Arezoo Bagheri, Mahsa Saadati,
Volume 3, Issue 2 (10-2015)
Abstract
Background and Objective: Discriminant analysis and logistic regression are classical methods for classifying data in several studies. However, these models do not lead in valid results due to not meeting all necessary assumptions. The purpose of this study was to classify the number of Children Ever Born (CEB) using decision tree model in order to present an efficient method to classify demographic data.
Methods: In the present study, CART tree model with Gini splitting rule was fitted to classify the number of CEB in fertility behavior of at least once married 15-49 year-old women, in Semnan-2012. 405 women aged 15-49 years old comprised the survey sample.
Results: Women in first and second birth cohorts who had married at an early age had 3 CEB while women who had married at an older age had 2 CEB. Women in third birth cohort who had married at an early age and were employed, had 2 CEB while unemployed women in this cohort whose type of marriages were familial and non-familial had 0 and 1 CEB respectively. Women in the third birth cohort who were married in older age had 1 CEB.
Conclusion: Among important advantages of CART model are the simplicity in interpretation, using distribution-free measures, considering missing data and outliers for construction trees which has increased the usage of this method. Therefore, this method is a suitable way for classifying demographic data in comparison to other classical modeling methods in the conditions that necessary assumptions are not met.
Dr Mahsa Saadati,
Volume 5, Issue 2 (10-2017)
Abstract
Background & Objective: Migration, in any forms and by any motivations or outcomes, as a demographic phenomenon, has various cultural and socio-economic effects on local, regional, national and international levels. On the other hand, fertility plays an important role in health and population studies and researchers have examined its changes and trends in various aspects. The aim of this research was modeling the mean number of children ever born (CEB) for women who have left their cities or villages and migrated to Tehran city using regression tree model.
Methods: Data was obtained from 2% of raw data from the census of 2011 and analyzed by regression tree model. Tree models are nonparametric statistical techniques which do not need complicated and unreachable assumptions of traditional parametric ones and have a considerable accuracy of modeling. These models are associated with simple interpretation of results. Therefore, they have been used by researches in many fields such as social sciences.
Results: Age, educational level, job status, cause of migration, internet use for urban migrant women and age for rural migrant women were assumed as influential covariates in predicting the mean number of CEB.
Conclusion: Regression tree findings revealed that urban migrants who were in higher age groups, lower educational levels, unemployed and have not used internet have had more mean number of CEBs.
Arezou Bagheri, Mahsa Saadati,
Volume 6, Issue 2 (6-2018)
Abstract
Background and objectives: Birth spacing is an important variable for identification of fertility acceleration, total fertility rate, and maternal and fetal health. Therefore, special attention has been paid to this issue by researchers in the fields of medical sciences, health, and population. In addition, proper analysis of this concept is of foremost importance. Application of classical analytical techniques with no attention to their assumptions (e.g., independence of events) is associated with inefficient results. As such, this study aimed to present frailty models as effective models for this analysis.
Methods: Frailty models consider the dependence between unobserved intervals and dispersions by exerting a random impact on the model. Different types of these models include shared, conditional, correlated and time-dependent frailty, each of which along with their applications were presented in the current research using two examples. Results: In practice, the shared frailty model is highly applied due to its simplicity. Nevertheless, since most of the unknown factors affecting the birth spacing are not common between different births, the shared frailty models must be used with caution.
Conclusion: Use of classical statistical methods, such as the Cox proportional hazards model, the important assumption of which is the dependence of events occurred, is not appropriate for the accurate analysis of birth spacing. On the other hand, frailty models consider the correlation between the intervals and are an effective method for analysis of birth spacing, use of which is recommended to researchers in fields of medicine and population.
Mahsa Saadati, Arezoo Bagheri,
Volume 7, Issue 3 (9-2019)
Abstract
Background and objectives: Application of statistical machine learning methods such as ensemble based approaches in survival analysis has been received considerable interest over the past decades in time-to-event data sets. One of these practical methods is survival forests which have been developed in a variety of contexts due to their high precision, non-parametric and non-linear nature. This article aims to evaluate the performance of survival forests by comparing them with Cox-proportional hazards (CPH) model in studying first birth interval (FBI).
Methods: A cross sectional study in 2017 was conducted by the stratified random sampling and a structured questionnaire to gather the information of 610, 15-49-year-old married women in Tehran. Considering some influential covariates on FBI, random survival forest (RSF) and conditional inference forest (CIF) were constructed by bootstrap sampling method (1000 trees) using R-language packages. Then, the best model is used to identify important predictors of FBI by variable importance (VIMP) and minimal depth measures.
Results: According to prediction accuracy results by out-of-bag (OOB) C-index and integrated Brier score (IBS), RSF outperforms CPH and CIF in analyzing FBI (C-index of 0.754 for RSF vs 0.688 for CIF and 0.524 for CPH and IBS of 0.076 for RSF vs 0.086 for CIF and 0.107 for CPH). Woman’s age was the most important predictor on FBI.
Conclusions: Applying suitable method in analyzing FBI assures the results which be used for making policies to overcome decrement in total fertility rate.
