽  
Two Stage ◽  
Regression Imputation ◽  
Over Time

The mechanistic pathways linking genetic polymorphisms and complex disease traits remain largely uncharacterized. At the same time, expansive new transcriptome data resources offer unprecedented opportunity to unravel the mechanistic underpinnings of complex disease associations. Two-stage strategies involving conditioning on a single, penalized regression imputation for transcriptome association analysis have been described for cross-sectional traits. In this manuscript, we propose an alternative two-stage approach based on stochastic regression imputation that additionally incorporates error in the predictive model. Application of a bootstrap procedure offers flexibility when a closed form predictive distribution is not available. The two-stage strategy is also generalized to longitudinally measured traits, using a linear mixed effects modeling framework and a composite test statistic to evaluate whether the genetic component of gene-level expression modifies the biomarker trajectory over time. Simulations studies are performed to evaluate relative performance with respect to type-1 error rates, coverage, estimation error, and power under a range of conditions. A case study is presented to investigate the association between whole blood expression for each of five inflammasome genes with inflammatory response over time after endotoxin challenge.

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