Joint latent class model: Simulation study of model properties and application to amyotrophic lateral sclerosis disease

Background In many clinical applications, evolution of a longitudinal marker is censored by an event occurrence, and, symmetrically, event occurrence can be influenced by the longitudinal marker evolution. In such frameworks joint modeling is of high interest. The Joint Latent Class Model (JLCM) allows to stratify the population into groups (classes) of patients that are homogeneous both with respect to the evolution of a longitudinal marker and to the occurrence of an event; this model is widely employed in real-life applications. However, the finite sample-size properties of this model remain poorly explored. Methods In the present paper, a simulation study is carried out to assess the impact of the number of individuals, of the censoring rate and of the degree of class separation on the finite sample size properties of the JLCM. A real-life application from the neurology domain is also presented. This study assesses the precision of class membership prediction and the impact of covariates omission on the model parameter estimates. Results Simulation study reveals some departures from normality of the model for survival sub-model parameters. The censoring rate and the number of individuals impact the relative bias of parameters, especially when the classes are weakly distinguished. In real-data application the observed heterogeneity on individual profiles in terms of a longitudinal marker evolution and of the event occurrence remains after adjusting to clinically relevant and available covariates; Conclusion The JLCM properties have been evaluated. We have illustrated the discovery in practice and highlights the usefulness of the joint models with latent classes in this kind of data even with pre-specified factors. We made some recommendations for the use of this model and for future research.

Dynamic Evolution of the Type of “meideshuo” Discourse Marker and Its Metapragmatic Functions

<p align="LEFT"><span style="font-family: TrebuchetMS; font-size: xx-small;"><span style="font-family: TrebuchetMS; font-size: xx-small;">A type of “</span></span><em><span style="font-family: TrebuchetMS,Italic; font-size: xx-small;"><span style="font-family: TrebuchetMS,Italic; font-size: xx-small;">meideshuo</span></span></em><span style="font-family: TrebuchetMS; font-size: xx-small;"><span style="font-family: TrebuchetMS; font-size: xx-small;">” discourse markers</span></span></p><p align="LEFT"><span style="font-family: TrebuchetMS; font-size: xx-small;"><span style="font-family: TrebuchetMS; font-size: xx-small;">contains three subtypes: “</span></span><em><span style="font-family: TrebuchetMS,Italic; font-size: xx-small;"><span style="font-family: TrebuchetMS,Italic; font-size: xx-small;">meideshuo</span></span></em><span style="font-family: TrebuchetMS; font-size: xx-small;"><span style="font-family: TrebuchetMS; font-size: xx-small;">”,</span></span></p><p align="LEFT"><span style="font-family: TrebuchetMS; font-size: xx-small;"><span style="font-family: TrebuchetMS; font-size: xx-small;">“</span></span><em><span style="font-family: TrebuchetMS,Italic; font-size: xx-small;"><span style="font-family: TrebuchetMS,Italic; font-size: xx-small;">meihuashuo</span></span></em><span style="font-family: TrebuchetMS; font-size: xx-small;"><span style="font-family: TrebuchetMS; font-size: xx-small;">”, and “</span></span><em><span style="font-family: TrebuchetMS,Italic; font-size: xx-small;"><span style="font-family: TrebuchetMS,Italic; font-size: xx-small;">meishuode</span></span></em><span style="font-family: TrebuchetMS; font-size: xx-small;"><span style="font-family: TrebuchetMS; font-size: xx-small;">”. From the</span></span></p><p align="LEFT">point of view of semantic-pragmatics, this type</p><p align="LEFT">of discourse markers evolves dynamically from</p><p align="LEFT">the meaning of “no words to say” to “positive</p><p align="LEFT">meaning”, and finally evolves to a discourse</p><p align="LEFT">marker. The metapragmatic functions of the</p><p align="LEFT">discourse markers are new information focused,</p><p align="LEFT">proposition affirmed, and communicative</p><p align="LEFT">purpose predicted. The process of the discourse</p><p align="LEFT">marker evolution is not only the process of</p><p align="LEFT">lexicalization and grammaticalization, but also</p><p align="LEFT">the process of intersubjectification. The</p><p align="LEFT">pragmatic extensions and semantic entailment</p><p align="LEFT">are contributed to function as motivations for</p><p align="LEFT"><span style="font-family: TrebuchetMS; font-size: xx-small;"><span style="font-family: TrebuchetMS; font-size: xx-small;">the dynamic evolution of “</span></span><em><span style="font-family: TrebuchetMS,Italic; font-size: xx-small;"><span style="font-family: TrebuchetMS,Italic; font-size: xx-small;">meideshuo</span></span></em><span style="font-family: TrebuchetMS; font-size: xx-small;"><span style="font-family: TrebuchetMS; font-size: xx-small;">”</span></span></p><p>discourse markers.</p>

A Bayesian Model of AFLP Marker Evolution and Phylogenetic Inference

Amplified Fragment Length Polymorphism (AFLP) markers are formed by selective amplification of DNA fragments from digested total genomic DNA. The technique is popular because it is a relatively inexpensive way to produce large numbers of reproducible genetic markers. In this paper, we describe a Bayesian approach to modeling AFLP marker evolution by nucleotide substitution and an MCMC approach to estimate phylogeny from AFLP marker data. We demonstrate the method on species in Carex section Ovales, a group of sedges common in North America. We compare the results of our analysis with a clustering method based on Nei and Li's restriction-site distance and a two-state Bayesian analysis using MrBayes.

Computer Simulation of Marker Evolution Upon Ion Irradiation

ABSTRACTIn ion beam mixing, the subject on the marker evolution of Si/Pt/Si target during 300 keV Xe ion irradiation has been an interesting topic of recent years. A cottputer simulation for this system by using the Monte Carlo code TCIS-4 is reported here. Dynamic target model was employed. Some radiation-enhanced diffusion and relaxation of the displacement cascades were also considered. Results of this simulation study reveal the evolution of Pt marker profile with fluences during bombardment. The shifts of Pt marker and the dispersions evaluated from the profiles agree fairly well with experimental date, demonstrating a reslistic model of the target being irradiated is important.