Coding and explanation of independent variables.

Background
Many studies analyze sexual and reproductive event data using descriptive life tables. Survival analysis has better power to estimate factors associated with age at first sex (AFS), but proportional hazards models may not be right model to use. This study used accelerated failure time (AFT) models, restricted Mean Survival time model (RMST) models, with semi and non-parametric methods to assess age at first sex (AFS), factors associated with AFS, and verify underlying assumptions for each analysis.
Methods
Self-reported sexual debut data was used from respondents 15–24 years in eight cross-sectional surveys between 1994–2016, and from adolescents’ survey in an observational community study (2019–2020) in northwest Tanzania. Median AFS was estimated in each survey using non-parametric and parametric models. Cox regression, AFT parametric models (exponential, gamma, generalized gamma, Gompertz, Weibull, log-normal and log-logistic), and RMST were used to estimate and identify factors associated with AFS. The models were compared using Akaike information criterion (AIC) and Bayesian information criterion (BIC), where lower values represent a better model fit.
Results
The results showed that in every survey, the Cox regression model had higher AIC and BIC compared to the other models. Overall, AFT had the best fit in every survey round. The estimated median AFS using the parametric and non-parametric methods were close. In the adolescent survey, log-logistic AFT showed that females and those attending secondary and higher education level had a longer time to first sex (Time ratio (TR) = 1.03; 95% CI: 1.01–1.06, TR = 1.05; 95% CI: 1.02–1.08, respectively) compared to males and those who reported not being in school. Cell phone ownership (TR = 0.94, 95% CI: 0.91–0.96), alcohol consumption (TR = 0.88; 95% CI: 0.84–0.93), and employed adolescents (TR = 0.95, 95% CI: 0.92–0.98) shortened time to first sex.
Conclusion
The AFT model is better than Cox PH model in estimating AFS among the young population.