Volume 6, Issue 2 (6-2018)
Jorjani Biomed J 2018, 6(2): 48-59 |
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Bagheri A, Saadati M. Analysis of Birth Spacing Using Frailty Models. Jorjani Biomed J 2018; 6 (2) :48-59
URL: http://goums.ac.ir/jorjanijournal/article-1-612-en.html
URL: http://goums.ac.ir/jorjanijournal/article-1-612-en.html
1- National Population Studies & Comprehensive Management Institute, Tehran, Iran
2- National Population Studies & Comprehensive Management Institute, Tehran, Iran , mahsa.saadati@gmail.com
2- National Population Studies & Comprehensive Management Institute, Tehran, Iran , mahsa.saadati@gmail.com
Abstract: (4061 Views)
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.
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.
Type of Article: Original article |
Subject:
Bio-statistics
Received: 2017/08/28 | Accepted: 2018/09/18 | Published: 2018/09/18
Received: 2017/08/28 | Accepted: 2018/09/18 | Published: 2018/09/18
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