ArticleEpidemiology Research International2012
First Use of Multiple Imputation with the National Tuberculosis Surveillance System.
Article in Epidemiology Research International, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Do hassles and uplifts trajectories predict mortality? Longitudinal findings from the VA Normative Aging Study.Journal of behavioral medicine · 2016Article
- First population-level effectiveness evaluation of a national programme to prevent HIV transmission from mother to child, South Africa.Journal of epidemiology and community health · 2015Article
- Do hassles mediate between life events and mortality in older men? Longitudinal findings from the VA Normative Aging Study.Experimental gerontology · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
aimsThe purpose of this study was to compare methods for handling missing data in analysis of the National Tuberculosis Surveillance System of the Centers for Disease Control and Prevention. Because of the high rate of missing human immunodeficiency virus (HIV) infection status in this dataset, we used multiple imputation methods to minimize the bias that may result from less sophisticated methods.
methodsWe compared analysis based on multiple imputation methods with analysis based on deleting subjects with missing covariate data from regression analysis (case exclusion), and determined whether the use of increasing numbers of imputed datasets would lead to changes in the estimated association between isoniazid resistance and death.
resultsFollowing multiple imputation, the odds ratio for initial isoniazid resistance and death was 2.07 (95% CI 1.30, 3.29); with case exclusion, this odds ratio decreased to 1.53 (95% CI 0.83, 2.83). The use of more than 5 imputed datasets did not substantively change the results.
conclusionsOur experience with the National Tuberculosis Surveillance System dataset supports the use of multiple imputation methods in epidemiologic analysis, but also demonstrates that close attention should be paid to the potential impact of missing covariates at each step of the analysis.
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Registered trials
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