Obesity, physical inactivity, and sedentary behavior, concomitants of the modern environment, are potentially modifiable breast cancer risk factors. This study investigated the association of anthropometric measurements, physical activity and sedentary behavior, with the risk of incident, invasive breast cancer using a prospective cohort of women enrolled in the Canadian Study of Diet, Lifestyle and Health. Using a case-cohort design, an age-stratified subcohort of 3,320 women was created from 39,532 female participants who returned completed self-administered lifestyle and dietary questionnaires at baseline. A total of 1,097 incident breast cancer cases were identified from the entire cohort via linkage to the Canadian Cancer Registry. Cox regression models, modified to account for the case-cohort design, were used to estimate hazard ratios (HR) and 95 % confidence intervals (CI) for the association between anthropometric characteristics, physical activity, and the risk of breast cancer. Weight gain as an adult was positively associated with risk of post-menopausal breast cancer, with a 6 % increase in risk for every 5 kg gained since age 20 (HR 1.06; 95 % CI 1.01-1.11). Women who exercised more than 30.9 metabolic equivalent task (MET) hours per week had a 21 % decreased risk of breast cancer compared to women who exercised less than 3 MET hours per week (HR 0.79; 95 % CI 0.62-1.00), most evident in pre-menopausal women (HR 0.62; 95 % CI 0.43-0.90). As obesity reaches epidemic proportions and sedentary lifestyles have become more prevalent in modern populations, programs targeting adult weight gain and promoting physical activity may be beneficial with respect to reducing breast cancer morbidity.
Methods to find the optimization solution are fundamental and extremely crucial for scientists to program computational software to solve optimization problems efficiently and for practitioners to use it efficiently. Thus, it is very essential to know about the idea, origin, and usage of these methods. Although the methods have been used for very long time and the theory has been developed too long, most, if not all, of the authors who develop the theory are unknown and the theory has not been stated clearly and systematically. To bridge the gap in the literature in this area and provide academics and practitioners with an overview of the methods, this paper reviews and discusses the four most commonly used methods to find the optimization solution including the bisection, gradient, Newton-Raphson, and secant methods. We first introduce the origin and idea of the methods and develop all the necessary theorems to prove the existence and convergence of the estimate for each method.We then give two examples to illustrate the approaches. Thereafter, we review the literature of the applications of getting the optimization solutions in some important issues and discuss the advantages and disadvantages of each method. We note that all the theorems developed in our paper could be well-known but, so far, we have not seen any book or paper that discusses all the theorems stated in our paper in detail. Thus, we believe the theorems developed in our paper could still have some contributions to the literature. Our review is useful for academics and practitioners in finding the optimization solutions in their studies.
Estimation is used widely in numerous disciplines, including Mathematics, Statistics, Economics, Business, and Decision Sciences, among others. Estimation is a process for determining an approximation, which is a value that can be used for a number of purposes, even if input data are sufficient, incomplete, missing or unsecure. In practice, estimation relates to “using the value of a statistic inferred from a sample to estimate the value of a corresponding population parameterâ€. Estimation is usually separated into two categories, namely point estimation and interval estimation. The main purpose of this paper is to present a universal approach to the theory and practice of three methods in statistical inference to obtain point estimates, namely the moment, maximum likelihood, and Bayesian methods. The paper also discusses the advantages and disadvantages of the three universal approaches in practical applications in Economics, Business and Decision Sciences.
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