Journal of Biostatistics and Epidemiology

Adjustment of Truncation Effect in First Birth Interval using Current Status Data Technique

Sun, 15 Jun 2025 00:00:00 +0430

Background: Estimating the First Birth Interval (FBI) from cross-sectional data often presents challenges related to truncation effects. These challenges stem from the data’s inability to capture the enough exposure for a event, resulting in potential biases and inaccuracies in FBI estimates. Recognizing and addressing truncation effects is essential for obtaining more precise and meaningful fertility parameter estimates in a cross-sectional survey.

Objective: This study seeks to mitigate truncation effects in the estimation of the FBI by utilizing the Current Status Data Technique. This approach involves focusing on women with specific marital durations, providing a means to counteract the bias caused by truncation and thereby yielding more accurate and reliable FBI estimates.

Methodology: Data from the National Family Health Survey (NFHS-IV) are employed for this study. The Current Status Data Technique is applied to the dataset, considering exclusively those women with marital durations less than 120 months. This methodology enables the adjustment of truncation effects and facilitates a more precise estimation of the FBI. Statistical analysis is conducted to determine the FBI distribution and ascertain the necessary sample size.

Results: The application of the Current Status Data Technique yields an FBI estimate of 30.70 months. To achieve reliable estimations of the FBI using Current Status Techniques, a minimum sample size exceeding ”5000” observations is required.

Conclusion: Truncation effect in FBI is address and some non parametric adjustment is used for estimating the duration of FBI. The Current Status Data Technique emerges as a valuable tool for mitigating these effects and enhancing the precision of FBI estimates. This research contributes to an improved understanding of fertility dynamics and provides valuable insights for future studies on the First Birth Interval.

Epidemiological Study of The Physical Ability to Practice Physical Education in Children with School Pathologies

Omar BEN RAKAA, Mustapha BASSIRI, Said LOTFI — Wed, 23 Apr 2025 00:00:00 +0430

Introduction: The term "school pathologies" encompasses two distinct categories of health disorders: those that are caused or exacerbated by a lack of physical activity, and those that predominantly affect children of school age. This study employs an epidemiological approach to examine the physical aptitude of students in relation to their capacity to engage in physical education and sports (PE) classes. Our approach is based on an analysis of 93,870 medical records.
Methods: The survey is comprised of four distinct sections. The initial stage of the analysis entails an examination of the prevalence of confirmed impairments among school-aged children. Secondly, an evaluation of the physical aptitude to engage in physical education will be conducted. Thirdly, an analysis of the physical inaptitude of students to participate in physical education will be conducted.
Results: The results indicated a range of prevalence rates for various diagnosed and confirmed impairments, though no notable differences were observed between the sexes. Similarly, the majority of respondents attended school in urban areas (64.38%), and the most prevalent age group in this study was 16-18 years (59.59%;p<.05). In contrast, a prevalence of 40.20% of students with SEN (or 3.93‰ of the diagnosed population who are totally unfit for physical practice in PE) has been observed. However, this figure varies according to the types and characteristics of impairment. Three children with one type of impairment out of 1,000 pupils are unfit, which engenders physical inactivity at school due to medical restrictions. This phenomenon is not influenced by gender; however, it differs between geographical areas and age groups. This indicates a correlation between urbanization and age-related changes in physical disability and inactivity.
Conclusion: This study underscores the necessity of monitoring the physical activity of students with SEN, whether at school or elsewhere, to gain a more comprehensive understanding of well-being.

Unraveling Growth: Analyzing the key Factors Influencing growth rate of children under two years

Wed, 23 Apr 2025 00:00:00 +0430

Introduction: According to the significance of children in the culture, economy, and human resources for the country's future, their growth in the first 2 years and its influencing factors are crucial for the country's progress.
Objectives: The study evaluates the determinants that impact the growth of children in their first two years of life.
Methods: This data was collected based on electronic files from 601 children who visited Health Service Centers in North Khorasan province from April 2020 to April 2023. We used longitudinal models (transmission-random-marginal) and SPSS software version 26. We employed the Akaike information criterion, with a significance level of 0.05.
Results: Based on our research, we found that the recommended growth measurements for weight, length, and head circumference for children under 2 years using the longitudinal method. Children's growth was significantly associated with gender, place of residence, mother's education, type of breastfeeding, intrauterine age, singleton birth, and mother's weight (P<0.05). Boys showed better weight growth compared to girls, but the rate of weight and height growth was similar for both genders.
Conclusions: According to the results of this study, adequate training on factors affecting children's growth should be provided to both mothers and health workers, which may reduce the risk of developing disorders.

Deep Neural Network for Cure Fraction Survival Analysis Using Pseudo Values

Ola Abuelamayem — Wed, 23 Apr 2025 00:00:00 +0430

Background:The hidden assumption in most of survival analysis models is the occurrence of the event of interest for all study units. The violation of this assumption occurs in several situations. For example, in medicine, some patients may never have cancer, and some may never face Alzheimer. Ignoring such information and analyzing the data with traditional survival models may lead to misleading results. Analyzing long term survivals can be performed using both traditional and neural networks. There has been an increasing interest in modeling lifetime data using neural network. However, for long-term survivors only one neural network was introduced to estimate the uncured proportion together with the EM algorithm to account for the latency part. Neural network in survival analysis requires special cost function to account for censoring.
Methods: In this paper, we extend the neural network using pseudo values to analyze cure fraction model. It neither requires the use of special cost function nor the EM algorithm.
Results: The network is applied on both synthetic and Melanoma real datasets to evaluate its performance. We compared the results using goodness of fit methods in both datasets with cox proportional model using EM algorithm.
Conclusion: The proposed neural network has the flexibility of analyzing data without parametric assumption or special cost function. Also, it has the advantage of analyzing the data without the need of EM algorithm. Comparing the results with cox proportional model using EM algorithm, the proposed neural network performed better.

Machine Learning Models for Prognostic Assessment of Covid-19 Mortality Using Computed Tomography-Based Radiomics

Wed, 23 Apr 2025 00:00:00 +0430

Introduction: This study examines the significance of developing a predictive approach for assessing the prognosis of individuals diagnosed with COVID-19. This method can help physicians make treatment decisions that decrease mortality and prevent unnecessary treatments. This study also emphasizes the significance of radiomics features. Therefore, our objective was to assess the predictive capabilities of Computed Tomography-based radiomics models using a dataset comprising 577 individuals diagnosed with COVID-19.
Methods: The U-net model was applied to automatically perform whole lung segmentations, extracting 107 texture, intensity, and morphological features. We utilized two feature selectors and three classifiers. We assessed the random forest, logistic regression, and support vector machines by implementing a five-fold cross-validation approach. The precision, sensitivity, specificity, accuracy, F1-score, and area under the receiver operating characteristic curve were reported.
Result: The random forest model achieved an area under the receiver operating characteristic curve, precision, sensitivity, specificity, accuracy, and F1-score in the range of 0.85 (CI95%: 0.76–0.91), 0.75, 0.82, 0.78, 0.68, and 0.71, respectively. Logistic regression attained an area under the receiver operating characteristic curve of 0.80 (CI95%: 0.72–0.88), corresponding to values of 0.88, 0.62, 0.74, 0.55, and 0.67, respectively. Support Vector Machines computes the above six metrics as an area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, precision, and F1-score in the range of 0.69 (CI95%: 0.59–0.79), 0.68, 0.64, 0.66, 0.5, and 0.57, respectively.
Conclusion: We are developing a robust radiomics classifier that accurately predicts mortality in COVID-19 patients. Lung Computed Tomography radiomics features may aid in identifying high-risk individuals who need supplementary therapy and decrease the propagation of the virus.