Journal of Emergencies, Trauma, and Shock
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ORIGINAL ARTICLE
Year : 2009  |  Volume : 2  |  Issue : 2  |  Page : 73-79

Evaluating the validity of multiple imputation for missing physiological data in the national trauma data bank


1 Department of Epidemiology and Biostatistics. McGill University, Montreal, Quebec, Canada
2 Unité de Traumatologie-Urgence-Soins Intensifs, Centre de recherche du CHA (Hôpital de l'Enfant-Jésus), Université Laval, Quebec City, Quebec, Canada
3 Unité de Traumatologie-Urgence-Soins Intensifs, Centre de recherche du CHA (Hôpital de l'Enfant-Jésus), Université Laval, Quebec City, Quebec; Département d'Anesthésie, Division de Soins Intensifs, Université Laval, Quebec City, Quebec, Canada

Correspondence Address:
Lynne Moore
Department of Epidemiology and Biostatistics. McGill University, Montreal, Quebec
Canada
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0974-2700.44774

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Background: The National Trauma Data Bank (NTDB) is plagued by the problem of missing physiological data. The Glasgow Coma Scale score, Respiratory Rate and Systolic Blood Pressure are an essential part of risk adjustment strategies for trauma system evaluation and clinical research. Missing data on these variables may compromise the feasibility and the validity of trauma group comparisons. Aims: To evaluate the validity of Multiple Imputation (MI) for completing missing physiological data in the National Trauma Data Bank (NTDB), by assessing the impact of MI on 1) frequency distributions, 2) associations with mortality, and 3) risk adjustment. Methods: Analyses were based on 170,956 NTDB observations with complete physiological data (observed data set). Missing physiological data were artificially imposed on this data set and then imputed using MI (MI data set). To assess the impact of MI on risk adjustment, 100 pairs of hospitals were randomly selected with replacement and compared using adjusted Odds Ratios (OR) of mortality. OR generated by the observed data set were then compared to those generated by the MI data set. Results: Frequency distributions and associations with mortality were preserved following MI. The median absolute difference between adjusted OR of mortality generated by the observed data set and by the MI data set was 3.6% (inter-quartile range: 2.4%-6.1%). Conclusions: This study suggests that, provided it is implemented with care, MI of missing physiological data in the NTDB leads to valid frequency distributions, preserves associations with mortality, and does not compromise risk adjustment in inter-hospital comparisons of mortality.


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