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Longitudinal multiple imputation approaches for body mass index or other variables with very low individual-level variability: the mibmi command in Stata

Overview of attention for article published in BMC Research Notes, January 2017
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  • Good Attention Score compared to outputs of the same age (70th percentile)

Mentioned by

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6 tweeters

Citations

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8 Dimensions

Readers on

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29 Mendeley
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Title
Longitudinal multiple imputation approaches for body mass index or other variables with very low individual-level variability: the mibmi command in Stata
Published in
BMC Research Notes, January 2017
DOI 10.1186/s13104-016-2365-z
Pubmed ID
Authors

Evangelos Kontopantelis, Rosa Parisi, David A. Springate, David Reeves

Abstract

In modern health care systems, the computerization of all aspects of clinical care has led to the development of large data repositories. For example, in the UK, large primary care databases hold millions of electronic medical records, with detailed information on diagnoses, treatments, outcomes and consultations. Careful analyses of these observational datasets of routinely collected data can complement evidence from clinical trials or even answer research questions that cannot been addressed in an experimental setting. However, 'missingness' is a common problem for routinely collected data, especially for biological parameters over time. Absence of complete data for the whole of a individual's study period is a potential bias risk and standard complete-case approaches may lead to biased estimates. However, the structure of the data values makes standard cross-sectional multiple-imputation approaches unsuitable. In this paper we propose and evaluate mibmi, a new command for cleaning and imputing longitudinal body mass index data. The regression-based data cleaning aspects of the algorithm can be useful when researchers analyze messy longitudinal data. Although the multiple imputation algorithm is computationally expensive, it performed similarly or even better to existing alternatives, when interpolating observations. The mibmi algorithm can be a useful tool for analyzing longitudinal body mass index data, or other longitudinal data with very low individual-level variability.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

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 %
Researcher 10 34%
Student > Ph. D. Student 3 10%
Student > Master 3 10%
Professor > Associate Professor 2 7%
Student > Doctoral Student 2 7%
Other 3 10%
Unknown 6 21%
Readers by discipline Count As %
Medicine and Dentistry 6 21%
Social Sciences 3 10%
Nursing and Health Professions 2 7%
Pharmacology, Toxicology and Pharmaceutical Science 2 7%
Biochemistry, Genetics and Molecular Biology 2 7%
Other 4 14%
Unknown 10 34%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 23 January 2017.
All research outputs
#4,032,805
of 15,051,974 outputs
Outputs from BMC Research Notes
#687
of 3,357 outputs
Outputs of similar age
#104,213
of 352,923 outputs
Outputs of similar age from BMC Research Notes
#1
of 2 outputs
Altmetric has tracked 15,051,974 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 3,357 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 79% 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 352,923 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 70% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them