| Title |
Semiparametric model for semi-competing risks data with application to breast cancer study
|
|---|---|
| Published in |
Lifetime Data Analysis, September 2015
|
| DOI | 10.1007/s10985-015-9344-x |
| Pubmed ID | |
| Authors |
Renke Zhou, Hong Zhu, Melissa Bondy, Jing Ning |
| Abstract |
For many forms of cancer, patients will receive the initial regimen of treatments, then experience cancer progression and eventually die of the disease. Understanding the disease process in patients with cancer is essential in clinical, epidemiological and translational research. One challenge in analyzing such data is that death dependently censors cancer progression (e.g., recurrence), whereas progression does not censor death. We deal with the informative censoring by first selecting a suitable copula model through an exploratory diagnostic approach and then developing an inference procedure to simultaneously estimate the marginal survival function of cancer relapse and an association parameter in the copula model. We show that the proposed estimators possess consistency and weak convergence. We use simulation studies to evaluate the finite sample performance of the proposed method, and illustrate it through an application to data from a study of early stage breast cancer. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 3 | 20% |
| Other | 2 | 13% |
| Student > Doctoral Student | 2 | 13% |
| Researcher | 2 | 13% |
| Student > Ph. D. Student | 2 | 13% |
| Other | 2 | 13% |
| Unknown | 2 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 3 | 20% |
| Mathematics | 2 | 13% |
| Biochemistry, Genetics and Molecular Biology | 1 | 7% |
| Nursing and Health Professions | 1 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 7% |
| Other | 4 | 27% |
| Unknown | 3 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 06 September 2015.
All research outputs
#20,290,425
of 22,826,360 outputs
Outputs from Lifetime Data Analysis
#89
of 120 outputs
Outputs of similar age
#224,555
of 267,371 outputs
Outputs of similar age from Lifetime Data Analysis
#2
of 3 outputs
Altmetric has tracked 22,826,360 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 120 research outputs from this source. They receive a mean Attention Score of 1.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,371 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.