Applying a Data Duplication Technique in Linear Regression Analysis of Waiting Time to Pregnancy (original) (raw)
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Determinants of Waiting Time to Third Pregnancy Using Censored Linear Regression
Journal of Biosocial Science, 2002
The intervals between pregnancies have important effects on fertility and maternal and infant health outcomes. This study uses linear regression with censored observation to assess the determinants of the waiting time to third pregnancy. The analysis is applied to data from the Second Malaysian Family Life Survey consisting of 1172 women who had their second delivery ending in a live birth. Contraceptive use, age of the woman, duration of breast-feeding, length of previous pregnancy interval and education of the woman all affect the waiting time to third pregnancy significantly.
Dhaka University Journal of Science, 2015
The Cox regression model, which is widely used for the analysis of factor effects with censored survival data, makes the assumption of constant hazard ratio. Different methods should be used to deal with non-proportionality of hazards when this assumption is violated. In this study, we use the Extended Cox regression model where time dependent covariate terms with fixed functions of time are considered. Time to first birth for the ever married women after marriage, taken from BDHS 2011 women data is fitted using Extended Cox regression model due to the failure of existence of proportionality assumption. This model performs well as expected compared to Cox regression model. DOI: http://dx.doi.org/10.3329/dujs.v63i1.21764 Dhaka Univ. J. Sci. 63(1): 25-30, 2015 (January)
Journal of Physics: Conference Series, 2018
Survival analysis is a branch of statistics, which is focussed on the analysis of time-to-event data. In multivariate survival analysis, the proportional hazards (PH) is the most popular model in order to analyze the effects of several covariates on the survival time. However, the assumption of constant hazards in PH model is not always satisfied by the data. The violation of the PH assumption leads to the misinterpretation of the estimation results and decreasing the power of the related statistical tests. On the other hand, the accelerated failure time (AFT) models do not assume the constant hazards in the survival data as in PH model. The AFT models, moreover, can be used as the alternative to PH model if the constant hazards assumption is violated. The objective of this research was to compare the performance of PH model and the AFT models in analyzing the significant factors affecting the first birth interval (FBI) data in Indonesia. In this work, the discussion was limited to three AFT models which were based on Weibull, exponential, and log-normal distribution. The analysis by using graphical approach and a statistical test showed that the non-proportional hazards exist in the FBI data set. Based on the Akaike information criterion (AIC), the log-normal AFT model was the most appropriate model among the other considered models. Results of the best fitted model (log-normal AFT model) showed that the covariates such as women's educational level, husband's educational level, contraceptive knowledge, access to mass media, wealth index, and employment status were among factors affecting the FBI in Indonesia.
Prognostic factors in birth time: A Survival Analysis
The waiting time between children births are called tempo. This phenomenon plays a crucial role in the child and mother health. The purpose of the present study is determination of factors which influence this variable of interest. A significance level of 5% and power of 80% considered to calculate the required sample size for this cross sectional study. The sample size of 124 women determined using PASS software (ver. 11.0.4). These women randomly selected of married women between 15-45 years old with at least two children, living in Irin village. We consider the waiting time between the first and the second birth in women randomly sampled from Irin village of Tehran province of Iran.The selected mothers had 364 children at the time of study. The average and standard error for the number of children were 2.940 and 1.102 respectively. 59% of the children were girl and the rest were boy.Tempo variable as an index estimated using Toki method. The lowest tempo is for 5th to 6th births (28.5) and highest is for 6th to 7th (60). Cox regression model was used to determine the significant explanatory factors.Birth of child was considered as an event and time between the first and the second event was considered as outcome in this model.According to the fitted Cox regression model, only maternal education and father's occupation were statistically significant at 5% on time to second birth.
Survival Analysis in Modeling the Birth Interval of the First Child in Indonesia
Open Journal of Statistics, 2014
First birth interval is one of the examples of survival data. One of the characteristics of survival data is its observation period that is fully unobservable or censored. Analyzing the censored data using ordinary methods will lead to bias, so that reducing such bias required a certain method called survival analysis. There are two methods used in survival analysis that are parametric and non-parametric method. The objective of this paper is to determine the appropriate method for modeling the birth of the first child. The exponential model with the inclusion of covariates is used as parametric method, considering that the newly married couples tend to have desire for having baby as soon as possible and such desire will be weakened by increasing age of marriage. The data that will be analyzed were taken from the Indonesia Demographic and Health Survey (IDHS) 2012. The result of data analysis shows that the birth of the first child data is not exponentially distributed thus the Cox proportional hazard method is used. Because of the suspicion that disproportional covariate exists, then the proportional hazard test is conducted to show that the covariate of age is not proportional, the generalized Cox proportional method is used, namely Cox extended that allows the inclusion of disproportional covariates. The result of analysis using Cox extended model indicates that the factors affecting the birth of the first child in Indonesia are the area of residence, educational history and its age.
Journal of Health, Population and Nutrition, 2015
Background: Caesarean delivery (C-section) rates have been increasing dramatically in the past decades around the world. This increase has been attributed to multiple factors such as maternal, socio-demographic and institutional factors and is a burning issue of global aspect like in many developed and developing countries. Therefore, this study examines the relationship between mode of delivery and time to event with provider characteristics (i.e., covariates) respectively. Methods: The study is based on a total of 1142 delivery cases from four private and four public hospitals maternity wards. Logistic regression and Cox proportional hazard models were the statistical tools of the present study. Results: The logistic regression of multivariate analysis indicated that the risk of having a previous C-section, prolonged labour, higher educational level, mother age 25 years and above, lower order of birth, length of baby more than 45 cm and irregular intake of balanced diet were significantly predict for C-section. With regard to survival time, using the Cox model, fetal distress, previous C-section, mother's age, age at marriage and order of birth were also the most independent risk factors for C-section. By the forward stepwise selection, the study reveals that the most common factors were previous C-section, mother's age and order of birth in both analysis. As shown in the above results, the study suggests that these factors may influence the health-seeking behaviour of women. Conclusions: Findings suggest that program and policies need to address the increase rate of caesarean delivery in Northern region of Bangladesh. Also, for determinant of risk factors, the result of Akaike Information Criterion (AIC) indicated that logistic model is an efficient model.
Determinants of Time to First Birth among women in Ethiopia using Cox Proportional Hazards Model
2022
Background The age of first birth corresponds to the age of the mother giving birth to the first child. The study aims at access the determinants of timing to age at first birth among Ethiopian women. Methods The data for this study was extracted from the published reports of Ethiopian Demographic and Health Survey. The study used15, 683 women aged 15–49 years from nine regions and two city administrations. Cox Proportional hazards model was used for identifying factors associated with age at first birth. Results The median time of age at first birth for Ethiopian women was22 years with 95% CI; (21.82, 22.18). Multivariable Cox Proportional Hazards Model shows that region, place of residence, education, wealth index, religion, work status, age at first marriage, age at first sex, and use of contraceptives have significant effects on the age at first birth at 5% level of significance. From region category, Amhara region (p-value = 0.398), Benishangul Gumuz(p-value = 0.112) Region, an...
Determinants of Second Child Birth Interval Among Women of Lusaka Province: A Cox Regression Model
BackgroundChild birth intervalis the length of time between two successful live births.Shorter child birth interval among women of reproductive age is a serious global public health challenge. It is associated with low birth weight, child malnutrition, and child mortality.There is a general dearth of literature on this subject in Zambia. Therefore, the aim of this study was to establish the determinants of second child birth interval among women.MethodsA cross-sectional study involving 100 women of reproductiveage group in Lusaka province. The participants were purposively and convenientlyselected. A pre tested structured questionnaire was used to collect the data. The key variables in the data were age, marital status, educational level, tribe, income. The data from the questionnaires were summarized into a Microsoft excel sheet, and cleaned for errors and duplications.Thereafter the excel sheet was exported to Stata version 14.2 for analysis.Kaplan-Meier and Cox regression analysi...
Jurnal Biometrika dan Kependudukan
The age of a woman when giving birth to her first child needs to be a concern because it is related to the safety of the mother and baby. A woman being too young or too old increases the risk of death for both the mother and baby. Every woman giving birth for the first time is likely to experience psychological disorders such as anxiety and excessive fear during labor, and even postpartum depression. Given the importance and possible extent of the consequences of women giving birth for the first time, this study intended to assess the factors that influence the age at first birth, especially amongst women of childbearing age in East Java. These factors include the age at first marriage, education, and region. The method used was the extended Cox regression model. The analysis shows that the age at first marriage and education are factors that significantly influence the age at first birth. The more mature the age at first marriage, the more mature the age at first birth. Likewise, t...
Parametric Survival Modeling of Maternal Obstetric Risk; a Censored Study
American Journal of Mathematics and Statistics, 2019
The world mortality rate has declined 45% since 1990, but still 800 women die every day from pregnancy or childbirth related causes. According to the United Nations Population Fund (UNFPA) this is equivalent to about one woman every two minutes and for every woman who dies, 20 or 30 encounter complications with serious or long-lasting consequences. Young mothers face higher risks of complications and death during pregnancy than older mothers, especially adolescents aged 15 years or younger. Adolescents have higher risks for postpartum hemorrhage, thus, an increased risk of death during pregnancy or childbirth compared with older women. The study is therefore focused on addressing the issue of good statistical estimators, is born out of the weaknesses of the estimators in use, and the apparent lack of research into the application of other methods. Survival analysis technique was employed, once the survival function has been developed, various tests and the modeling of Maternal Mortality, as well as the determination of the appropriate distributions that best describes maternal mortality was done, using both the parametric and non-parametric methods. These include the identification of prognostic factors through regression analysis and the determination of an appropriate distribution for maternal survival. Results of its application to data from Oyo State, Nigeria showed that while about 90% of pregnant women made it alive to delivery, only 86% of them survived to the end of the postpartum period. There were significant differentials by location, and Maternal Age: The Weibull distribution described maternal survival well.