Journal of Biostatistics and Epidemiology

Longitudinal data clustering methods: A Systematic Review

2024-11-06T06:53:31+0330

In the last few decades, in many research fields, different methods were introduced to discover groups with the same trends in longitudinal data. The clustering process is an unsupervised learning method, which classifies longitudinal data based on different criteria by performing algorithms. The current study was performed with the aim of reviewing various methods of longitudinal data clustering, including two general categories of non-parametric methods and model-based methods. PubMed, SCOPUS, ISI, Ovid, and Google Scholar were searched between 2000 and 2021. According to our systematic review, the non-parametric k-means Clustering Method utilizing Euclidean distance emerges as a leading approach for clustering longitudinal data This research, with an overview of the studies done in the field of clustering, can help researchers as a toolbox to choose various methods of longitudinal data clustering in idea generation and choosing the appropriate method in the classification and analysis of longitudinal data.

Studying the Relationship between the Teaching Status of Research Methods, Biostatistics Courses and, Academic Research Skills, from the Postgraduate Students' Point of View

2024-10-15T09:52:43+0330

Background: This study aimed to study the relationship between the teaching status of Research Methods as well as Biostatistics and academic research skills, from the postgraduate students' point of view.

Methods: This cross-sectional study, conducted in 2022, examined 633 postgraduate students in Iran University of Medical Sciences in 2016-2022. The data included demographic and educational-research information, such as the teaching Biostatistics and course credit, its score, the duration of writing the thesis, and its score. Data analysis was done using univariate and multiple logistic regression throughout the odds ratio (OR) in SPSS with a significance level of 0.05.

Results: 47.7% of the graduates passed Biostatistics and 79.8% published paper from the thesis. The proposal writing in Master’s and MPH graduates, in people without a statistical consultant prolonged significantly (OR:1.67, P=0.017), as well as in PhD graduates, who have passed Research Methods, (OR=2.94, P=0.039), and in clinical graduates, in those who did not receive a methodologist advise (OR=2.7, P=0.024). In Master’s and MPH students, who had passed the Biostatistics or Research Method experienced a longer duration in thesis writing (OR=1.77 and 1.38, respectively).

Conclusion: Passing the Biostatistics and Research Methods, and the presence of a statistical consultant or methodologist in the thesis of the graduates, shortened the time of writing the proposal or the thesis, and also caused them to get a better score in the thesis.

CRP, PCT, and D-dimer as Biomarkers for Disease Severity in COVID-19 Patients: A Retrospective Study in Kinshasa, Democratic Republic of Congo

2024-10-15T09:52:42+0330

Abstract

Background: The COVID-19 pandemic has had a significant impact on global health, resulting in more than 6 million reported deaths worldwide as of April 2023. This study aimed to investigate the potential of C-reactive protein (CRP), procalcitonin (PCT), and D-dimer as biomarkers for assessing disease severity in COVID-19 patients in Kinshasa, Democratic Republic of Congo.

Methods: A retrospective examination was conducted involving 339 COVID-19 patients admitted to Kinshasa hospitals between January 2021 and March 2022. CRP, PCT, and D-dimer levels were measured in all patients and compared between those with severe and non-severe illnesses.

Results: Our findings revealed significantly higher CRP, PCT, and D-dimer levels in severe cases compared to non-severe cases. Specifically, the median CRP level was 120.6 mg/L in severe cases, 47.3 mg/L in mild cases, and 13.5 mg/L in moderate cases. The median PCT levels were 0.26 ng/mL in severe cases, 0.08 ng/mL in mild cases, and 0.07 ng/L in moderate cases. Additionally, the median D-dimer level was 1836.9 µg/L in severe cases and 597.6 µg/L in mild cases, with a value of 481.1 µg/L in moderate cases. System learning techniques were also employed to predict disease severity based on these biomarkers, achieving an accuracy of 97%.

Conclusion: Our findings suggest that CRP, PCT, and D-dimer serve as valuable biomarkers for identifying severe COVID-19 cases in Kinshasa. Furthermore, the application of machine learning methods can yield accurate predictions of disease severity based on these biomarkers. These biomarkers hold the potential to assist clinicians in informed decision-making regarding patient management and contribute to improved clinical outcomes for COVID-19 patients.

Associations Between Conspiracy Ideation, COVID-19 Conspiracy Ideation with Adherence to Preventive Measures Among Adults in Kuwait: A Cross-Sectional Study

2024-10-15T09:52:41+0330

Background: A lack of knowledge about COVID-19 has led people to believe in conspiracy theories, their origins, and their purposes. These theories influence people’s compliance with preventive strategies and accepting vaccination, thus affecting the overall community health. This study investigated the association between compliance with preventive measures, conspiracy ideation, and COVID-19 conspiracy ideation. Methods: Data from 554 participants ≥18 years were collected using a questionnaire distributed over social media platforms. Associations between compliance with preventive strategies and several covariates were investigated. To quantify/test the effect of belief in conspiracy theory and COVID-19 conspiracy while accounting for other covariates, a multiple logistic regression model was implemented to estimate odds ratios (OR) and their 95% confidence intervals (CI). Results: Participants were mainly males (58.3%), employed (61.2%), and Kuwaiti nationals (79.1%) with a median (IQR) age of 32 (20) years. The prevalence of generic conspiracy ideation, COVID-19 conspiracy, and poor compliance with preventive measures were 33%, 28.3%, and 34.7%, respectively. After adjustment for several covariates, believers in conspiracy theory (aOR=1.97, 95%CI:1.24-3.14), believers in COVID-19 conspiracy (aOR=1.96, 95%CI:1.2– 3.21), compared to none/low believers, were significantly associated with poor compliance with preventive measures. Conclusions: Believers in conspiracy theories and COVID-19 conspiracy theories are significantly more likely to be poorly compliant with preventive measures against COVID-19. This has a negative effect on the community health. Policymakers need to address conspiracy theories on public platforms which will help promote the adaptation of correct public health practices and preventive strategies leading to better health of the community.

New Lifetime Distribution and its Application to Cancer Data

2024-10-15T09:52:41+0330

Introduction: Recently, researchers have introduced new generated families of univariate lifetime distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution or compounding two distributions to get more flexibility in fitting data in different areas such as medical sciences, environmental sciences, and engineering. The addition of parameter(s) has been proven useful in exploring tail properties and for improving the goodness-of-fit of the family of the proposed distributions.

Methods: A new Three-Parameter Weibull-Generalized Gamma (for short, “TWGG”) distribution which provides more flexibility in modeling lifetime data is developed using a two-component mixture of Weibull distribution (with parameters and Generalised Gamma distribution (with parameters . Some of its mathematical properties such as the density function, cumulative distribution function, survival function, hazard rate function, moment generating function, Renyi entropy and order statistics are obtained. The maximum likelihood estimation method was used in estimating the parameters of the proposed distribution and a simulation study is performed to examine the performance of the maximum likelihood estimators of the parameters.

Results: Real life applications of the proposed distribution to two cancer datasets are presented and its fit was compared with the fit attained by some existing lifetime distributions to show how the TWGG distribution works in practice.

Conclusion: The results suggest that the proposed model performed better than its competitors and it’s a useful alternative to the existing models.