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Attention Score in Context
| Title |
Multiple imputation for handling missing outcome data when estimating the relative risk
|
|---|---|
| Published in |
BMC Medical Research Methodology, September 2017
|
| DOI | 10.1186/s12874-017-0414-5 |
| Pubmed ID | |
| Authors |
Thomas R. Sullivan, Katherine J. Lee, Philip Ryan, Amy B. Salter |
| Abstract |
Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework. |
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 % |
|---|---|---|
| Venezuela, Bolivarian Republic of | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 75 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 27% |
| Researcher | 14 | 19% |
| Student > Doctoral Student | 6 | 8% |
| Other | 6 | 8% |
| Student > Bachelor | 5 | 7% |
| Other | 12 | 16% |
| Unknown | 12 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 20 | 27% |
| Mathematics | 5 | 7% |
| Computer Science | 5 | 7% |
| Nursing and Health Professions | 4 | 5% |
| Social Sciences | 4 | 5% |
| Other | 21 | 28% |
| Unknown | 16 | 21% |
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 07 September 2017.
All research outputs
#18,801,532
of 23,301,510 outputs
Outputs from BMC Medical Research Methodology
#1,775
of 2,054 outputs
Outputs of similar age
#242,897
of 316,337 outputs
Outputs of similar age from BMC Medical Research Methodology
#24
of 32 outputs
Altmetric has tracked 23,301,510 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,054 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
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