| Title |
Survival analysis and regression models
|
|---|---|
| Published in |
Journal of Nuclear Cardiology, May 2014
|
| DOI | 10.1007/s12350-014-9908-2 |
| Pubmed ID | |
| Authors |
Brandon George, Samantha Seals, Inmaculada Aban |
| Abstract |
Time-to-event outcomes are common in medical research as they offer more information than simply whether or not an event occurred. To handle these outcomes, as well as censored observations where the event was not observed during follow-up, survival analysis methods should be used. Kaplan-Meier estimation can be used to create graphs of the observed survival curves, while the log-rank test can be used to compare curves from different groups. If it is desired to test continuous predictors or to test multiple covariates at once, survival regression models such as the Cox model or the accelerated failure time model (AFT) should be used. The choice of model should depend on whether or not the assumption of the model (proportional hazards for the Cox model, a parametric distribution of the event times for the AFT model) is met. The goal of this paper is to review basic concepts of survival analysis. Discussions relating the Cox model and the AFT model will be provided. The use and interpretation of the survival methods model are illustrated using an artificially simulated dataset. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 67% |
| Colombia | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 484 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | <1% |
| Germany | 1 | <1% |
| Unknown | 482 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 79 | 16% |
| Student > Master | 75 | 15% |
| Researcher | 57 | 12% |
| Student > Bachelor | 44 | 9% |
| Student > Doctoral Student | 25 | 5% |
| Other | 70 | 14% |
| Unknown | 134 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 87 | 18% |
| Agricultural and Biological Sciences | 35 | 7% |
| Mathematics | 30 | 6% |
| Engineering | 24 | 5% |
| Biochemistry, Genetics and Molecular Biology | 21 | 4% |
| Other | 132 | 27% |
| Unknown | 155 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 April 2024.
All research outputs
#3,514,632
of 25,918,104 outputs
Outputs from Journal of Nuclear Cardiology
#153
of 2,052 outputs
Outputs of similar age
#33,714
of 244,869 outputs
Outputs of similar age from Journal of Nuclear Cardiology
#3
of 23 outputs
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We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.