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Conditional survival analysis in Korean patients with gastric cancer undergoing curative gastrectomy

Overview of attention for article published in BMC Cancer, December 2015
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Title
Conditional survival analysis in Korean patients with gastric cancer undergoing curative gastrectomy
Published in
BMC Cancer, December 2015
DOI 10.1186/s12885-015-2022-2
Pubmed ID
Authors

Jin Won Lee, Bandar Ali, Han Mo Yoo, Cho Hyun Park, Kyo Young Song

Abstract

Conditional survival (CS) measures the probability that patients will survive an additional number of years given that they have already survived for a certain period of time. In total, 2935 gastric cancer patients who had undergone curative gastrectomy between 1995 and 2011 were enrolled. The Cox proportional hazard regression model was used to evaluate the factors associated with overall survival (OS). Three-year CS estimates at 't' years after surgery were calculated as follows: CS(t) = S(t + 3)/S(t). The 1-, 2-, 3-, 4- and 5-year OS rates of the 2935 patients were 96.6 %, 92.0 %, 88.7 %, 85.6 and 82.7 %, respectively. The probability of surviving an additional 3 years on the condition of having already survived 1, 2, 3, 4 and 5 years after surgery were 88.6 %, 89.9 %, 91.0 %, 92.2 % and 93.2 %, respectively. Patients with a higher risk at baseline showed a greater increase in CS over time. CS estimates provide important information about dynamic prognostic changes over time for Korean gastric cancer patients, and as such, can be used to guide long-term follow-up strategies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 25%
Lecturer 1 6%
Student > Bachelor 1 6%
Other 1 6%
Professor 1 6%
Other 3 19%
Unknown 5 31%
Readers by discipline Count As %
Medicine and Dentistry 6 38%
Engineering 2 13%
Chemistry 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Unknown 6 38%