Estimation of spermarche from longitudinal spermaturia data

Area of Science:

• Reproductive biology
• Biostatistics
• Pediatric endocrinology

Background:

• Spermarche, the onset of sperm emission, is a key pubertal milestone.
• Accurate determination of spermarche age is hindered by intermittent sperm presence in urine samples.
• Previous studies faced challenges with sperm-negative samples post-spermarche.

Purpose of the Study:

• To address the challenge of intermittent sperm-positive urine samples after spermarche.
• To develop and illustrate a statistical approach for modeling spermarche onset.
• To analyze longitudinal data on spermarche in a cohort of boys.

Main Methods:

• Application of an empirical Bayes approach to model the probability of sperm-positive urine samples.
• Analysis of longitudinal data from 40 Scottish boys, with urine testing every 3 months.
• Development of methods to handle censored data due to early study withdrawal.

Main Results:

• The empirical Bayes method provides a robust framework for analyzing intermittent data.
• The study successfully modeled the probability of sperm presence after spermarche.
• Longitudinal data analysis allowed for detailed observation of spermarche progression.

Conclusions:

• The empirical Bayes approach is a valuable tool for studying spermarche onset with intermittent data.
• This method improves the accuracy of determining the age at spermarche.
• The findings contribute to a better understanding of male pubertal development.