PEMODELAN DATA TERSENSOR KANAN MENGGUNAKAN ZERO INFLATED NEGATIVE BINOMIAL DAN HURDLE NEGATIVE BINOMIAL

Health is a very important thing for humanity. One way to look at a person's health condition is through the number of unhealthy days which can also shows the productivity of the community in a region. Modeling the number of unhealthy days which are examples of count data can be done using Poisson regression. Problems that are often faced in data counts are overdispersion and excess zero. Poisson regression cannot be applied to data that experiences both of these. Zero Inflated Negative Binomial and Hurdle Negative Binomial modeling was performed on data with 2 conditions, uncensored and censored. The explanatory variables used are gender, age, marital status, education level, home ownership status and rural-urban status. According to the results of the AIC and RMSE calculation, Zero Inflated Negative Binomial on censored data showed the best performance for estimating the number of unhealthy days.

SF3B1ness score: screeningSF3B1mutation status from over 60,000 transcriptomes based on a machine learning approach

In precision oncology, genomic evidence is used to determine the optimal treatment for each patient. However, identification of somatic mutations from genome sequencing data is often technically difficult and functional significance of somatic mutations is inconclusive in many cases. In this paper, to seek for an alternative approach, we tackle the problem of predicting functional mutations from transcriptome sequencing data. Focusing onSF3B1, a key splicing factor gene, we develop SF3B1ness score for classifying functional mutation status using a combination of Naive Bayes classifier and zero-inflated beta-binomial modeling (R package is available at (https://github.com/friend1WS/SF3B1ness). Using 8,992 TCGA exome and RNA sequencing data for evaluation, we show that the classifier based on SF3B1ness score is able to (1) attain very high precision (>93%) and sensitivity (>95%), (2) rescue several somatic mutations not identified by exome sequence analysis especially due to low variant allele frequencies, and (3) successfully measure functional importance for somatic mutation whose significance has been unknown. Furthermore, to demonstrate that the SF3B1ness score is highly robust and can be extensible to the cohorts outside training data, we performed a functionalSF3B1mutation screening on 51,577 additional transcriptome sequencing data. We have detected 135 samples with putativeSF3B1functional mutations including those that are rarely registered in the somatic mutation database (e.g., G664C, L747W, and R775G). Moreover, we could identify two cases withSF3B1mutations from normal tissues, implying that SF3B1ness score can be used for detecting clonal hematopoiesis.

Alcohol and hypertension: An analysis using The Health Survey for England 2014

Aims: This study aims to model the risk relationships between alcohol consumption and hypertension, as alcohol is likely an important modifiable risk factor in treating hypertension and an important lifestyle variable to be taken into consideration by policy makers and physicians. Design/Participants/Measures: This cross-sectional study uses data from the The Health Survey for England to perform a correlational analysis, as well as multinomial and binomial modeling to evaluate alcohol’s impact on hypertension outcomes, all while controlling for relevant covariates (age, sex, smoking, exercise, body mass index, and education). Findings: Findings indicate that alcohol consumption correlates with blood pressure and hypertension, yet the significance of these findings is weakened by large between-person variability and by confounding factors. Conclusions: Based on these results, for the best cardiovascular health outcomes, we suggest that it is best to err on the side of caution and recommend, regarding alcohol intake, very limited (in the case of healthy patients) to no (for those with hypertension) alcohol consumption.

Impact of traffic Enforcement on Traffic Safety

Motor vehicle crashes (MVCs) have a huge cost to society in terms of death, injury and property damage. The cost of fatal MVCs alone is estimated at US $44 billion per year. Among many confounding factors, traffic citations as an element that may reduce MVC frequency are not well understood, and most research carried out to date has evaluated the effects of the total number of citations on the number of MVCs. However, certain types of citations may be more likely to reduce the number of MVCs, whereas other types are not very effective. This research was set out to examine the impact of different types of traffic citations on MVCs on two hazardous main US highways in Wyoming US-30 and US-26. A negative binomial modeling technique was implemented by exploiting 4 years of crash and citations data to identify the causal impacts of traffic citations on crash frequency by incorporating traffic and geometric features. The modeling results showed that higher numbers of speeding and seat belt citations reduce the number of crashes significantly. These findings are the results of law enforcement efforts along the highways. Traffic count and the number of horizontal curves were found to significantly increase the number of MVCs.