Abstract: Risk ratios or p-values from multiple, independent studies – observational or randomized – can be pooled to address a common research question in meta-analysis. However, reliability of independent studies should not be assumed as claimed risk factor−disease relationships may fail to reproduce. An independent evaluation was undertaken of a published meta-analysis of cohort studies examining diet−disease associations; specifically between red and processed meat and six disease outcomes (all-cause mortality, cardiovascular mortality, all cancer mortality, breast cancer incidence, colorectal cancer incidence, type 2 diabetes incidence). The number of hypotheses examined were counted in 15 random base papers (14%) of 105 used in the meta-analysis. Test statistics (relative risk values with 95% confidence limits) for 125 results used in the meta-analysis were converted to p-values; p-value plots were used to examine the effect heterogeneity of the p-values. The possible number of hypotheses examined in the 15 base papers was large, median = 20,736 (interquartile range = 1,728–331,776). Each p-value plot for selected health effects showed either a random pattern (p-values> 0.05), or a two-component mixture (small p-values < 0.001 while other p-values appeared random). Given potentially large numbers of hypotheses examined in the base studies, questionable research practices cannot be ruled out as explanations for some test statistics with small p-values. Like the original findings of the published meta-analysis, our independent evaluation concludes that base papers used in the meta-analysis do not support evidence for an association between red and processed meat and the six health effects investigated. PubDate: Wed, 27 Apr 2022 16:02:26 +000

Abstract: A new method of approximating the Binomial probability function is introduced. The method is based on the discrete normal distribution. In particular, the discrete normal probability function is used to approximate the binomial probability function. The new approximation is compared with the exact values and the approximation based on Central limit theorem. The maximum absolute error of the approximation is used to measure the accuracy of the method. It turned out that this method of approximation is useful and easy to use in practice. Also, the result can be an important theoretical statistical result that can be used in educational statistics. PubDate: Thu, 21 Apr 2022 12:39:37 +000

Abstract: Countries that suffer disturbances in their power generation are less likely to meet many of the sustainable development goals and general economic growth. This study used a three-variable SVAR model to examine the interactions of water level, crude oil and power generated from the Akosombo hydroelectric generation Dam in Ghana. Data used for this span from January 2010 to December 2019. From the results, none of the three important variables studied was found to be completely independent; dam level and crude oil are adjusted to absorb power generation shocks. All three variables drift away from their normal levels to contain shock before returning to their desired levels at varying time points. It has also been established that Dam water level shocks leads to a negative response in both power generation and crude oil in the short run. Overall, shocks to crude oil explains much of the variability in power generation than shocks to dam water level. These findings convey that there is exist very useful interactions among the three-variables studied in this paper. Policy makers should institute effective measures for early detection and intervention of the short-term power disturbance that characterizes the hydroelectric power generation to ensure a sustainable power and growth of the Ghanaian economy. PubDate: Thu, 21 Apr 2022 11:58:21 +000

Abstract: Bivariate survival cure rate models extend the understanding of time-to-event data by allowing for a cured fraction of the population and dependence between paired units and make more accurate and informative conclusions. In this paper, we propose a Bayesian bivariate cure rate mode where a correlation coefficient is used for the association between bivariate cure rate fractions and a new generalized Farlie Gumbel Morgenstern (FGM) copula function is applied to model the dependence structure of bivariate survival times. For each marginal survival time, we apply a Weibull distribution, a log normal distribution, and a flexible three-parameter generalized extreme value (GEV) distribution to compare their performance. For the survival model fitting, DIC and LPML are used for model comparison. We perform a goodness-of-fit test for the new copula. Finally, we illustrate the performance of the proposed methods in simulated data and real data via Bayesian paradigm. PubDate: Wed, 09 Mar 2022 04:33:18 +000

Abstract: Dependent and independent variables may appear uncorrelated when analyzed in full range in medical data. However, when an independent variable is divided by the cutoff value, the dependent and independent variables may become correlated in each group. Furthermore, researchers often convert independent variables of quantitative data into binary data by cutoff value and perform statistical analysis with the data. Therefore, it is important to select the optimum cutoff value since performing statistical analysis depends on the cutoff value. Our study determines the optimal cutoff value when the data of dependent and independent variables are quantitative. The piecewise linear regression analysis divides an independent variable into two by the cutoff value, and linear regression analysis is performed in each group. However, the piecewise linear regression analysis may not obtain the optimal cutoff value when data follow a non-normal distribution. Unfortunately, medical data often follows a non-normal distribution. We, therefore, performed theWilcoxon-Mann-Whitney (WMW) test with two-sided for all potential cutoff values and adopted the cutoff value that minimizes the P-value (called minimum P-value approach). Calculating the cutoff value using the minimum P-value approach is often used in the log-rank and chi-squared test but not the WMW test. First, using Monte Carlo simulations at various settings, we verified the performance of the cutoff value for the WMW test by the minimum P-value approach. Then, COVID-19 data were analyzed to demonstrate the practical applicability of the cutoff value. PubDate: Wed, 09 Mar 2022 03:28:26 +000