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Objective(s): Divorce as an important social harm has always been the focus of investigators and policymakers as it affects women compared to males. Therefore, considering the importance and prominent role of women in the family and society, the present article aimed to examine the most important determinants of the first marriage survival of divorced women with children. Methods: In a cross-sectional survey conducted by the Civil Registry Organization in 2017-2018, the information of those who referred to the offices of the provincial centers for divorce registration was collected using a questionnaire. Considering the large number of predictors and the ineffectiveness of classic survival analysis methods in big data modeling, the present study investigated the most important factors, including women and their spouses, their families and provincial macro variables, affecting the first marriage survival of 756 women with children using survival forests using R software. Results: Based on the highest value of Harrell's coordination index (0.8412), the lowest mean prediction error (0.0885) and the lowest value of integrated Brier score (0.038), the algorithm of random survival forest with log rank split rule (RSF1) in investigating factors affecting the first marriage survival of these women was more efficient. The findings showed that based on variable importance and minimum depth indicators, the first child’s age was the most important variable in examining their first marriage survival. Conclusions: Since big data are analyzed in many medical and social studies, survival forests can be used as an efficient method to identify the most important predictors and reduce their dimensions, and then use classical survival analysis methods for modeling.

Objective (s): In many medical and epidemiological studies, each person can experience several events one after the other sequentially under some circumstances, known as recurrence events. Broad analytical objectives of recurrent events consist of describing event process in an individual, dispersion of events from one person to another, and determination of relations between constant or time-dependent predictors with event time. So the main purpose of this article is to present and interpret advanced statistical models to analyze such events which have been proposed in recent years. Methods: In this article, variance-corrected models including AG, conditional PWP (PWP-TT and PWP-GT) and marginal WLW models for analyzing recurrent events are presented and compared with each other. Results: When the number of events for each individual are small and the risk of a reoccurrence of an event varies between different events, usually conditional PWP models are used, while marginal WLW and the time-dependent AG models are applied to determine frequency of events. Conclusion: Medical and epidemiology studies should consider various factors such as the number of events, relationship between events, the variability of the effect of factors from one event to another, the biological process, and the correlation structure of data to select the valid recurrent models.

Objective (s): First birth interval has received special attention in public health and demographic investigations due to its implication in fertility, and maternal and child health. Study of ideal first birth interval also is a very important issue since it demonstrates people’s attitude towards fertility. The aim of this study was to investigate the factors, which affect ideal first birth interval among pre- marriage youths. Methods: A sample of 12500 pre-marriage male and female youths in all provinces of Iran who visited pre-marriage counseling centers was selected. The sample selection was based on multistage stratified sampling method. Data were collected in year 2014 using a structured questionnaire. The ideal first birth interval as outcome variable was indicated by univariate survival analysis and stratified Cox model while controlling for some independent covariates. Results: Based on univariate survival analysis, age, ideal numbers of children and employment status were found to have significant effects on the mean of ideal first birth interval in all provinces. However by fitting different stratified Cox models to different groups of provinces, different covariates were found to play significant effect. Conclusion: Although there were different factors for influencing ideal first birth interval in different provinces, the differences were in the same direction. In all provinces, ideal first birth interval decreased by increase in age and university students favored longer ideal first birth interval than other participants. Risk of early childbearing for women, people who lived in rural area, had lower educational levels, had greater ideal number of children and had sex preferences were higher compared to the other groups.