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.

Barriers to Accessing Dengue Healthcare: A Multicenter Survey from Dhaka, a Major Dengue Hotspot in Bangladesh

Wed, 08 Jan 2025 00:00:00 +0330

Abstract

Background

Dengue fever in Bangladesh, particularly in Dhaka, faces significant healthcare access barriers. Understanding these barriers is crucial for targeted interventions. Therefore, this study aims to analyze the barriers to accessing dengue healthcare through a multicenter survey in Dhaka, a major dengue hotspot in Bangladesh.

Methods

This cross-sectional study was employed throughout the study. The study was conducted in Dhaka City. this study used two-stage stratified sampling based on hospital type (public/private) and randomly selected 16 hospitals (7 public and 9 private), focusing on patients admitted with dengue. A total of 101 patients comprised the final sample.

Result

The study reveals overall 96.04% of participants perceived dengue as a serious threat. Demographically, the patients mostly lived in urban (85.15%) and varied in education. MANOVA indicates that demographic variables significantly impact access barriers, highlighting age, and education status as influential factors (P-value <0.05).

Conclusion

This study highlights the importance of age and education as key determinants of access barriers in dengue healthcare. Addressing the unique needs of children and older adults, as well as enhancing educational opportunities, could be pivotal in mitigating these barriers.

DeepWei-Cu: A Deep Weibull Network for Cure Fraction Models

Ola Abuelamayem — Sun, 01 Dec 2024 00:00:00 +0330

Introduction: Survival analysis including cure fraction subgroups is heavily used in different fields like economics, engineering and medicine. The main core of the analysis is to understand the relationship between the covariates and the survival function taking into consideration censoring and long-term survival. The analysis can be performed using traditional statistical models or neural networks. Recently, neural network has attracted attention in analyzing lifetime data due to its ability of efficiently estimating the survival function under the existence of complex covariates. To the best of our knowledge, this is the first time a parametric neural network is introduced to analyze mixture cure fraction models.
Methods: In this paper, we introduce a novel neural network based on mixture cure fraction Weibull loss function.
Results: Alzheimer disease dataset as long as synthetic dataset are used to study the efficiency of the model. We compared the results using goodness of fit methods in both datasets with Weibull regression.
Conclusion: The proposed neural network has the flexibility of analyzing continuous data without discretization. Also, it has the advantage of using Weibull distribution properties. For example, it can analyze data with different hazard rates (monotonically decreasing, monotonically increasing and constant). comparing the results with Weibull regression, the proposed neural network performed better.

Stress-Strength Reliability of Two-Parameter Exponential Distribution Based on Progres- sively Type-II Censored Data

Sajad Rostamian — Sun, 01 Dec 2024 00:00:00 +0330

Introduction: Stress-strength models has achieved considerable attention in recent years due to its applicability in various areas like engineering, quality control, biology, genetics, medicine etc. This paper investigates estimation of the stress-strength reliability parameter in two-parameter exponential distributions under progressively type-II censored samples.
Methods: The maximum likelihood and the best linear unbiased estimates of are obtained, and the Bayes estimates of are computed under the squared error, linear-exponential, and Stein loss functions. Also, confidence intervals of stress-strength reliability such as the bootstrap confidence intervals, highest posterior density credible interval, and confidence interval based on the generalized pivotal quantity are obtained. Results: Using a simulation study, the point estimators and confidence intervals are evaluated and compared. A set of real data is presented for better clarification of the issue.
Conclusion: The results demonstrated that with increasing the sample size, in almost cases the estimated
risk of all the estimators decrease. Also, in almost all cases the Bayes estimator under the linear-exponential
loss function has smaller estimated risk than the other estimators. Based on our simulation, the expected
lengths of all intervals tend to decrease when the sample size increases. Moreover, the highest posterior density confidence intervals are shorter than the others intervals for all the values of P(Y<X).

Mining hypertension predictors using decision tree: Baseline data of Kharameh cohort study

Sun, 01 Dec 2024 00:00:00 +0330

Background: Hypertension is a serious chronic disease and an important risk factor for many health problems. this study aimed to investigate the factors associated with hypertension using a decision-tree algorithm.
Methods: this cross-sectional study was conducted in Kharameh city between 2014-2017 through census. The study included 2510 hypertensive and 7840 non-hypertensive individuals. 70% of the cases were randomly allocated to the training dataset for establishing the decision tree, while the remaining 30% were used as the testing dataset for performance evaluation of the decision-tree. Two models were assessed. In the first model (model I), 15 variables including age, gender, body mass index, years of education, Occupation status, marital status, family history of hypertension, physical activity, total energy, number of meals, salt, oil type, drug use, alcohol use and smoke entered in to the model. in the second model (model II) 16 variables including age, gender, BMI and Blood factors as HCT, MCHC, PLT, FBS, BUN, CERAT, TG, CHOL, ALP, HDL, GGT, LDL and SG were considered. a receiver operating characteristic (ROC) curve was applied to assess the validation of the models.
Results: The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) are important metrics to evaluate the performance of a decision tree model. For model I, the accuracy, sensitivity, specificity and area under the ROC curve (AUC) value were 79.24%, 82.41%, 78.24% and 0.80, respectively. for model II, the corresponding values were 79.50%, 81.03%, 79.02% and 0.80, respectively.
Conclusion: We have suggested a decision tree model to identify the risk factors associated with hypertension. This model can be useful for early screening and improving preventive and curative health services in health promotion.