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

Abstract

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.

Keywords

• Spline Smoothing
• Truncation Effect
• Selection Bias
• Current Status Data
• Ayer Method