Abstract
BackgroundCompeting risks, which are particularly encountered in medical studies, are an important topic of concern, and appropriate analyses must be used for these data. One feature of competing risks is the cumulative incidence function, which is modeled in most studies using non- or semi-parametric methods. However, parametric models are required in some cases to ensure maximum efficiency, and to fit various shapes of hazard function.MethodsWe have used the stable distributions family of Hougaard to propose a new four-parameter distribution by extending a two-parameter log-logistic distribution, and carried out a simulation study to compare the cumulative incidence estimated with this distribution with the estimates obtained using a non-parametric method. To test our approach in a practical application, the model was applied to a set of real data on fertility history.ConclusionsThe results of simulation studies showed that the estimated cumulative incidence function was more accurate than non-parametric estimates in some settings. Analyses of real data indicated that the proposed distribution showed a much better fit to the data than the other distributions tested. Therefore, the new distribution is recommended for practical applications to parameterize the cumulative incidence function in competing risk settings.
Highlights
Competing risks, which are encountered in medical studies, are an important topic of concern, and appropriate analyses must be used for these data
An important quantity in competing risk settings is the cumulative incidence function (CIF), which makes it possible to calculate the probability of a particular event
The results showed that the bias and mean square error (MSE) of the CIF estimates obtained with the four-parameter method for the event of interest at t = 1.25 to t = 2 were smaller than with the Sparling distribution and the non-parametric method
Summary
Competing risks, which are encountered in medical studies, are an important topic of concern, and appropriate analyses must be used for these data. In medical research with time-to-event data, there may be more than one final outcome of interest, and this circumstance can complicate the statistical analysis. In such cases, events other than the desired one(s) are considered as competing risks when their occurrence prevents the event of interest [1,2]. In fertility studies in women, researchers are interested in calculating the cumulative live birth rate in the presence of competing risks over time. Competing events, such as the probability of stillborn fetuses or abortions, can be calculated
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