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 Role of depressive symptoms in cardiometabolic diseases and subsequent transitions to all-cause mortality: an application of multistate models in a prospective cohort study

2021 ◽  
pp. svn-2020-000693
Author(s):  
Yanan Qiao ◽  
Siyuan Liu ◽  
Guochen Li ◽  
Yanqiang Lu ◽  
Ying Wu ◽  
...  
Keyword(s):  
Heart Disease ◽  
Depressive Symptoms ◽  
Depression Scale ◽  
European Population ◽  
Multistate Models ◽  
Cardiometabolic Diseases ◽  
Increased Risk ◽  
All Cause Mortality ◽  
Patients With Diabetes

Background and purposeThe role of depression in the development and outcome of cardiometabolic diseases remains to be clarified. We aimed to examine the extent to which depressive symptoms affect the transitions from healthy to diabetes, stroke, heart disease and subsequent all-cause mortality in a middle-aged and elderly European population.MethodsA total of 78 212 individuals aged ≥50 years from the Survey of Health Ageing and Retirement in Europe were included. Participants with any baseline cardiometabolic diseases including diabetes, stroke and heart disease were excluded. Depressive symptoms were measured by the Euro-Depression scale at baseline. Participants were followed up to determine the occurrence of cardiometabolic diseases and all-cause mortality. We used multistate models to estimate the transition-specific HRs and 95% CIs after adjustment of confounders.ResultsDuring 500 711 person-years of follow-up, 4742 participants developed diabetes, 2173 had stroke, 5487 developed heart disease and 7182 died. Depressive symptoms were significantly associated with transitions from healthy to diabetes (HR: 1.12, 95% CI: 1.05 to 1.20), stroke (HR: 1.31, 95% CI: 1.18 to 1.44), heart disease (HR: 1.26, 95% CI: 1.18 to 1.34) and all-cause mortality (HR: 1.41, 95% CI: 1.34 to 1.49). After cardiometabolic diseases, depressive symptoms were associated with the increased risk of all-cause mortality in patients with diabetes (HR: 1.54, 95% CI: 1.25 to 1.89), patients who had stroke (HR: 1.29, 95% CI: 1.03 to 1.61) and patients with heart disease (HR: 1.21, 95% CI: 1.02 to 1.44).ConclusionsDepressive symptoms increase the risk of diabetes, stroke and heart disease, and affect the risk of mortality after the onset of these cardiometabolic conditions. Screening and treatment of depressive symptoms may have profound implications for the prevention and prognosis of cardiometabolic diseases.

scholarly journals Multistate models of developmental toxicity: Application to valproic acid-induced malformations in the zebrafish embryo

2021 ◽  
Vol 414 ◽  
pp. 115424
Author(s):  
Ségolène Siméon ◽  
Rémy Beaudouin ◽  
Katharina Brotzmann ◽  
Thomas Braunbeck ◽  
Frédéric Y. Bois
Keyword(s):  
Valproic Acid ◽  
Zebrafish Embryo ◽  
Developmental Toxicity ◽  
Multistate Models

Coupling acoustic tracking with conventional tag returns to estimate mortality for a coastal flatfish with high rates of emigration

2020 ◽  
Vol 77 (1) ◽  
pp. 1-22
Author(s):  
Trevor K. Scheffel ◽  
Joseph E. Hightower ◽  
Jeffrey A. Buckel ◽  
Jacob R. Krause ◽  
Frederick S. Scharf
Keyword(s):  
Spatial Scales ◽  
Age Groups ◽  
River Estuary ◽  
Natural Mortality ◽  
Multistate Models ◽  
Fishery Resource ◽  
Paralichthys Lethostigma ◽  
Movement Rates ◽  
Southern Flounder ◽  
Behavioral Attributes

The addition of acoustic telemetry to conventional tagging studies can generate direct estimates of mortality and movement rates to inform fisheries management. We applied a combined telemetry and tag-return design to southern flounder (Paralichthys lethostigma), a coastal flatfish that demonstrates limited movements within estuarine habitats coupled with extensive ontogenetic migrations that present unique challenges for estimating mortality rates. The fates of acoustically and conventionally tagged fish were followed during 2014–2016 to estimate annual rates of fishing mortality (F), natural mortality (M), and estuarine emigration (E). Multistate models estimated southern flounder annual F for each of the 3 years at two spatial scales (New River estuary F = 0.49–1.61; North Carolina coast F = 0.36–0.72). Annual rates of emigration were high (E = 1.06–1.67), and direct estimation of this source of loss considerably improved mortality estimates. The model estimated natural mortality as a constant annual rate (M = 0.84), which was similar in magnitude to life-history-based estimates for similar age groups. By accounting for unique behavioral attributes in the study design, the application of multistate tagging models provided robust estimates of mortality and emigration rates for a valuable coastal fishery resource that will inform future efforts to achieve yield and conservation goals.

A global test for competing risks survival analysis

2020 ◽  
Vol 29 (12) ◽  
pp. 3666-3683
Author(s):  
Dominic Edelmann ◽  
Maral Saadati ◽  
Hein Putter ◽  
Jelle Goeman
Keyword(s):  
Survival Analysis ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Competing Risks ◽  
Cox Model ◽  
Wald Test ◽  
Ratio Test ◽  
Type I ◽  
Multistate Models ◽  
Global Test

Standard tests for the Cox model, such as the likelihood ratio test or the Wald test, do not perform well in situations, where the number of covariates is substantially higher than the number of observed events. This issue is perpetuated in competing risks settings, where the number of observed occurrences for each event type is usually rather small. Yet, appropriate testing methodology for competing risks survival analysis with few events per variable is missing. In this article, we show how to extend the global test for survival by Goeman et al. to competing risks and multistate models[Per journal style, abstracts should not have reference citations. Therefore, can you kindly delete this reference citation.]. Conducting detailed simulation studies, we show that both for type I error control and for power, the novel test outperforms the likelihood ratio test and the Wald test based on the cause-specific hazards model in settings where the number of events is small compared to the number of covariates. The benefit of the global tests for competing risks survival analysis and multistate models is further demonstrated in real data examples of cancer patients from the European Society for Blood and Marrow Transplantation.

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