Significance and Innovation Sample Clauses

Significance and Innovation. Armitage-Doll multistage model has been successfully employed in the carcinogenesis stud- ies due to its simplicity in predicting cancer mortality rate. It estimates different numbers of stages for various types of cancer. This research is the first effort to use an alternative Bayesian approach in the Armitage-Doll multistage model. One of the advantages of the Bayesian method over the classical method is the ability to formally incorporate prior in- formation. Based on preliminary studies and literature reviews, knowledge of the number of stages could be translated into the prior distribution. Therefore, the posterior estimate derived from the combined information (prior and likelihood) could result in greater pre- cision as compared to the classical estimators. In addition, it is much easier to interpret the confidence interval and probability values under the Bayesian frameworks than classical methods.
Sample 1
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Significance and Innovation. Bayesian APC models have been applied to study various cancer incidence and mortality rates in the UK and United States [Xxxxxxx and Xxxxxxx, 0000, Xxxxxxxx and Xxxxxxx, 0000, Xxxx, 2002]. Different from the classical approaches which make strong parametric assump- tions, Bayesian models improve the precision in estimating the parameters by updating its posterior density from the combination of the prior belief and data [Xxxxxx and Xxxxx, 2009]. In this study, we first introduce multistage carcinogenesis models to the Bayesian APC model. Thus we can incorporate the biological meaning of age effects in the cancer develop- ment to predict cancer incidence and mortality rates while taking period and cohort effects into account as well. Prior settings for number of stages (s) and constant (θ) we described in Chapter 3 are used in determining the prior for age effect. Noninformative priors are assigned to model parameters in the TSCE carcinogenesis model. Inspired by Xxxxxxxx and Xxxxxxx [1994] and Xxxx [2002], we implement a Gaussian autoregressive prior in the for- xxxx direction for cohort and period effects. The use of the autoregressive prior for cohort effects can avoid excessive variability problems in the data caused by few early and late cohorts. The autoregressive prior structure can be viewed as an exchangeable prior model for second differences of period and cohort effects which are all identifiable. An arbitrary linear constraint on the log-linear trend components of APC effects can be imposed to solve the identifiability problem and optimistically will have no effect on the prediction of the model. The introduction of carcinogenesis model can make Bayesian APC model more biological sound in explaining cancer mortality and incidence. Furthermore, the entry of carcinogenesis model into APC model may help reduce the nonidentifiability concerns across the linear relationships in age, period and cohort effects. The Bayesian extended APC model can be used as a tool to estimate the cancer incidence and mortality rates with greater precision and to make more accurate projections for the near future. The estimation and prediction derived from the Bayesian extended APC model can be better used to inform public health policy makers in understanding the trend of cancer incidence and mortality.
Sample 1
Significance and Innovation. The AAPC model provides a general framework to jointly study the evolution in time and the spatial pattern of the risk of disease [Xxxxxxx et al., 2003]. The interaction terms over area can reduce the identifiability burden in the standard APC model [Xxxxxxx and Xxxxxxxxxx, 1987]. Gaussian RW1 and RW2 structures on the age, period and cohort effects can improve model estimation and prediction of future mortality rates [Xxxxxx and Xxxx, 2004, Xxxxx-Xxxx and Xxxxxx, 2001]. Model constraints can be further implemented in the Bayesian framework to handle the identifiability issues in APC models. In this study, I develop a new Bayesian extended AAPC model where multistage carcinogen- esis models are introduced into the AAPC model to incorporate more biological meaning of the age effects in studying the spatio-temporal pattern of cancer mortality rates. The prior means of age effects in the AAPC model are replaced by the log transformation of hazard functions derived from the Armitage-Doll multistage carcinogenesis model and the TSCE model. The proposed extended AAPC model is also compared with the conventional AAPC model where age effects are assigned as RW1 or RW2 priors in fitting cancer mortality data. Model selection procedures (DIC) are implemented to compare the performance of several alternative models.
Sample 1

Related to Significance and Innovation

  • Research, Science and Technology Cooperation 1. The aims of cooperation in research, science and technology, carried out in the mutual interest of the Parties and in compliance with their policies, will be: (a) to build on existing agreements already in place for cooperation on research, science and technology; (b) to encourage, where appropriate, government agencies, research institutions, universities, private companies and other research organizations in the Parties to conclude direct arrangements in support of cooperative activities, programs or projects within the framework of this Agreement, specially related to trade and commerce; and (c) to focus cooperative activities towards sectors where mutual and complementary interests exist, with special emphasis on information and communication technologies and software development to facilitate trade between the Parties. 2. The Parties will encourage and facilitate, as appropriate, the following activities including, but not limited to:

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  • TECHNOLOGICAL CHANGES 18.07.01 The intent and purpose of the following Articles is to ensure that ample consideration is given to the effect technological change will have upon the job security and conditions of employment of employees as well as the continuing effectiveness of the Company.

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  • CONFIDENTIAL, PROPRIETARY, AND TRADE SECRET INFORMATION AND MATERIALS a. Buyer and Seller shall each keep confidential and protect from unauthorized use and disclosure all (i) confidential, proprietary and/or trade secret information of a Party or third party disclosed by a Party; (ii) software provided under this Contract in source code form or identified as subject to this Article; and (iii) tooling identified as subject to this Article: in each case that is obtained, directly or indirectly, from the other in connection with this Contract or Buyer’s contract with its customer, if any, (collectively referred to as "Proprietary Information and Materials"). Proprietary Information and Materials excludes information that is, as evidenced by competent records provided by the receiving Party, known to the receiving party or lawfully in the public domain, in the same form as disclosed hereunder, disclosed to the receiving Party without restriction by a third party having the right to disclose it, or developed by the receiving Party independently without use of or reference to the disclosing Party’s Proprietary Information and Materials.

  • EMPLOYEE DEVELOPMENT AND TRAINING 1. The State agrees to provide advice and counseling to employees with respect to career advancement opportunities and agency developments which have an impact on their careers.

  • Research Use The Requester agrees that if access is approved, (1) the PI named in the DAR and (2) those named in the “Senior/Key Person Profile” section of the DAR, including the Information Technology Director and any trainee, employee, or contractor1 working on the proposed research project under the direct oversight of these individuals, shall become Approved Users of the requested dataset(s). Research use will occur solely in connection with the approved research project described in the DAR, which includes a 1-2 paragraph description of the proposed research (i.e., a Research Use Statement). Investigators interested in using Cloud Computing for data storage and analysis must request permission to use Cloud Computing in the DAR and identify the Cloud Service Provider (CSP) or providers and/or Private Cloud System (PCS) that they propose to use. They must also submit a Cloud Computing Use Statement as part of the DAR that describes the type of service and how it will be used to carry out the proposed research as described in the Research Use Statement. If the Approved Users plan to collaborate with investigators outside the Requester, the investigators at each external site must submit an independent DAR using the same project title and Research Use Statement, and if using the cloud, Cloud Computing Use Statement. New uses of these data outside those described in the DAR will require submission of a new DAR; modifications to the research project will require submission of an amendment to this application (e.g., adding or deleting Requester Collaborators from the Requester, adding datasets to an approved project). Access to the requested dataset(s) is granted for a period of one (1) year, with the option to renew access or close-out a project at the end of that year. Submitting Investigator(s), or their collaborators, who provided the data or samples used to generate controlled-access datasets subject to the NIH GDS Policy and who have Institutional Review Board (IRB) approval and who meet any other study specific terms of access, are exempt from the limitation on the scope of the research use as defined in the DAR.

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