cll: CLL data
In mipred: Prediction using Multiple Imputation
Description
Usage
Format
Source
References
Description
A dataset containing survival outcome and predictors on 694 patients who
received hematopoietic stem cell transplant.
Usage
1
Format
A data frame with 694 rows and 11 variables. Each row describes the data from a single patient. The below described variables are included in the data file. Missing observations are present in the variables performance status(9%), remission status (6%) and cytogenic abnormality (25%).
- id
record identification number
- age10
age at transplantation
- perfstat
performance status indicated by the Karnofsky Index (four categories)
- remstat
remission status at transplantation (three categories)
- cyto
cytogenetic abnormalities (four categories)
- asct
previous autologous transplantation (two categories)
- donor
donor type (three categories)
- sex_match
patient-donor sex match (four categories)
- cond
conditioning regimen (three categories)
- srv5y
overall survival (OS) up to five years after first allogeneic stem cell transplantation
- srv5y_s
censoring indicator (0=alive at end follow-up, 1=dead)
Source
European Society for Blood and Marrow Transplantation (EBMT). https://www.ebmt.org
References
Please reference the following papers when using this data. Schetelig, J. et al. (2017) Risk factors for treatment failure after allogeneic transplantation of patients with CLL: a report from the European Society for Blood and Marrow Transplantation. Bone Marrow Transplantation, 52, 552-560. Schetelig, J. et al. (2017) Centre characteristics and procedure-related factors have an impact on outcomes of allogeneic transplantation for patients with CLL: a retrospective analysis from the European Society for Blood and Marrow Transplantation (EBMT). British Journal of Haematology, 178, 521-533. Mertens, B.J.A. et al. (2019) Construction and assessment of prediction rules for binary outcome in the presence of missing predictor data using multiple imputation and cross-validation: theoretical perspective and data-based evaluation. Biometrical Journal. See ArXiv for an early version https://arxiv.org/abs/1810.05099. We thank EBMT and DKMS for their work in collecting and preparing the CLL data and for approval to share the data.
mipred documentation built on July 12, 2019, 5:04 p.m.
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Description Usage Format Source References
Description
A dataset containing survival outcome and predictors on 694 patients who received hematopoietic stem cell transplant.
Usage
1 |
Format
A data frame with 694 rows and 11 variables. Each row describes the data from a single patient. The below described variables are included in the data file. Missing observations are present in the variables performance status(9%), remission status (6%) and cytogenic abnormality (25%).
- id
record identification number
- age10
age at transplantation
- perfstat
performance status indicated by the Karnofsky Index (four categories)
- remstat
remission status at transplantation (three categories)
- cyto
cytogenetic abnormalities (four categories)
- asct
previous autologous transplantation (two categories)
- donor
donor type (three categories)
- sex_match
patient-donor sex match (four categories)
- cond
conditioning regimen (three categories)
- srv5y
overall survival (OS) up to five years after first allogeneic stem cell transplantation
- srv5y_s
censoring indicator (0=alive at end follow-up, 1=dead)
Source
European Society for Blood and Marrow Transplantation (EBMT). https://www.ebmt.org
References
Please reference the following papers when using this data. Schetelig, J. et al. (2017) Risk factors for treatment failure after allogeneic transplantation of patients with CLL: a report from the European Society for Blood and Marrow Transplantation. Bone Marrow Transplantation, 52, 552-560. Schetelig, J. et al. (2017) Centre characteristics and procedure-related factors have an impact on outcomes of allogeneic transplantation for patients with CLL: a retrospective analysis from the European Society for Blood and Marrow Transplantation (EBMT). British Journal of Haematology, 178, 521-533. Mertens, B.J.A. et al. (2019) Construction and assessment of prediction rules for binary outcome in the presence of missing predictor data using multiple imputation and cross-validation: theoretical perspective and data-based evaluation. Biometrical Journal. See ArXiv for an early version https://arxiv.org/abs/1810.05099. We thank EBMT and DKMS for their work in collecting and preparing the CLL data and for approval to share the data.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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