Inferences for Joint Modelling of Repeated Ordinal Scores and Time to Event Data

In clinical trials and other follow-up studies, it is natural that a response variable is repeatedly measured during follow-up and the occurrence of some key event is also monitored. There has been a considerable study on the joint modelling these measures together with information on covariates. But most of the studies are related to continuous outcomes. In many situations instead of observing continuous outcomes, repeated ordinal outcomes are recorded over time. The joint modelling of such serial outcomes and the time to event data then becomes a bit complicated. In this article we have attempted to analyse such models through a latent variable model. In view of the longitudinal variation on the ordinal outcome measure, it is desirable to account for the dependence between ordered categorical responses and survival time for different causes due to unobserved factors. A flexible Monte Carlo EM (MCEM) method based on exact likelihood is proposed that can simultaneously handle the longitudinal ordinal data and also the censored time to event data. A computationally more efficient MCEM method based on approximation of the likelihood is also proposed. The method is applied to a number of ordinal scores and survival data from trials of a treatment for children suffering from Duchenne Muscular Dystrophy. Finally, a simulation study is conducted to examine the finite sample properties of the proposed estimators in the joint model under two different methods

Joint Model for Longitudinal and Time-To-Event Data: a Two-Stage Approach

In clinical and epidemiological studies, very often, observations are collected on more than one correlated processes. For example, in AIDS related studies, along with a longitudinal biomarker like CD4 cell count, data on time-to-death is also recorded. Modelling them separately may give bias estimates. This necessitates the concept of joint modelling where two or more processes are modelled together. To link these processes, the usual technique is to use the same or highly correlated subject-specific random-effects for all the sub-models. In this work, structural correlation based on the conditional distribution of time-to-event given longitudinal response is used. A computationally efficient two-stage method is used to find the estimates. At the first stage, longitudinal submodel is fitted using nlme package in R. In the second stage, to avoid the complexity of second order differentiation, we have used an adaptive gradient descent algorithm. The simulation study shows that this structural correlation is good enough to take care of the correlation between these two simultaneous processes. A rapid convergence is also achieved. The proposed method is finally applied to a data set related to AIDS studies. Keywords

Joint Model for Longitudinal and Time-To-Event Data: a Two-Stage Approach

In clinical and epidemiological studies, very often, observations are collected on more than one correlated processes. For example, in AIDS related studies, along with a longitudinal biomarker like CD4 cell count, data on time-to-death is also recorded. Modelling them separately may give bias estimates. This necessitates the concept of joint modelling where two or more processes are modelled together. To link these processes, the usual technique is to use the same or highly correlated subject-specific random-effects for all the sub-models. In this work, structural correlation based on the conditional distribution of time-to-event given longitudinal response is used. A computationally efficient two-stage method is used to find the estimates. At the first stage, longitudinal submodel is fitted using nlme package in R. In the second stage, to avoid the complexity of second order differentiation, we have used an adaptive gradient descent algorithm. The simulation study shows that this structural correlation is good enough to take care of the correlation between these two simultaneous processes. A rapid convergence is also achieved. The proposed method is finally applied to a data set related to AIDS studies. Keywords

Blockchain Application in Healthcare Systems: A Review

In the recent years, blockchain technology has gained significant attention in the healthcare sector. It has the potential to alleviate a wide variety of major difficulties in electronic health record systems. This study presents an elaborate overview of the existing research works on blockchain applications in the healthcare industry. This paper evaluates 144 articles that discuss the importance and limits of using blockchain technologies to improve healthcare operations. The objective is to demonstrate the technology’s potential uses and highlight the difficulties and possible sectors for future blockchain research in the healthcare domain. The paper starts with an extensive background study of blockchain and its features. Then, the paper focuses on providing an extensive literature review of the selected articles to highlight the current research themes in blockchain-based healthcare systems. After that, major application areas along with the solutions provided by blockchain in healthcare systems are pointed out. Finally, a discussion section provides insight into the limitations, challenges and future research directions

Smart Home System: A Comprehensive Review

Smart home is a habitation that has been outfitted with technological solutions that are intended to provide people with services that are suited to their needs. The purpose of this article is to perform a systematic assessment of the latest smart home literature and to conduct a survey of research and development conducted in this field. In addition to presenting a complete picture of the current smart home system’s (SHS) development and characteristics, this paper provides a deep insight into latest hardware and trends. The research then moves on to a detailed discussion of some of the important services provided by the SHS and its advantages. The paper also statistically discusses the current and future research trends in the SHS, followed by a detailed portrayal of the difficulties and roadblocks in implementing them. The comprehensive overview of the SHS presented in this paper will help designers, researchers, funding agencies, and policymakers have a bird’s-eye view of the overall concept, attributes, technological aspects, and features of modern SHSs

New aQTL SNPs for the CYP2D6 Identified by a Novel Mediation Analysis of Genome-Wide SNP Arrays, Gene Expression Arrays, and CYP2D6 Activity

Background. The genome-wide association studies (GWAS) have been successful during the last few years. A key challenge is that the interpretation of the results is not straightforward, especially for transacting SNPs. Integration of transcriptome data into GWAS may provide clues elucidating the mechanisms by which a genetic variant leads to a disease. Methods. Here, we developed a novel mediation analysis approach to identify new expression quantitative trait loci (eQTL) driving CYP2D6 activity by combining genotype, gene expression, and enzyme activity data. Results. 389,573 and 1,214,416 SNP-transcript-CYP2D6 activity trios are found strongly associated (P<10-5, FDR=16.6% and 11.7%) for two different genotype platforms, namely, Affymetrix and Illumina, respectively. The majority of eQTLs are trans-SNPs. A single polymorphism leads to widespread downstream changes in the expression of distant genes by affecting major regulators or transcription factors (TFs), which would be visible as an eQTL hotspot and can lead to large and consistent biological effects. Overlapped eQTL hotspots with the mediators lead to the discovery of 64 TFs. Conclusions. Our mediation analysis is a powerful approach in identifying the trans-QTL-phenotype associations. It improves our understanding of the functional genetic variations for the liver metabolism mechanisms

Patterns and severity of vincristine-induced peripheral neuropathy in children with acute lymphoblastic leukemia.

Vincristine, a critical component of combination chemotherapy treatment for pediatric acute lymphoblastic leukemia (ALL), can lead to vincristine‐induced peripheral neuropathy (VIPN). Longitudinal VIPN assessments were obtained over 12 months from newly diagnosed children with ALL (N = 128) aged 1–18 years who received vincristine at one of four academic children's hospitals. VIPN assessments were obtained using the Total Neuropathy Score‐Pediatric Vincristine (TNS©‐PV), National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE©), Balis© grading scale, and Pediatric Neuropathic Pain Scale©–Five (PNPS©‐5). Of children who provided a full TNS©‐PV score, 85/109 (78%) developed VIPN (TNS©‐PV ≥4). Mean TNS©‐PV, grading scale, and pain scores were low. CTCAE©‐derived grades 3 and 4 sensory and motor VIPN occurred in 1.6%/0%, and 1.9%/0% of subjects, respectively. VIPN did not resolve in months 8–12 despite decreasing dose density. VIPN was worse in older children. Partition cluster analysis revealed 2–3 patient clusters; one cluster (n = 14) experienced severe VIPN. In this population, VIPN occurs more commonly than previous research suggests, persists throughout the first year of treatment, and can be severe.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111980/1/jns12114.pd