BackgroundCaesarean section is recommended in situations in which vaginal birth presents a greater likelihood of adverse maternal or perinatal outcomes than normal. However, it is associated with a higher risk of complications, especially when performed without a clear medical indication. Since labour attendants have no standardised clinical method to assist in this decision, statistical tools developed based on multiple labour variables may be an alternative. The objective of this paper was to develop and evaluate the accuracy of models for caesarean section prediction using maternal and foetal characteristics collected at admission and through labour.MethodThis is a secondary analysis of the World Health Organization’s Better Outcomes in Labour Difficulty prospective cohort study in two sub-Saharan African countries. Data were collected from women admitted for labour and childbirth in 13 hospitals in Nigeria as well as Uganda between 2014 and 2015. We applied logistic regression to develop different models to predict caesarean section, based on the time when intrapartum assessment was made. To evaluate discriminatory capacity of the various models, we calculated: area under the curve, diagnostic accuracy, positive predictive value, negative predictive value, sensitivity and specificity.ResultsA total of 8957 pregnant women with 12.67% of caesarean births were used for model development. The model based on labour admission characteristics showed an area under the curve of 78.70%, sensitivity of 63.20%, specificity of 78.68% and accuracy of 76.62%. On the other hand, the models that applied intrapartum assessments performed better, with an area under the curve of 93.66%, sensitivity of 80.12%, specificity of 89.26% and accuracy of 88.03%.ConclusionIt is possible to predict the likelihood of intrapartum caesarean section with high accuracy based on labour characteristics and events. However, the accuracy of this prediction is considerably higher when based on information obtained throughout the course of labour.

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We have considered different estimation procedures for the unknown parameters of the extended exponential geometric distribution. We introduce different types of estimators such as the maximum likelihood, method of moments, modified moments, L-moments, ordinary and weighted least squares, percentile, maximum product of spacings, and minimum distance estimators. The different estimators are compared by using extensive numerical simulations. We discovered that the maximum product of spacings estimator has the smallest mean square errors and mean relative estimates, nearest to one, for both parameters, proving to be the most efficient method compared to other methods. Combining these results with the good properties of the method such as consistency, asymptotic efficiency, normality, and invariance we conclude that the maximum product of spacings estimator is the best one for estimating the parameters of the extended exponential geometric distribution in comparison with its competitors. For the sake of illustration, we apply our proposed methodology in two important data sets, demonstrating that the EEG distribution is a simple alternative to be used for lifetime data.

In this paper, we proposed a mechanistic breast cancer survival model based on the axillary lymph node chain structure, considering lymph nodes as a potential dissemination arrangement. We assume a naive breast cancer treatment protocol consisting of exposing patients first to a chemotherapy treatment on r intervals at k-cycles separated by equal time intervals, and then they proceed to surgery. Our model, different from former ones, accommodates a quantity of contaminated lymph nodes, which is observed during surgery. We assume a generalised negative binomial survival distribution for the unknown number of contaminated lymph nodes after surgery, which, during an unknown period, may potentially propagate the disease. Estimation is based on a maximum likelihood approach. A simulation study assesses the coverage probability of asymptotic confidence intervals when small or moderate samples are considered. A Brazilian breast cancer data illustrate the applicability of our modelling.

Cure fraction is not an easy task to be calculated relating probabilistic estimations to an event. For instance, cancer patients may abandon treatment, be cured, or die due to another illness, causing limitations regarding the information about the odds of cancer cure (related to the patient follow-up) and may mislead the researcher's inference. In this paper, we overcame this limitation and proposed a risk assessment tool related to the lifetime of cancer patients to survival functions to help medical decision-making. Moreover, we proposed a new machine learning algorithm, so-called long-term generalized weighted Lindley (LGWL) distribution, solving the inferential limitation caused by the censored information. Regarding the robustness of this distribution, some mathematical properties are shown and inferential procedures discussed, under the maximum likelihood estimators' perspective. Empirical results used TCGA lung cancer data (but not limited to this cancer type) showing the competitiveness of the proposed distribution to the medical field. The cure-rate is dynamic but quantifiable. For instance, after 14 years of development/spread of lung cancer, the group of patients under the age of 70 had a cure fraction of 32%, while the group of elderly patients presented a cure fraction of 22%, whereas those estimations using the traditional (long-term) Weibull distribution is 31% and 17%. The LGWL returned closer curves to the empirical distribution, then were better adjusted to the adopted data, elucidating the importance of cure-rate fraction in survival models.