Semiparametric multivariate joint model for skewed-longitudinal and survival data: A Bayesian approach.

Joint models and statistical inference for longitudinal and survival data have been an active area of statistical research and have mostly coupled a longitudinal biomarker-based mixed-effects model with normal distribution and an event time-based survival model. In practice, however, the following issues may standout: (i) Normality of model error in longitudinal models is a routine assumption, but it may be unrealistically violating data features of subject variations. (ii) Data collected are often featured by the mixed types of multiple longitudinal outcomes which are significantly correlated, ignoring their correlation may lead to biased estimation. Additionally, a parametric model specification may be inflexible to capture the complicated patterns of longitudinal data. (iii) Missing observations in the longitudinal data are often encountered; the missing measures are likely to be informative (nonignorable) and ignoring this phenomenon may result in inaccurate inference. Multilevel item response theory (MLIRT) models have been increasingly used to analyze the multiple longitudinal data of mixed types (ie, continuous and categorical) in clinical studies. In this article, we develop an MLIRT-based semiparametric joint model with skew-t distribution that consists of an extended MLIRT model for the mixed types of multiple longitudinal data and a Cox proportional hazards model, linked through random-effects. A Bayesian approach is employed for joint modeling. Simulation studies are conducted to assess performance of the proposed models and method. A real example from primary biliary cirrhosis clinical study is analyzed to estimate parameters in the joint model and also evaluate sensitivity of parameter estimates for various plausible nonignorable missing data mechanisms.

Susceptibility Analysis of Glacier Debris Flow Based on Remote Sensing Imagery and Deep Learning: A Case Study along the G318 Linzhi Section.

Glacial debris flow is a common natural disaster, and its frequency has been increasing in recent years due to the continuous retreat of glaciers caused by global warming. To reduce the damage caused by glacial debris flows to human and physical properties, glacier susceptibility assessment analysis is needed. Most research efforts consider the effect of existing glacier area and ignore the effect of glacier ablation volume change. In this paper, we consider the impact of glacier ablation volume change to investigate the susceptibility of glacial debris flow. The susceptibility to mudslide was evaluated by taking the glacial mudslide-prone ditch of G318 Linzhi section of Sichuan-Tibet Highway as the research object. First, by using a simple band ratio method with manual correction, we produced a glacial mudslide remote sensing image dataset, and second, we proposed a deep-learning-based approach using a weight-optimized glacial mudslide semantic segmentation model for accurately and automatically mapping the boundaries of complex glacial mudslide-covered remote sensing images. Then, we calculated the ablation volume by the change in glacier elevation and ablation area from 2015 to 2020. Finally, glacial debris flow susceptibility was evaluated based on the entropy weight method and Topsis method with glacial melt volume in different watersheds as the main factor. The research results of this paper show that most of the evaluation indices of the model are above 90%, indicating that the model is reasonable for glacier boundary extraction, and remote sensing images and deep learning techniques can effectively assess the glacial debris flow susceptibility and provide support for future glacial debris flow disaster prevention.

Carbon, nitrogen, and phosphorus metabolic relationships and reaction mechanisms in SBBR processes in the plateau habitat.

In this study, two laboratory-scale SBBR reactors were established in a plateau habitat. Using high flux sequencing, the SBBR process was compared by natural sediment and autotrophic sludge to characterize the functional modules and functional genes of carbon, nitrogen, and phosphorus metabolism under different working conditions and to analyze the reaction mechanism. The results showed that all the functional modules of carbon metabolism and nitrogen metabolism were found in the SBBR process, except for methane metabolism, which occurred at 25 °C in tank 2, the functional modules related to methane metabolism are enhanced at all working conditions. Except for methane metabolism, all functional genes in tank 2 are inhibited by different working conditions, whereas tank 1 shows a slight enhancement. The different working conditions in nitrogen metabolism demonstrate inhibition of functional modules and functional genes in both tanks. Oxidative phosphorylation was missing five functional modules, except for M00153, where only two genes, K00424 and K22501, are missing, all of the required genes are missing in the other four functional modules. Overall the different conditions demonstrated some inhibition in both reaction tanks of the SBBR process. It is preferable to use self-cultivated sludge for membrane acclimation when operating the SBBR process in a plateau habitat. The findings of this study can be used to further research microbial carbon, nitrogen, and phosphorus metabolism mechanisms in SBBR processes in plateau habitats.

Quantile-Adaptive Sufficient Variable Screening by Controlling False Discovery.

Sufficient variable screening rapidly reduces dimensionality with high probability in ultra-high dimensional modeling. To rapidly screen out the null predictors, a quantile-adaptive sufficient variable screening framework is developed by controlling the false discovery. Without any specification of an actual model, we first introduce a compound testing procedure based on the conditionally imputing marginal rank correlation at different quantile levels of response to select active predictors in high dimensionality. The testing statistic can capture sufficient dependence through two paths: one is to control false discovery adaptively and the other is to control the false discovery rate by giving a prespecified threshold. It is computationally efficient and easy to implement. We establish the theoretical properties under mild conditions. Numerical studies including simulation studies and real data analysis contain supporting evidence that the proposal performs reasonably well in practical settings.

Macrophages promote cartilage regeneration in a time- and phenotype-dependent manner.

Immune regulation of osteochondral defect regeneration has not yet been rigorously characterized. Although macrophages have been demonstrated to regulate the regeneration process in various tissues, their direct contribution to cartilage regeneration remains to be investigated, particularly the functions of polarized macrophage subpopulations. In this study, we investigated the origins and functions of macrophages during healing of osteochondral injury in the murine model. Upon osteochondral injury, joint macrophages are predominantly derived from circulating monocytes. Macrophages are essential for spontaneous cartilage regeneration in juvenile C57BL/6 mice, by modulating proliferation and apoptosis around the injury site. Exogeneous macrophages also exhibit therapeutic potential in promoting cartilage regeneration in adult mice with poor regenerative capacity, possibly via regulation of PDGFRα+  stem cells, with this process being influenced by initial phenotype and administration timing. Only M2c macrophages are able to promote regeneration of both cartilage tissues and subchondral bone. Overall, we reveal the direct link between macrophages and osteochondral regeneration and highlight the key roles of relevant immunological niches in successful regeneration.

Construction of Two-Dimensional Fluorescent Covalent Organic Framework Nanosheets for the Detection and Removal of Nitrophenols.

In this work, we synthesized a two-dimensional fluorescent covalent-organic framework (TFPB-TTA COF) nanosheet by selecting and designing reactive monomers to realize the dual-functional processing of nitrophenols. The staggered benzene ring, triazine structure, and imine bond (C═N) of the TFPB-TTA COF can capture free nitrophenols through hydrogen bonding and conjugation interaction, and then, the photoinduced electron transfer and fluorescence resonance energy transfer (FRET) between the TFPB-TTA COF and nitrophenols affects the fluorescence emission of the TFPB-TTA COF, realizing the fluorescence sensing of nitrophenols. The large Ksv values and the low detection limit suggest that the TFPB-TTA COF can serve as sensitive and selective fluorescence sensors for nitrophenol detection in an aqueous system. At the same time, the strong interaction combined with the porous network structure of the TFPB-TTA COF facilitates the efficient adsorption and removal of nitrophenols. Especially for 2,4,6-trinitrophenol, the maximum adsorption capacity can reach 1045.53 mg/g with good recyclability and high structural stability of the TFPB-TTA COF. This work proposed a simple synthetic method for the construction of a fluorescent COF platform for the sensitive determination and efficient adsorption of nitrophenols.

Translation and validation of the Chinese ABCD risk questionnaire to evaluate adults' awareness and knowledge of the risks of cardiovascular diseases.

BACKGROUND: Assessment of health beliefs and risk perception is a critical means to prevent coronary heart disease, but there are few such studies on assessment in the Chinese population. Given the demonstrated value and widespread use of the Attitudes and Beliefs about Cardiovascular Disease Risk Questionnaire (ABCD), this study was designed to translate it into Chinese, and to evaluate its reliability and validity in a Chinese population. METHODS: The Chinese version of the ABCD was created using the Beaton translation model, which included forward and backward translation. The reliability and construct validity of the Chinese ABCD were examined in a sample of 353 adults who participated in the public welfare projects of the Chinese National Center for Cardiovascular Diseases in Guilin city, Guangxi. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed to examine the factor structure of the Chinse ABCD. The internal consistency of the questionnaire was assessed using Cronbach's α and corrected item-total correlations. RESULTS: We deleted item 7 in the knowledge dimension of the Chinese ABCD and added two items about smoking and sleep knowledge, while retaining 25 of the original items, so that it finally included 27 items. The correlations were .20-.90; the correlations between each item and the total score of the ABCD were .34-.86; and the item-level Content Validity Index (I-CVI) was .86-1.00. The results of the EFA showed that all items were close to .40, and the cumulative variance contribution rate was 63.88%. The model fit was acceptable (χ2 = 698.79, df = 243, χ2/df = 2.87, P < 0.001, SRMR = 0.06, RMSEA = 0.05, CFI = 0.96, and TLI = 0.94) according to the CFA. The Cronbach' s α of the entire questionnaire was .86, and the α of each of dimension was .65, .90, .88, and .78. The split-half reliability of the entire the ABCD was .67, and the test-retest reliability was .97 (P < 0.05). The questionnaire had good reliability and validity and was associated with sociodemographic and health-related characteristics (smoking and Body Mass Index). CONCLUSION: The Chinese version of the ABCD has good reliability and validity, and provides a reliable assessment tool for measuring public health beliefs about the risk of cardiovascular disease, promoting the primary prevention of coronary heart disease.

Mapping the anatomy of perceptual pseudoneglect. A multivariate approach.

Fundamental to the understanding of the functions of spatial cognition and attention is to clarify the underlying neural mechanisms. It is clear that relatively right-dominant activity in ventral and dorsal parieto-frontal cortex is associated with attentional reorienting, certain forms of mental imagery and spatial working memory for higher loads, while lesions mostly to right ventral areas cause spatial neglect with pathological attentional biases to the right side. In contrast, complementary leftward biases in healthy people, called pseudoneglect, have been associated with varying patterns of cortical activity. Notably, this inconsistency may be explained, at least in part, by the fact that pseudoneglect studies have often employed experimental paradigms that do not control sufficiently for cognitive processes unrelated to pseudoneglect. To address this issue, here we administered a carefully designed continuum of pseudoneglect and control tasks in healthy adults while using functional magnetic resonance imaging (fMRI). Data submitted to partial least square (PLS) imaging analysis yielded a significant latent variable that identified a right-dominant network of brain regions along the intra-occipital and -parietal sulci, frontal eye fields and right ventral cortex in association with perceptual pseudoneglect. Our results shed new light on the interplay of attentional and cognitive systems in pseudoneglect.

Facile synthesis of fluorescent tungsten oxide quantum dots for telomerase detection based on the inner filter effect.

The traditional detection of telomerase activity is mainly based on the polymerase chain reaction (PCR), which has the disadvantages of being time-consuming and susceptible to interferences; thus, here, we propose a facile method for the fabrication of fluorescent tungsten oxide quantum dots (WOx QDs) and employ them for telomerase activity sensing. It is found that the fluorescence of WOx QDs can be significantly quenched by hemin based on the inner filter effect (IFE). However, in the presence of telomerase, the primer-DNA can be extended to generate repeating units of TTAGGG to form G-quadruplex and thus, hemin can be encapsulated to reduce its absorbance, resulting in decreased IFE and efficient fluorescence recovery of WOx QDs. Based on the fluorescence changes of IFE between hemin and WOx QDs, the telomerase activity within the range of 50-30 000 HeLa cells can be detected and the lowest detection amount can reach 17 cells. The method exhibits good versatility that can also be applied to telomerase detection in A549 and L929 cells. In addition, because of the good biocompatibility of the sensor, it can be used for the real-time monitoring of telomerase activity in living cells, thus showing great potential in tumor diagnosis and inhibitor drug screening.

Four New Isocoumarins and a New Natural Tryptamine with Antifungal Activities from a Mangrove Endophytic Fungus Botryosphaeria ramosa L29.

Four new isocoumarin derivatives, botryospyrones A (1), B (2), C (3), and D (4), and a new natural tryptamine, (3aS, 8aS)-1-acetyl-1, 2, 3, 3a, 8, 8a-hexahydropyrrolo [2,3b] indol-3a-ol (5), were isolated from a marine mangrove endophytic fungus Botryosphaeria ramosa L29, obtained from the leaf of Myoporum bontioides. Their structures were elucidated using spectroscopic analysis. The absolute configurations of compounds 3, 4, and 5 were determined by comparison of their circular dichroism (CD) spectra with the calculated data. The inhibitory activities of compound 1 on Fusarium oxysporum, of compounds 2 and 3 on F. oxysporum and Fusarium graminearum, and of compound 5 on F. oxysporum, Penicillium italicum, and F. graminearum were higher than those of triadimefon, widely used as an agricultural fungicide. Compound 5 was produced after using the strategy we called "using inhibitory stress from components of the host" (UISCH), wherein (2R, 3R)-3, 5, 7-trihydroxyflavanone 3-acetate, a component of M. bontioides with weak growth inhibitory activity towards B. ramosa L29, was introduced into the culture medium.

Cobalt phosphide nanowires for fluorometric detection and in-situ imaging of telomerase activity via hybridization chain reactions.

The authors describe cobalt phosphide (CoP) nanowires for use in sensitive fluorometric determination of the activity of the enzyme telomerase. A hybridization chain reaction (HCR) is applied to amplify the signal and carboxyfluorescein (FAM)-labelled hairpin probes (H1 and H2) are applied to match the telomeric DNA sequence. The CoP nanowires act as both the photoinduced electron transfer (PET) acceptor to induce fluorescence quenching, and also as an efficient probe carrier to facilitate telomerase imaging in living cells. The telomerase-triggered primer extension initiates an alternating hybridization reaction between H1 and H2. These result in the dissociation of FAM-labelled probes from CoP nanowires and thus an enhancement of the green fluorescence. The method is fairly simple and was applied to the detection of three types of cancer cells. The detection limit is as low as 7 cells (in case of HeLa cells). Conceivably, the method has a large potential in terms of inhibitor drug screening. Graphical abstract Schematic presentation of telomerase detection based on cobalt phosphide (CoP) nanowires and hybridization chain reaction (HCR). The telomerase-triggered primer extension can initiate the alternating hybridization reaction between carboxyfluorescein (FAM)-labelled hairpin probes (H1 and H2), and the generated long DNA duplex cannot be adsorbed on the CoP nanowires. This prevents the photoinduced electron transfer (PET) from FAM to CoP nanowires.

A facile graphene oxide-based fluorescent nanosensor for the in situ "turn-on" detection of telomerase activity.

A facile and sensitive method for the quantitative detection of telomerase and in situ imaging of intracellular telomerase is developed by using a graphene oxide (GO)-based fluorescent nanosensor. The nanosensor consists of a fluorescent DNA (P1) adsorbed on the GO surface. Here, GO serves not only as a fluorescence quencher but also as a carrier to successfully transport P1 into cancer cells as a signal reporter. P1 is a dye-labeled single-stranded DNA complementary to the telomeric repeated sequence, and initially the combination of P1 and GO exhibits minimal background fluorescence. When telomerase extends its repeat units of TTAGGG on the 3'-end of the primer-DNA, the fluorescence of P1 is subsequently recovered because the telomeric repeated sequence can hybridize with P1 and liberate it from the GO surface. This method enables the determination of telomerase activity down to 10 cells. For the in situ detection of telomerase, upon endocytosis of the P1/GO combinatorial probe into living cancer cells, the intracellular telomerase extends its primer to produce the telomeric repeated sequence and then turns on the fluorescence of P1, which can be directly monitored by confocal laser scanning microscopy. The feasibility of the assay is further investigated by treating with telomerase-related drugs, and the results demonstrate its potential in antitumor drug screening and cancer therapy evaluation.

Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies.

Longitudinal and time-to-event data are often observed together. Finite mixture models are currently used to analyze nonlinear heterogeneous longitudinal data, which, by releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, can cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, and be associated with clinically important time-to-event data. This article develops a joint modeling approach to a finite mixture of NLME models for longitudinal data and proportional hazard Cox model for time-to-event data, linked by individual latent class indicators, under a Bayesian framework. The proposed joint models and method are applied to a real AIDS clinical trial data set, followed by simulation studies to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and Cox model are fitted separately.

Branched-Chain Amino Acids as Predictors for Individual Differences of Cisplatin Nephrotoxicity in Rats: A Pharmacometabonomics Study.

Nephrotoxicity is the dose-limiting adverse effect of cisplatin with large individual differences. Up to now, little has been done on how to recognize and predict the individual differences in either preclinical or clinical research. In the present study, important postdose indicators were screened out first and integrated into a grouping factor, according to which rats were recognized as lowly or highly sensitive individuals. Then, mass-spectrometry-based untargeted metabolomics approach was performed to dissect the metabolic differences in predose serum of the two groups. Eventually, branched-chain amino acids (BCAAs) were found to be most significant with the lowest p value of Mann-Whitney U test and the highest area under receiving operating characteristic curve (AUC-ROC). The findings were further confirmed by absolute quantitation of BCAAs using liquid chromatography-tandem mass spectrometry. Binary logistic regression showed that in the discovery set absolute BCAA contents in rat predose serum could predict cisplatin nephrotoxicity with accuracy of 85%. This result was validated by another two independent external validation sets with accuracy of 81.8 and 78.8%, respectively. This study could provide new insight into cisplatin nephrotoxicity and may help expedite personalized medicine of cisplatin or other antitumor drugs in future clinical studies.

Influences of Normalization Method on Biomarker Discovery in Gas Chromatography-Mass Spectrometry-Based Untargeted Metabolomics: What Should Be Considered?

Data reduction techniques in gas chromatography-mass spectrometry-based untargeted metabolomics has made the following workflow of data analysis more lucid. However, the normalization process still perplexes researchers, and its effects are always ignored. In order to reveal the influences of normalization method, five representative normalization methods (mass spectrometry total useful signal, median, probabilistic quotient normalization, remove unwanted variation-random, and systematic ratio normalization) were compared in three real data sets with different types. First, data reduction techniques were used to refine the original data. Then, quality control samples and relative log abundance plots were utilized to evaluate the unwanted variations and the efficiencies of normalization process. Furthermore, the potential biomarkers which were screened out by the Mann-Whitney U test, receiver operating characteristic curve analysis, random forest, and feature selection algorithm Boruta in different normalized data sets were compared. The results indicated the determination of the normalization method was difficult because the commonly accepted rules were easy to fulfill but different normalization methods had unforeseen influences on both the kind and number of potential biomarkers. Lastly, an integrated strategy for normalization method selection was recommended.

Bayesian quantile regression for nonlinear mixed-effects joint models for longitudinal data in the presence of mismeasured covariate errors.

Quantile regression (QR) models have recently received increasing attention in longitudinal studies where measurements of the same individuals are taken repeatedly over time. When continuous (longitudinal) responses follow a distribution that is quite different from a normal distribution, usual mean regression (MR)-based linear models may fail to produce efficient estimators, whereas QR-based linear models may perform satisfactorily. To the best of our knowledge, there have been very few studies on QR-based nonlinear models for longitudinal data in comparison to MR-based nonlinear models. In this article, we study QR-based nonlinear mixed-effects (NLME) joint models for longitudinal data with non-central location and outliers and/or heavy tails in response, and non-normality and measurement errors in covariate under Bayesian framework. The proposed QR-based modeling method is compared with an MR-based one by an AIDS clinical dataset and through simulation studies. The proposed QR joint modeling approach can be not only applied to AIDS clinical studies, but also may have general applications in other fields as long as relevant technical specifications are met.

Discovery of Potential Biomarkers with Dose- and Time-Dependence in Cisplatin-Induced Nephrotoxicity Using Metabolomics Integrated with a Principal Component-Based Area Calculation Strategy.

Cisplatin is a potent chemotherapeutic agent. However, its clinical usage is restricted by serious adverse effects, especially nephrotoxicity. For revealing the dose- and time-dependence of cisplatin-induced nephrotoxicity, mass spectrometry-based metabolomics integrated with a principal component-based area calculation (PCAC) strategy was proposed in the present study. Area plots based on the first two principal components of the principal component analysis model were constructed first. Then, the sums of cumulative areas under PC-T curves (AUCPC-T) were calculated. Finally, the fold change of AUCPC-T between experimental and control groups at different time points was calculated and used as an indicative parameter. With the PCAC approach, dose- and time-dependence of cisplatin-induced metabolic change was quantitatively confirmed for the first time. Furthermore, 27 potential biomarkers with dose- and time-dependence related to nephrotoxicity induced by cisplatin were screened out and tentatively identified. Metabolic pathways interrupted by cisplatin mainly included energy, amino acid, and lipid metabolism.

A novel liquid chromatography tandem mass spectrometry method for simultaneous determination of branched-chain amino acids and branched-chain α-keto acids in human plasma.

Branched-chain amino acids (BCAAs) and branched-chain α-keto acids (BCKAs) play significant biological roles as they are involved in protein and neurotransmitter synthesis as well as energy metabolism pathways. To routinely and accurately study the dynamics of BCAAs and BCKAs in human diseases, e.g. cerebral infarction, a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method has been developed and validated. The plasma samples were deproteinized with acetonitrile, and then separated on a reversed phase C18 column with a mobile phase of 0.1 % formic acid (solvent A)-methanol (solvent B) using gradient elution. The detection of BCAAs and BCKAs was conducted in multiple reaction monitoring with positive/negative electrospray ionization switching mode. Biologically relevant isomers such as leucine and isoleucine were individually quantified by combining chromatographic separation and fragmentation. Good linearity (R (2) > 0.99) was obtained for all six analytes with the limits of detection from 0.1 to 0.2 µg/mL. The intra-day and inter-day accuracy ranged from 93.7 to 108.4 % and the relative standard deviation (RSD) did not exceed 15.0 %. The recovery was more than 80 % with RSD less than 14.0 %. The main improvements compared to related, state-of-the-art methods included enhanced sensitivity, enhanced separation of isomers, and reduced complexity of sample processing. Finally, the validated method was applied to analyze the plasma samples of healthy volunteers and patients suffering cerebral infarction, and significant differences in the concentration levels of BCAAs and BCKAs were observed.

Bayesian quantile regression-based nonlinear mixed-effects joint models for time-to-event and longitudinal data with multiple features.

This article explores Bayesian joint models for a quantile of longitudinal response, mismeasured covariate and event time outcome with an attempt to (i) characterize the entire conditional distribution of the response variable based on quantile regression that may be more robust to outliers and misspecification of error distribution; (ii) tailor accuracy from measurement error, evaluate non-ignorable missing observations, and adjust departures from normality in covariate; and (iii) overcome shortages of confidence in specifying a time-to-event model. When statistical inference is carried out for a longitudinal data set with non-central location, non-linearity, non-normality, measurement error, and missing values as well as event time with being interval censored, it is important to account for the simultaneous treatment of these data features in order to obtain more reliable and robust inferential results. Toward this end, we develop Bayesian joint modeling approach to simultaneously estimating all parameters in the three models: quantile regression-based nonlinear mixed-effects model for response using asymmetric Laplace distribution, linear mixed-effects model with skew-t distribution for mismeasured covariate in the presence of informative missingness and accelerated failure time model with unspecified nonparametric distribution for event time. We apply the proposed modeling approach to analyzing an AIDS clinical data set and conduct simulation studies to assess the performance of the proposed joint models and method. Copyright © 2016 John Wiley & Sons, Ltd.

A Bayesian mixture of semiparametric mixed-effects joint models for skewed-longitudinal and time-to-event data.

In longitudinal studies, it is of interest to investigate how repeatedly measured markers in time are associated with a time to an event of interest, and in the mean time, the repeated measurements are often observed with the features of a heterogeneous population, non-normality, and covariate measured with error because of longitudinal nature. Statistical analysis may complicate dramatically when one analyzes longitudinal-survival data with these features together. Recently, a mixture of skewed distributions has received increasing attention in the treatment of heterogeneous data involving asymmetric behaviors across subclasses, but there are relatively few studies accommodating heterogeneity, non-normality, and measurement error in covariate simultaneously arose in longitudinal-survival data setting. Under the umbrella of Bayesian inference, this article explores a finite mixture of semiparametric mixed-effects joint models with skewed distributions for longitudinal measures with an attempt to mediate homogeneous characteristics, adjust departures from normality, and tailor accuracy from measurement error in covariate as well as overcome shortages of confidence in specifying a time-to-event model. The Bayesian mixture of joint modeling offers an appropriate avenue to estimate not only all parameters of mixture joint models but also probabilities of class membership. Simulation studies are conducted to assess the performance of the proposed method, and a real example is analyzed to demonstrate the methodology. The results are reported by comparing potential models with various scenarios.