Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although AO has significant performance in various problems, like other algorithms, suffers from certain limitations its search mechanism, such as local optima stagnation and convergence speed. This is general problem that faces almost all which can be solved by enhancing process...

A survival analysis on a data set of 295 early breast cancer patients is performed in this study. A new proportional hazards model, hypertabastic model was applied in the survival analysis. We assume a proportional hazards model, and select two sets of risk factors for death and metastasis for breast cancer patients respectively by using standard variable selection methods. To evaluate the perf...

There are several statistical methods for time-to-event analysis, among which is the Cox proportional hazards model that is most commonly used. However, when the absolute change in risk, instead of the risk ratio, is of primary interest or when the proportional hazard assumption for the Cox proportional hazards model is violated, an additive hazard regression model may be more appropriate. In t...

The use of the Cox proportional hazards regression model is widespread. A key assumption of the model is that of proportional hazards. Analysts frequently test the validity of this assumption using statistical significance testing. However, the statistical power of such assessments is frequently unknown. We used Monte Carlo simulations to estimate the statistical power of two different methods ...

In some cancer clinical studies, researchers have interests to explore the risk factors associated with competing risk outcomes such as recurrence-free survival. We develop a novel recursive partitioning framework on competing risk data for both prognostic and predictive model constructions. We define specific splitting rules, pruning algorithm, and final tree selection algorithm for the compet...

Methods We analyse survival data reconstructed from two trials publications which reported extreme non-proportional hazards (with respective p-values as 9.19E-13 and 1.344E-24 in non-PH Grambsch-Thernau test). We use the Cox proportional hazards model, flexible parametric model and accelerated failure time model to estimate the time-independent hazard ratio, between-arm difference in restricted...

For the final reduced multivariate Cox proportional hazards model presented in Table 3 of the main text, we assessed the proportional hazards assumption via the Schoenfeld residuals. We examined plots of the residuals for each variable against time to check for a non-­‐zero slope, which indicates that the proportional hazards assumption is violated (Hosmer et al. 1999). We then performed statis...

We have developed a prognostic index model for survival data based on an ensemble of artificial neural networks that optimizes directly on the concordance index. Approximations of the c-index are avoided with the use of a genetic algorithm, which does not require gradient information. The model is compared with Cox proportional hazards (COX) and three support vector machine (SVM) models by Van ...

The semi-parametric Cox proportional hazards (PH) regression model was developed by Sir David Cox (Cox 1972) and is by far the most popular model for survival analysis. The model defines a hazard function, which is the rate of an event occurring at any given time, given the observation is still at risk, as a function of the observed covariates. When data consist of independent and identically d...