Adebowale Olusola Adejumo

The comparison of survival among patients with breast cancer was analyzed using the Cox proportional hazards model and the Kaplan-Meier estimator to model the survival outcomes. A sample of 563 patients was selected to evaluate the impact of risk factors such as age, tumor stage, type of surgery, chemotherapy, radiotherapy, and hormone therapy. The log-rank test results indicated that radiotherapy (p = 0.8) and age (p = 0.08) did not significantly affect survival distribution. Conversely, tumor stage (p < 0.0001), surgical type (p = 0.0004), chemotherapy (p = 0.04), and hormone therapy (p = 0.0004) showed significant differences in survival outcomes. Cox regression analysis identified tumor stage as the most influential factor, with patients at Stage 2, 3, and 4 exhibiting 1.85 to 3.98 times higher risks of mortality compared to those at Stage 1. Additionally, patients undergoing mastectomy had a 1.32 times higher risk than those receiving breast-conserving surgery (p = 0.0288). The assumptions of proportional hazards were violated for chemotherapy and hormone therapy (p = 0.015 and p = 0.018, respectively), which contributed to the overall significance of the model (p = 0.048). Stratification of these variables confirmed the robustness of the model, with tumor stage and surgical option remaining key factors. These findings underscore the importance of early diagnosis in improving survival outcomes for breast cancer patients.

The purpose of this study is to investigate Nigerian monthly crude oil prices from 2006 to 2023 using the Autoregressive Integrated Moving Average (ARIMA) model in order to provide reliable forecasts for fiscal and economic planning. Crude oil remains central to Nigeria’s economy, and its unstable price patterns have significant implications for government budgeting, revenue generation, and long-term policy design. The study employs monthly price data sourced from the National Bureau of Statistics. Preliminary inspection of the series revealed sharp fluctuations without a clear long-term direction. The Augmented Dickey-Fuller test confirmed that the series was non-stationary at the level but became stationary after first differencing. Autocorrelation and partial autocorrelation plots suggested three possible models: ARIMA (0,1,1), ARIMA (1,1,0), and ARIMA (1,1,1). Model performance was compared using AIC, AICc, and BIC values, and further validated with residual diagnostics. The findings indicate that ARIMA (1,1,0) is the best-fitting model, showing that present changes in oil prices are strongly linked to immediate past changes, which reflects the short-term memory property of the oil market. Forecasts from the model point to moderate price stability around US$90–95 per barrel in the near term, though widening confidence intervals highlight rising uncertainty over longer horizons. The practical implication is that accurate short-term forecasts can guide budgetary and fiscal policies in Nigeria and other oil-dependent economies, while underscoring the importance of diversifying revenue sources to reduce vulnerability to oil price shocks.

Developing new compound distributions which are more flexible than the existing distributions have become the new trend in distribution theory. In this present study, the Lomax distribution was extended using the Gompertz family of distribution, its resulting densities and statistical properties were carefully derived, and the method of maximum likelihood estimation was proposed in estimating the model parameters. A simulation study to assess the performance of the parameters of Gompertz Lomax distribution was provided and an application to real life data was provided to assess the potentials of the newly derived distribution. Excerpt from the analysis indicates that the Gompertz Lomax distribution performed better than the Beta Lomax distribution, Weibull Lomax distribution, and Kumaraswamy Lomax distribution.