International Journal of Statistics in Medical Research

A Double Truncated Binomial Model to Assess Psychiatric Health through Brief Psychiatric Rating Scale: When is Intervention Useful?

2024-01-18T11:02:30+00:00

A double truncated binomial distribution model with ‘u’ classes truncated on left and ‘v’ classes truncated on right is introduced. Its characteristics, namely, generating functions; and the measures of skewness and kurtosis have been obtained. The unknown parameter has been estimated using the method of maximum likelihood and the method of moments. The confidence interval of the estimate has been obtained through Fisher’s information matrix.

The model is applied on cross sectional data obtained through Brief Psychiatric Rating Scale (BPRS) administered on a group of school going adolescent students; and the above-mentioned characteristics have been evaluated. An expert, on the basis of the BPRS score values, suggested an intervention program. The BPRS scores of the students who could be administered the intervention program lied in a range (which was above the lowest and below the highest possible values) suggested by the expert. Whereas the complete data suggested the average number of problem areas is four (which was not in consonance with the observations given by the expert), the double truncated model suggested the number of such areas as five which was consistent with the observations made by the expert. This establishes the usefulness of double truncated models in such scenarios.

Analysis of Wide Modified Rankin Score Dataset using Markov Chain Monte Carlo Simulation

2024-01-18T11:02:27+00:00

Brain hemorrhage and strokes are serious medical conditions that can have devastating effects on a person's overall well-being and are influenced by several factors. We often encounter such scenarios specially in medical field where a single variable is associated with several other features. Visualizing such datasets with a higher number of features poses a challenge due to their complexity. Additionally, the presence of a strong correlation structure among the features makes it hard to determine the impactful variables with the usual statistical procedure. The present paper deals with analysing real life wide Modified Rankin Score dataset within a Bayesian framework using a logistic regression model by employing Markov chain Monte Carlo simulation. Latterly, multiple covariates in the model are subject to testing against zero in order to simplify the model by utilizing a model comparison tool based on Bayes Information Criterion.

Triglyceridemic Waist Phenotypes as Risk Factors for Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis

2024-02-19T14:23:03+00:00

Introduction: Triglyceride waist phenotypes, which combine high triglyceride levels and central obesity, have recently emerged as an area of interest in metabolic disease research.

Objective: To conduct a systematic review (SR) with meta-analysis to determine if triglyceride waist phenotypes are a risk factor for T2DM.

Materials: SR with meta-analysis of cohort studies. The search was conducted in four databases: PubMed/Medline, Scopus, Web of Science, and EMBASE. Participants were classified into four groups, based on triglyceride level and waist circumference (WC): 1) Normal WC and normalConduct triglyceride level (NWNT); 2) Normal WC and high triglyceride level (NWHT), 3) Altered WC and normal triglyceride level (EWNT) and 4) Altered WC and high triglyceride level (EWHT). For the meta-analysis, only studies whose measure of association were presented as Hazard ratio (HR) along with 95% confidence intervals (CI95%) were used.

Results: Compared to people with NWHT, a statistically significant association was found for those with NWHT (HR: 2.65; CI95% 1.77–3.95), EWNT (HR: 2.54; CI95% 2.05–3.16) and EWHT (HR: 4.41; CI95% 2.82–6.89).

Conclusions: There is a clear association between triglyceride waist phenotypes and diabetes, according to this SR and meta-analysis. Although central obesity and high triglyceride levels are associated with a higher risk of the aforementioned disease, their combination appears to pose an even greater risk. Therefore, in the clinical setting, it is important to consider this when assessing the risk of diabetes.

Adaptive Elastic Net on High-Dimensional Sparse Data with Multicollinearity: Application to Lipomatous Tumor Classification

2024-03-29T11:02:25+00:00

Predictive models can experience instabilities because of the combination of high-dimensional sparse data and multicollinearity problems. The adaptive Least Absolute Shrinkage and Selection Operator (adaptive Lasso) and adaptive elastic net were developed using the adaptive weight on penalty term. These adaptive weights are related to the power order of the estimators. Therefore, we concentrate on the power of adaptive weight on these penalty functions. This study purposed to compare the performances of the power of the adaptive Lasso and adaptive elastic net methods under high-dimensional sparse data with multicollinearity. Moreover, we compared the performances of the ridge, Lasso, elastic net, adaptive Lasso, and adaptive elastic net in terms of the mean of the predicted mean squared error (MPMSE) for the simulation study and the classification accuracy for a real-data application. The results of the simulation and the real-data application showed that the square root of the adaptive elastic net performed best on high-dimensional sparse data with multicollinearity.

The Impact of the Risk Perception of COVID-19 PANDEMIC on College Students' Occupational Anxiety: The Moderating Effect of Career Adaptability

2024-05-21T10:23:41+00:00

In order to understand the changes in college students' risk perception and occupational emotion under major public health events and to explore the influencing factors of college graduates' employment guidance, 578 college students were surveyed by questionnaire to explore the impact of the risk perception of COVID-19 pandemic on college students' occupational anxiety and its internal mechanisms, and to analyze the mediating role of psychological resilience in the impact and the moderating role of career adaptability. The results showed that: (1) there is a significant positive correlation between the risk perception of COVID-19 pandemic and occupational anxiety; there is a significant negative correlation between risk perception and psychological resilience; there is a significant negative correlation between the psychological resilience and occupational anxiety. (2) Psychological resilience plays a mediating role between risk perception and occupational anxiety. (3) Career adaptability plays a negative moderating role between the risk perception of COVID-19 pandemic and occupational anxiety. These results showed that the risk perception of COVID-19 pandemic not only directly aggravates college students' occupational anxiety, but also indirectly affects occupational anxiety through psychological resilience; Career adaptability significantly alleviats the incremental effect of the risk perception of COVID-19 pandemic on college students' occupational anxiety. This paper has positive enlightenment on how to improve the employability of college students and alleviate their employment anxiety during major public health events.

Competing Risks Model to Evaluate Dropout Dynamics Among the Type 1 Diabetes Patients Registered with the Changing Diabetes in Children (CDiC) Program

2024-06-04T11:16:27+00:00

Understanding the survival dynamics of registered patients on a disease control program is a vital issue for the success of program objectives. Dropout of registered patients from such a program is a critical issue, hindering the effectiveness of the program. This study aimed to identify the risk factors of dropout of patients who were registered on the Changing Diabetes in Children (CDiC) program, taking a case of Uganda. Survival analysis was done by integrating competing risk of factors associated with attrition from the CDiC program. The data for the study was obtained from patients with type 1 diabetes mellitus (T1DM) registered during 2009-2018 at health units with specialized pediatric diabetes clinics from various regions in Uganda. The study considered follow-up data of 1132 children with T1DM. Our analysis revealed that the Body Mass Index (BMI) significantly influences dropout time, with patients classified as underweight showing higher hazards than those with normal BMI. Moreover, when considering competing risks, dropout hazards increased. Comparing the Cox model with the Fine and Gray model shows the latter exhibiting a smaller AIC value, which indicates its superiority in the time-to-dropout analysis. Thus, utilizing methods that integrate competing risks for CDiC dropout analysis is preferable and recommended for related studies. These findings provide actionable insights for enhancing CDiC program efficacy.

Automatic Diagnosis of Lung Diseases (Pneumonia, Cancer) with given Reliabilities on the Basis of an Irradiation Images of Patients

2024-06-10T12:11:08+00:00

The article proposes algorithms for the automatic diagnosis of human lung diseases pneumonia and cancer, based on images obtained by radiation irradiation, which allow us to make decisions with the necessary reliability, that is, to restrict the probabilities of making possible errors to a pre-planned level. Since the information obtained from the observation is random, Wald’s sequential analysis method and Constrained Bayesian Method (CBM) of statistical hypothesis testing are used for making a decision, which allow us to restrict both types of possible errors. Both methods have been investigated using statistical simulation and real data, which fully confirmed the correctness of theoretical reasoning and the ability to make decisions with the required reliability using artificial intelligence. The advantage of CBM compared to Wald’s method is shown, which is expressed in the relative scarcity of observation results needed to make a decision with the same reliability. The possibility of implementing the proposed method in modern computerized X-ray equipment due to its simplicity and promptness of decision-making is also shown.

Support of Characteristics, Physical Environmental and Psychological On Quality Of Life Of Patients With DM Type II

2024-07-15T12:55:53+00:00

Background: Diabetes mellitus is one of the diseases that ranks high in the list of non-communicable diseases in Indonesia. Factors that can affect quality of life such as physical environment and psychological.

Objective: This study aims to examine the relationship between characteristic, physical environment and psychological on quality of life of type II diabetes mellitus patients at the Barombong Public Health Center, Makkasar City.

Methodology: This study is a quantitative research with an analytical observational approach using a cross-sectional design. The population size in this study is 578 individuals, sample calculation using the WHO formula yielded a sample size of 385 individuals with predefined exclusion an inclusion criteria. The sampling technique employed is simple random sampling (SRS), and the hypothesis test used is chi-square.

Results: Bivariate statistical analysis shows that there is a relationship between quality of life and age (p=0,000), duration of illness (p=0,000), temperature (p=0,000), noise (p=0,000), positive feelings (p=0,000), thinking, learning, and concentration (p=0,000), self-esteem (p=0,000), while variables that are not associated with quality of life are gender (p=0,111), marital status (p=0,228) and social support (p=0,645). Based on logistic regression analysis, it was found that the factors that most influence quality of life are duration of illness (p=0,000) and positive feelings (p=0,000).

Conclusion: Length of suffering and positive feelings are the most dominant variables associated with quality of life with a probability level of 99.8%.