| Title |
Horizontal mixture model for competing risks: a method used in waitlisted renal transplant candidates
|
|---|---|
| Published in |
European Journal of Epidemiology, October 2017
|
| DOI | 10.1007/s10654-017-0322-3 |
| Pubmed ID | |
| Authors |
Katy Trébern-Launay, Michèle Kessler, Sahar Bayat-Makoei, Anne-Hélène Quérard, Serge Briançon, Magali Giral, Yohann Foucher |
| Abstract |
When a patient is registered on renal transplant waiting list, she/he expects a clear information on the likelihood of being transplanted. Nevertheless, this event is in competition with death and usual models for competing events are difficult to interpret for non-specialists. We used a horizontal mixture model. Data were extracted from two French dialysis and transplantation registries. The "Ile-de-France" region was used for external validation. The other patients were randomly divided for training and internal validation. Seven variables were associated with decreased long-term probability of transplantation: age over 40 years, comorbidities (diabetes, cardiovascular disease, malignancy), dialysis longer than 1 year before registration and blood groups O or B. We additionally demonstrated longer mean time-to-transplantation for recipients under the age of 50, overweight recipients, recipients with blood group O or B and with pre-transplantation anti-HLA class I or II immunization. Our model can be used to predict the long-term probability of transplantation and the time in dialysis among transplanted patients, two easily interpretable parts. Discriminative capacities were validated on both the internal and external (AUC at 5 years = 0.72, 95% CI from 0.68 to 0.76) validation samples. However, calibration issues were highlighted and illustrated the importance of complete re-estimation of the model for other countries. We illustrated the ease of interpretation of horizontal modelling, which constitutes an alternative to sub-hazard or cause-specific approaches. Nevertheless, it would be useful to test this in practice, for instance by questioning both the physicians and the patients. We believe that this model should also be used in other chronic diseases, for both etiologic and prognostic studies. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 50% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 7 | 24% |
| Student > Ph. D. Student | 6 | 21% |
| Researcher | 3 | 10% |
| Student > Bachelor | 3 | 10% |
| Other | 2 | 7% |
| Other | 2 | 7% |
| Unknown | 6 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 7 | 24% |
| Medicine and Dentistry | 6 | 21% |
| Biochemistry, Genetics and Molecular Biology | 2 | 7% |
| Psychology | 2 | 7% |
| Earth and Planetary Sciences | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 10 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 20 December 2017.
All research outputs
#7,772,772
of 24,321,976 outputs
Outputs from European Journal of Epidemiology
#827
of 1,750 outputs
Outputs of similar age
#121,262
of 332,992 outputs
Outputs of similar age from European Journal of Epidemiology
#20
of 28 outputs
Altmetric has tracked 24,321,976 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,750 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 39.2. This one has gotten more attention than average, scoring higher than 52% of its peers.
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 332,992 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.