Open Access Original Research Article

Modelling the Effects of Mindfulness Based Stress on Breast Cancer Survival Rate among Women in Meru and Nyeri Counties, Kenya, Using Cox Proportional Hazard Model

M. N. Mutwiri, M. M. Muraya, L. K. Gitonga

Asian Journal of Probability and Statistics, Page 1-8
DOI: 10.9734/ajpas/2022/v16i130390

Breast cancer remains the most commonly diagnosed cancer among women, affecting 34 women per every 100,000. This has led to high number of fatalities annually, which need to be mitigated. Establishing alternative conventional therapies such as working on mindfulness-based stress (MBS) may be a good alternative to improve prognosis and survival rate of breast cancer patients. However, there is little information on the effects of MBS factors on breast cancer survival. The objective of this study is to predict the effect of MBS factors on breast cancer survival rate among women in Meru and Nyeri Counties using Cox Proportional Hazard Model. Both primary data and secondary data were used. Primary data was obtained using a structured questionnaire from the breast cancer survivors and the medical practioners, while secondary data was obtained from records at Meru teaching and referral hospital and Nyeri level five hospital for the period 2012 to 2017. The MBS variables included cost burden of treatment, stress on diagnosis, prolonged time taken to access treatment, poor diet, alcohol use, physical activity and lack of awareness. . This study used mixed method research design. Data obtained were analysed using R software. Kaplain-Meier estimators were used to estimate the varying effects of MBS factors on survival rate. Log-rank test was used to perform comparisons of survival curves on the patients’ survival rate considering age. The likelihood ratio test showed that MBS factors are significant in predicting hazard rates ( X2= 66.7, p = 0.0000119). Treatment period, lack of awareness, ease of coping with stress  and observing the right diet  were also found to significantly (p < 0.05) affect breast cancer survival rate. Access of treatment immediately after diagnosis, availing the right information to the patients, helping patients to cope easily with stress and observing the right diet were found to be the best estimators in increasing breast cancer survival rate. The study showed the importance of using model in predicting breast cancer survival rates, which can greatly improve breast cancer prognosis.

Open Access Original Research Article

Comparative Performance of Simple Exponential Smoothing, Brown’s Linear Trend and ARIMA Model on Forecasting Neonatal Mortality Rate in Nigeria

Christogonus Ifeanyichukwu Ugoh, Nneka Chidinma Nwabueze, Nwabueze Achunam Simeon, Eze Theophine Chinaza, Okafor Chinasa Ogedi

Asian Journal of Probability and Statistics, Page 9-19
DOI: 10.9734/ajpas/2022/v16i130391

Paper proposes an appropriate time series model that is used to forecast the NMR in Nigeria. The data used for the study is sourced from the World Bank for a period of 1980-2019. The ARIMA model and Exponential Smoothing are fitted on the raw data. The Bayesian Information Criterion (BIC) is adopted to assess the adequacy of the ARIMA models. The NMR series is stationary after the second differencing. The ARIMA (0,2,0) with BIC value of -3.358 is considered the appropriate model among other ARIMA models, and it is compared to SES and Brown’s LT using Theil’s U Statistics and MAPE. The results showed that the Brown’s LT model is more ideal and adequate for forecasting NMR in Nigeria based on the Theil’s U forecast accuracy measures of 0.001911, and that by 2030, Nigeria will have a reduced NMR of 31.5 deaths per 1,000 live births, which shows a drop to 21.5%.

Open Access Original Research Article

A Statistical Study of Lead-Lag Relationship between BCG and DPT Vaccinations in Anambra State: A Cross Spectrum Analysis

Eke Charles Ngome

Asian Journal of Probability and Statistics, Page 20-29
DOI: 10.9734/ajpas/2022/v16i130392

This study examined the lead-lag relationship between between Bacille Calmette-Guerin (BCG) and Diphtheria, Pertussis and Tetanus (DPT) vaccinations in Anambra State using cross spectrum analysis with BCG as the input series. The monthly data on the number of vaccinations for BCG and DPT were collected from Expanded Programmes on Immunization office in Awka, Anambra State for the period of January, 2012 to December, 2020. The Fisher’s Kappa and Bartlett Komolgorov-Smirnov white noise tests showed that both series were white noise. The series were differenced stationary using Augumented Dickey-fuller test. The coherence squared presented a strong relationship between BCG and DPT vaccinations, though it did not show consistent pattern of relationship, however identified pattern of relationship existed at both lower and higher frequencies. In addition, the phase showed that at lower frequencies BCG led DPT, while at higher frequencies BCG lagged DPT. The analysis showed that there is a high awareness for both vaccinations in the State and it should be sustained by the state government.

Open Access Original Research Article

Application of Autoregressive Integrated Moving Average Model and Weighted Markov Chains on Forecasting Under-Five Mortality Rates in Nigeria

Christogonus Ifeanyichukwu Ugoh, Osuji George Amaeze, Nwankwo Chike Henry, Nneka Chidinma Nwabueze, Anabike Charles Ifeanyi, Muoneke Izuchukwu Godson

Asian Journal of Probability and Statistics, Page 30-43
DOI: 10.9734/ajpas/2022/v16i130393

The aim of this paper is to obtain the best model that will be used to predict Under-Five Mortality Rate (U5MR) between Autoregressive Integrated Moving Average (ARIMA) model and Weighted Markov Chains (WMC). The annual dataset of U5MR in Nigeria for the period 1980-2019 is obtained from the official website of World Bank. The descriptive statistics and the unit root test for the stationarity of data were carried on the data series. ARIMA was modelled to U5MR using the techniques of Box-Jenkins while WMC was modelled using the techniques of k-means cluster analysis, Chi-Square, and Correlation. The best ARIMA model was obtained using Bayesian Information Criterion (BIC) while the best forecast model was obtained using Theil’s U Statistics and Mean Absolute Percentage Error (MAPE). U5MR attained stationarity after third differencing under ARIMA model dynamics. ARIMA(0,3,2) is considered the best ARIMA model with BIC of -2.679, and was selected as the best forecast model with Theil’s U Statistic of 0.000014 and MAPE of 0.174336%. The fitted model was used to make out-sample forecast for the period 2020-2030, which showed a steady decline. The findings of this paper will help in establishment and implementation health policies.  

Open Access Original Research Article

Arima-garch Modeling of Monthly Crude Oil Prices Volatility from Nigeria

Eke, Charles Ngome

Asian Journal of Probability and Statistics, Page 44-53
DOI: 10.9734/ajpas/2022/v16i130394

This research work, studied the hybrid of autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity models that best fit monthly crude oil price volatility of Nigeria between January, 2010 to March, 2021. The study collected secondary data from quarterly Central Bank of Nigeria (CBN) Statistical Bulletin, June, 2021 on monthly crude oil price of Nigeria to compute the monthly crude oil price returns. The ARIMA-GARCH modeling was adopted for this work. The series was tested for stationarity using Augmented Dickey Fuller test. Several ARIMA -GARCH models were applied to the monthly crude oil price returns to ascertain the best fit model for the series. The ARIMA (2, 0, 5)-GARCH(1,4) model was selected as the best fit for the data since it has minimum values of Akaike Information Criteria and Mean Squared Errors. The forecasted period showed a crude oil price with an unstable monthly crude oil price returns. Therefore, the government of Nigeria was advised to be conservative when planning with revenue from crude oil sales in future.