Journal of Biostatistics and Epidemiology

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

Sachin Kumar — 2025-06-15

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 school pathologies: physical education exemptions for pupils in the Beni Mellal-Khenifra region

Omar BEN RAKAA — 2025-04-23

Context. 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. The objective of this epidemiological study is to ascertain the physical capacity of students with pathologies to engage in physical education and sports (PES) classes. Methods. The methodology employed in this study is based on an analysis of 93,870 medical records. This study is based on a survey conducted in the Beni Mellal-Khénifra region, which included 69 public secondary schools. The data were derived from 93,870 medical records of students aged 13 to 18. These records were analyzed to identify 918 confirmed pathological cases, with a detailed breakdown by gender and urban/rural area. The measurement instrument included the use of international references for the classification of health anomalies, followed by statistical analysis of the data using SPSS version 27 software. Results. The results indicate a notable variation in the prevalence of different diagnosed and confirmed school pathologies, with no discernible difference between the sexes. The majority of cases were found in urban areas (64.38%), and the most dominant age group was 16-18 (59.59%; p < .05). A prevalence of 40.20% of pupils, or 3.93‰ of the diagnosed population, are totally exempt from physical practice in PES, varying according to the types and characteristics of the pathology. Three out of every 1,000 children are unable to participate in physical education at school due to medical restrictions. This phenomenon is not influenced by sex, but varies according to area and age. This indicates a trend of association between urban area and age for physical inactivity. Conclusion. This work underscores the necessity of measuring the physical activity of these students, whether at school or elsewhere, in order to gain a more comprehensive understanding of their physical, social, and mental well-being, as well as their experience of physical education.

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

Fatemeh don't have Atarodi — 2025-04-23

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 — 2025-04-23

Abstract

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

Nima Yousefi — 2025-04-23

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.