K Holakouie Naieni, A Ostovar, A Danesh, S Sadjedinejad, L Ghalichee, Gh Moradi, Ma Mansournia, Ss Hashemi Nazari,
Volume 6, Issue 2 (22 2010)
Abstract
Background and objective: The nested case-control study has become popular as an efficient alternative to the full-cohort design. This study compares the results of a nested case-control analysis approach with the full cohort analysis.
Methods: A cohort of 276 subjects (new cases from a TB registry) was used for this study. Cox Regression model was used for the full cohort analysis. In order to do the nested case-control analysis, for each death, three random controls were selected from those who did not suffer from the outcome at the time of the outcome took place. Case control data was analyzed by the conditional logistic regression model.
Results: Results from both cohort and nested case-control analyses show that treatment group is the only variable that affects on the outcome. Gender, place of residence, and age has no effect on the outcome. For binary exposure variables with trivial effects (e.g. Gender and place of residence), the relative efficiency of nested case-control study design is approximately 75%.
Conclusion: Results of this study show that nested case-control study is not only an easy and cost-effective method for data analysis but also is as robust as cohort analysis in rate ratio and its variance estimation.
K Yazdani, S Nejat, A Rahimi-Movaghar , L Ghalichee, M Khalili,
Volume 10, Issue 4 (Vol 10, No 4 2015)
Abstract
Background and Objectives : In the current age, due to the increasing growth of knowledge and competition therein, evaluation of scientific products by means of scientometric methods has become a very important and necessary subject. Scientometrics, in simple words, is the science of measuring the knowledge. Scientometric indices are divided into three categories: productivity metrics that measure the productivity of the researchers or research groups, impact metrics that assess the quality (or performance) of journals, researchers, or research groups, and hybrid metrics that are used to summarize both productivity and effect metrics in one index. Scientometric studies help policy makers and managers of universities and heads of research centers in allocating budget, creating a balance between the budget and costs, making appointments, and promoting the researchers and institutes, and results in better recognition of weaknesses and strengths of their affiliated groups by evaluation of priorities, perspectives, and capacities. Meanwhile, this new knowledge can help with systematic evaluation and ranking of universities and research centers. This paper provides a review on scientometric concepts and its applications and discussed scientometric indices.
