Academic literature on the topic 'Multistate survival models'

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.

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.

Medical researchers are often interested to investigate the relationship between explicative variables and times-to-events such as disease progression or death. Such multiple times-to-events can be studied using multistate models. For chronic diseases, it may be relevant to consider semi-Markov multistate models because the transition intensities between two clinical states more likely depend on the time already spent in the current state than on the chronological time. When the cause of death for a patient is unavailable or not totally attributable to the disease, it is not possible to specifically study the associations with the excess mortality related to the disease. Relative survival analysis allows an estimate of the net survival in the hypothetical situation where the disease would be the only possible cause of death. In this paper, we propose a semi-Markov additive relative survival (SMRS) model that combines the multistate and the relative survival approaches. The usefulness of the SMRS model is illustrated by two applications with data from a French cohort of kidney transplant recipients. Using simulated data, we also highlight the effectiveness of the SMRS model: the results tend to those obtained if the different causes of death are known.

We estimated the magnitude and shape of size-dependent survival (SDS) across multiple sampling intervals for two cohorts of stream-dwelling Atlantic salmon ( Salmo salar ) juveniles using multistate capture–mark–recapture (CMR) models. Simulations designed to test the effectiveness of multistate models for detecting SDS in our system indicated that error in SDS estimates was low and that both time-invariant and time-varying SDS could be detected with sample sizes of >250, average survival of >0.6, and average probability of capture of >0.6, except for cases of very strong SDS. In the field (N ∼750, survival 0.6–0.8 among sampling intervals, probability of capture 0.6–0.8 among sampling occasions), about one-third of the sampling intervals showed evidence of SDS, with poorer survival of larger fish during the age-2+ autumn and quadratic survival (opposite direction between cohorts) during age-1+ spring. The varying magnitude and shape of SDS among sampling intervals suggest a potential mechanism for the maintenance of the very wide observed size distributions. Estimating SDS using multistate CMR models appears complementary to established approaches, can provide estimates with low error, and can be used to detect intermittent SDS.

Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-09-23T13:03:28Z No. of bitstreams: 1 DissRSC.pdf: 1649931 bytes, checksum: c3449a4367ea7de9e327fa7dc9110861 (MD5)
Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-27T19:22:02Z (GMT) No. of bitstreams: 1 DissRSC.pdf: 1649931 bytes, checksum: c3449a4367ea7de9e327fa7dc9110861 (MD5)
Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-27T19:22:09Z (GMT) No. of bitstreams: 1 DissRSC.pdf: 1649931 bytes, checksum: c3449a4367ea7de9e327fa7dc9110861 (MD5)
Made available in DSpace on 2016-09-27T19:22:16Z (GMT). No. of bitstreams: 1 DissRSC.pdf: 1649931 bytes, checksum: c3449a4367ea7de9e327fa7dc9110861 (MD5) Previous issue date: 2016-03-31
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Often intermediate events provide more detailed information about the disease process or recovery, for example, and allow greater accuracy in predicting the prognosis of patients. Such non-fatal events during the course of the disease can be seen as transitions from one state to another. The basic idea of a multistate models is that the person moves through a series of states in continuous time, it is possible to estimate the transition probabilities and intensities between them and the effect of covariates associated with each transition. Many studies include the grouping of survival times, for example, in multi-center studies, and is also of interest to study the evolution of patients over time, characterizing grouped multistate data. Because the data coming from different centers/groups, the failure times these individuals are grouped and the common risk factors not observed, it is interesting to consider the use of frailty so that we can capture the heterogeneity between the groups at risk for different types of transition, in addition to considering the dependence structure between transitions of individuals of the same group. In this work we present the methodology of multistate models, frailty models and then the integration of models with multi-state fragility models, dealing with the process of parametric and semi-parametric estimation. The conducted simulation study showed the importance of considering frailty in grouped multistate models, because without considering them, the estimates become biased. Furthermore, we find the frequentist properties of estimators of multistate model with nested frailty. Finally, as an application example to a set of real data, we use the process of bone marrow transplantation recovery of patients in four hospitals.We did a comparison of models through quality teasures setting AIC and BIC, coming to the conclusion that the model considers two random effects (one for the hospital and another for interaction transition-hospital) fits the data better. In addition to considering the heterogeneity between hospitals, such a model also considers the heterogeneity between hospitals in each transition. Thus, the values of the frailty estimated interaction transition-hospital reveal how fragile patients from each hospital are to experience certain type of event/transition.
Frequentemente eventos intermediários fornecem informações mais detalhadas sobre o processo da doença ou recuperação, por exemplo, e permitem uma maior precisão na previsão do prognóstico de pacientes. Tais eventos não fatais durante o curso da doença podem ser vistos como transições de um estado para outro. A ideia básica dos modelos multiestado é que o indivíduo se move através de uma série de estados em tempo contínuo, sendo possível estimar as probabilidades e intensidades de transição entre eles e o efeito das coivaráveis associadas a cada transição. Muitos estudos incluem o agrupamento dos tempos de sobrevivência como, por exemplo, em estudos multicêntricos, e também é de interesse estudar a evolução dos pacientes ao longo do tempo, caracterizando assim dados multiestado agrupados. Devido ao fato de os dados virem de diferentes centros/grupos, os tempos de falha desses indivíduos estarem agrupados e a fatores de risco comuns não observados, é interessante considerar o uso de fragilidades para que possamos capturar a heterogeneidade entre os grupos no risco para os diferentes tipos de transição, além de considerar a estrutura de dependência entre transições dos indivíduos de um mesmo grupo. Neste trabalho apresentamos a metodologia dos modelos multiestado, dos modelos de fragilidade e, em seguida, a integração dos modelos multiestado com modelos de fragilidade, tratando do seu processo de estimação paramétrica e semiparamétrica. O estudo de simulação realizado mostrou a importância de considerarmos fragilidades em modelos multiestado agrupados, pois sem consider´a-las, as estimativas tornam-se viesadas. Al´em disso, verificamos as propriedades frequentistas dos estimadores do modelo multiestado com fragilidades aninhadas. Por fim, como um exemplo de aplicação a um conjunto de dados reais, utilizamos o processo de recuperação de transplante de medula óssea de pacientes tratados em quatro hospitais. Fizemos uma comparação de modelos por meio das medidas de qualidade do ajuste AIC e BIC, chegando `a conclusão de que o modelo que considera dois efeitos aleatórios (uma para o hospital e outro para a interação transição-hospital) ajusta-se melhor aos dados. Além de considerar a heterogeneidade entre os hospitais, tal modelo também considera a heterogeneidade entre os hospitais em cada transição. Sendo assim, os valores das fragilidades estimadas da interação transição-hospital revelam o quão frágeis os pacientes de cada hospital são para experimentarem determinado tipo de evento/transição.

I confronted empirical habitat data (1994-2004) and population data (1988-2005) with ecological theory on habitat dynamics, recruitment, survival, and dispersal to develop predictive relationships between landcover variation and population dynamics. I focus on Florida Scrub-Jays, although one chapter presents a model for the potential influence of habitat restoration on viability of the Florida Grasshopper Sparrow. Both species are unique to Florida landscapes that are dominated by shrubs and grasses and maintained by frequent fires. Both species are declining, even in protected areas, despite their protected status. I mapped habitat for both species using grid polygon cells to quantify population potential and habitat quality. A grid cell was the average territory size and the landcover unit in which habitat-specific recruitment and survival occurred. I measured habitat-specific recruitment and survival of Florida Scrub-Jays from 1988-2008. Data analyses included multistate analysis, which was developed for capture-recapture data but is useful for analyzing many ecological processes, such as habitat change. I relied on publications by other investigators for empirical Florida Grasshopper Sparrow data. The amount of potential habitat was greatly underestimated by landcover mapping not specific to Florida Scrub-Jays. Overlaying east central Florida with grid polygons was an efficient method to map potential habitat and monitor habitat quality directly related to recruitment, survival, and management needs. Most habitats for both species were degraded by anthropogenic reductions in fire frequency. Degradation occurred across large areas. Florida Scrub-Jay recruitment and survival were most influenced by shrub height states. Multistate modeling of shrub heights showed that state transitions were influenced by vegetation composition, edges, and habitat management. Measured population declines of 4% per year corroborated habitat-specific modeling predictions. Habitat quality improved over the study period but not enough to recover precariously small populations. The degree of landcover fragmentation influenced mean Florida Scrub-Jay dispersal distances but not the number of occupied territories between natal and breeding territories. There was little exchange between populations, which were usually further apart than mean dispersal distances. Florida Scrub-Jays bred or delayed breeding depending on age, sex, and breeding opportunities. I show an urgent need also for Florida Grasshopper Sparrow habitat restoration given that the endangered bird has declined to only two sizeable populations and there is a high likelihood for continued large decline. A major effect of habitat fragmentation identified in this dissertation that should apply to many organisms in disturbance prone systems is that fragmentation disrupts natural processes, reducing habitat quality across large areas. Humans have managed wildland fire for > 40,000 years, so it should be possible to manage habitat for many endangered species that make Florida's biodiversity unique. This dissertation provides methods to quantify landscape units into potential source and sink territories and provides a basis for applying adaptive management to reach population and conservation goals.
Ph.D.
Department of Biology
Sciences
Conservation Biology PhD

This Introduction provides a broad overview of the scientific advances and crosscutting developments that increasingly influence epidemiologic research on the causes and prevention of cancer. High-throughput technologies have identified the molecular “driver” events in tumor tissue that underlie the multistage development of many types of cancer. These somatic (largely acquired) alterations disrupt normal genetic and epigenetic control over cell maintenance, division and survival. Tumor classification is also changing to reflect the genetic and molecular alterations in tumor tissue, as well as the anatomic, morphologic, and histologic phenotype of the cancer. Genome-wide association studies (GWAS) have identified more than 700 germline (inherited) genetic loci associated with susceptibility to various forms of cancer, although the risk estimates for almost all of these are small to modest and their exact location and function remain to identified. Advances in genomic and other “OMIC” technologies are identifying biomarkers that reflect internal exposures, biological processes and intermediate outcomes in large population studies. While research in many of these areas is still in its infancy, mechanistic and molecular assays are increasingly incorporated into etiologic studies and inferences about causation. Other sections of the book discuss the global public health impact of cancer, the growing list of exposures known to affect cancer risk, the epidemiology of over 30 types of cancer by tissue of origin, and preventive interventions that have dramatically reduced the incidence rates of several major cancers.

To apply a model to a particular system, it is necessary to estimate the variables describing the current state of the system, such as host population size and infection status, and also to estimate the parameters of the model, such as the transmission rate and birth and death rates. In practice, this is often the most difficult part of the modeling process. Mark–recapture methods can estimate both population size and survival, with multistate designs capable of also estimating infection prevalence and the rates at which individuals acquire and lose infection. Recent developments do not require physical capture of animals, using trail cameras or DNA analysis of hair or feces. Parameters associated with disease transmission are difficult to estimate in a field situation, but a range of new tools are available. The best way to estimate R0 depends on whether individual contacts can be traced, whether an epizootic is in its early stages, or whether infection is endemic. Approximate Bayesian Computation is a computer-intensive method that estimates multiple parameters simultaneously by selecting the parameter sets that can match observed system behavior most closely.