| Title |
Multistate models on pleural effusion after allogeneic hematopoietic stem cell transplantation
|
|---|---|
| Published in |
Open Access Medical Statistics, April 2017
|
| DOI | 10.2147/oams.s125465 |
| Pubmed ID | |
| Authors |
Joohyoung Lee, Dipenkumar Modi, Hyejeong Jang, Joseph P Uberti, Seongho Kim |
| Abstract |
A multistate model is more complicated than competing risk models and composed of finite number of states and transitions between states. Unlike competing risk models, this model has the ability to assess the effect of occurrence order of time-to-event data. Pleural effusion (PE) is a severe complication that often occurs after allogeneic hematopoietic stem cell transplantation (HSCT). Many patients develop pleural effusion during the first 100 days after allogeneic HSCT and graft-versus-host disease (GVHD) occurs either before or after the development of PE, implying that the occurrence order of PE and GVHD (i.e., PE after GVHD vs. GVHD after PE) would influence on the incidence, risk factors and mortality of pleural effusion. One can use either Cox proportional models or competing risk models to evaluate these values, but neither method is able to incorporate the occurrence order of incidence into the model. To resolve this difficulty, we developed a multistate model describing several possible events and event-related dependences and applied to a retrospective study of 606 patients, including eight covariates. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 1 | 33% |
| Other | 1 | 33% |
| Unknown | 1 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 1 | 33% |
| Engineering | 1 | 33% |
| Unknown | 1 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 18 April 2017.
All research outputs
#15,879,822
of 25,584,565 outputs
Outputs from Open Access Medical Statistics
#4
of 14 outputs
Outputs of similar age
#179,944
of 324,452 outputs
Outputs of similar age from Open Access Medical Statistics
#1
of 1 outputs
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