| Title |
Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys
|
|---|---|
| Published in |
BMC Medical Research Methodology, March 2012
|
| DOI | 10.1186/1471-2288-12-24 |
| Pubmed ID | |
| Authors |
Helena Marti, Laure Carcaillon, Michel Chavance |
| Abstract |
The weighted estimators generally used for analyzing case-cohort studies are not fully efficient and naive estimates of the predictive ability of a model from case-cohort data depend on the subcohort size. However, case-cohort studies represent a special type of incomplete data, and methods for analyzing incomplete data should be appropriate, in particular multiple imputation (MI). |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 6% |
| Germany | 1 | 3% |
| Unknown | 31 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 29% |
| Student > Ph. D. Student | 6 | 18% |
| Student > Master | 5 | 15% |
| Student > Doctoral Student | 4 | 12% |
| Professor > Associate Professor | 2 | 6% |
| Other | 3 | 9% |
| Unknown | 4 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 16 | 47% |
| Mathematics | 5 | 15% |
| Psychology | 2 | 6% |
| Nursing and Health Professions | 2 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 5 | 15% |
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 12 March 2012.
All research outputs
#15,242,707
of 22,663,969 outputs
Outputs from BMC Medical Research Methodology
#1,499
of 2,000 outputs
Outputs of similar age
#100,042
of 156,320 outputs
Outputs of similar age from BMC Medical Research Methodology
#21
of 39 outputs
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