2023 CiteScore: 0.8
pISSN: 2383-4196
eISSN: 2383-420X
Editor-in-Chief:
Hojjat Zeraati, PhD.
Vol 9 No 4 (2023)
Articles
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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.
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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.
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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.
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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.
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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.
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Introduction: Functional neurological disorders (FND) is one of the most common causes of neuropathy, However, its cause continues to be mysterious. Understanding the underlying mechanisms of FND is crucial for treatment strategies. The study was conducted on brain images(rs-fMRI) taken from two volunteers (FND patient and healthy subject) who had the same characteristics.
Method: We fitted Gaussian Graphical Models to a single subject data using a network approach.
Results: Based on the results of the networks, the number of significant edges was more in the left hemisphere in the patient, but in the healthy person, the number of these non-zero edges was more in the right hemisphere. Both the networks related to the healthy person and the patient had high density. Therefore, it indicated that the regions considered by these 2 people were strongly related to each other. The results showed the existence of more links and positive relationships between the regions, most of which showed a strong relationship. Among these connections, there were also negative connections. The networks of the healthy participant with almost symmetrical structures and the patient with FND showed different characteristics, including asymmetry between the hemispheres.
Conclusion: this study is the first to demonstrate that the brain regions of both FND patient and healthy participant can be conceptualized as networks. The findings of this study add to a growing body of literature that FND patient brain regions can be analyzed using network approaches.
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Background: Sample sizes that are too small can produce inconclusive results, while sample sizes that are too large may raise ethical concerns and produce trivial outcomes. Ethical considerations of sample size calculation in animal studies are essential and researchers should consider the 3R approach. Therefore, accurately calculating the sample size is essential to ensure adequate statistical power and avoid wastage of resources.
Method: The paper presents several innovative approaches for conducting small-scale sampling in animal studies. It includes a comprehensive review of relevant literature, discussing various proposed methods for determining sample size in animal studies.
Results: In this study, various formulas are available for preparing sample size calculations that are relevant to the research design. These include t-tests (for one sample and two independent/paired samples), ANOVA, ANCOVA, simple/multiple linear regression, as well as proportion studies and studies utilizing correlation coefficients.
Conclusions: Our aims to equip researchers with formulas for reliable findings and adherence to ethical principles for sample size calculating in animal studies.
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Many experts in the field of distribution theory have focused on extending probability distributions utilizing extended families of continuous distributions to improve the modeling adaptability of the conventional probability distributions. This study introduced a brand-new, five-parameter generalized exponentiated exponential distribution, which is a continuous probability distribution. With the aid of the quantile function, moments, moment generating function, survival function, hazard function, mean, and median, among other mathematical and statistical aspects, the new distribution's shape was deduced and researched. It was also possible to derive the probability density function for the minimum and maximum order statistics for this distribution. The method of maximum likelihood estimate was used to produce a conventional estimation of the unknown parameters. A simulation study was carried out to assess the efficiency and consistency of the estimation method used. To evaluate the fit and adaptability of the new model, it was applied to four real-world datasets in the field of medicine. The analysis's findings demonstrated that the new model performs better than its counterparts and offers a better fit than the Topp-Leone exponentiated exponential (TLEtEx), Topp-Leone Kumaraswamy exponential (TLKEx), exponentiated exponential (EtEx), and exponential (Ex) distributions.
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Introduction: Determining socio-economic status (SES) can greatly help decision makers in the field of social health. Because SES can play an important role in accessing medical services or welfare amenities. We aimed to determine SES using Principal Component Analysis (PCA), Multiple Correspondence Analysis (MCA) and Factor Analysis of Mixed Data (FAMD) methods.
Methods & Materials: In this cross-sectional study (2023), 4448 employees aged 19 to 75 years were included to the study from Tehran University of Medical Sciences employees` cohort (TEC). Demographic variables and socio-economic factors were considered. Considering the weaknesses of PCA and MCA methods, we calculated the SES score using PCA, MCA and FAMD methods, and the percentile of people was determined. These weaknesses include normality assumption and considering only linear relationship for PCA, inability to interpret the relationships between variables and considering each level of classification variables as a new variable for MCA
Results: We studied 4448 people (39.3% men) with a mean age of 42.3 and a standard deviation of 8.7. The correlation between the percentiles obtained through PCA, MCA and FAMD methods was very high, and the highest correlation was related to the percentiles obtained through PCA and FAMD methods with a value of 0.994. The intraclass correlation coefficient value was 0.996. Also, this value was 0.996 and 0.994 in the random samples of 250 and 100 individuals from the original data, respectively.
Conclusion: All of the three methods worked similarly on determining the SES and calculating the percentile of people. PCA and FAMD methods had better agreement than others. Therefore, in studies that have both quantitative and qualitative variables, the choice of analysis method depends on the opinion of the researcher.
Keywords: Socio-economic status, Principal Component Analysis, Multiple Correspondence Analysis, Factor Analysis of Mixed Data, cohort study
