Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
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Zhonghua Liu Xing Bing Xue Za Zhi · Mar 2014
[Body mass index and cancer incidence:a prospective cohort study in northern China].
To evaluate the association and its strength between body mass index (BMI, kg/m(2)) and cancer incidence in a large-scale population-based cohort study. ⋯ The association between BMI and cancer incidence varied by cancer site. Underweight increased the risk of gastric cancer and liver cancer in males, and obesity increased the risk of colon cancer in males, breast cancer and ovarian cancer in females. However, overweight might played a protective role in lung cancer incidence and bladder cancer incidence in males and obesity might play a protective role in lung cancer incidence in male non-smokers.
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Zhonghua Liu Xing Bing Xue Za Zhi · Mar 2014
[Implication of inverse-probability weighting method in the evaluation of diagnostic test with verification bias].
To evaluate and adjust the verification bias existed in the screening or diagnostic tests. Inverse-probability weighting method was used to adjust the sensitivity and specificity of the diagnostic tests, with an example of cervical cancer screening used to introduce the Compare Tests package in R software which could be implemented. Sensitivity and specificity calculated from the traditional method and maximum likelihood estimation method were compared to the results from Inverse-probability weighting method in the random-sampled example. ⋯ In the analysis of data with randomly missing verification by gold standard, the sensitivity and specificity calculated by traditional method were 90.48% (95%CI:80.74-95.56)and 71.96% (95%CI:68.71-75.00), respectively. The adjusted sensitivity and specificity under the use of Inverse-probability weighting method were 82.25% (95% CI:63.11-92.62) and 85.80% (95% CI: 85.09-86.47), respectively, whereas they were 80.13% (95%CI:66.81-93.46)and 85.80% (95%CI: 84.20-87.41) under the maximum likelihood estimation method. The inverse-probability weighting method could effectively adjust the sensitivity and specificity of a diagnostic test when verification bias existed, especially when complex sampling appeared.