Self-report measures are extensively used in nursing research. Data derived from such reports can be compromised by the problem of missing data. To help ensure accurate parameter estimates and valid research results, the problem of missing data needs to be appropriately addressed. However, a review of nursing research literature revealed that issues such as the extent and pattern of missingness, and the approach used to handle missing data are seldom reported. The purpose of this article is to provide researchers with a conceptual overview of the issues associated with missing data, procedures used in determining the pattern of missingness, and techniques for handling missing data. The article also highlights the advantages and disadvantages of these techniques, and makes distinctions between data that are missing at the item versus variable levels. Missing data handling techniques addressed in this article include deletion approaches, mean substitution, regression-based imputation, hot-deck imputation, multiple imputation, and maximum likelihood imputation.
The presence of statistical outliers is a shared concern in research. If ignored or improperly handled, outliers have the potential to distort the estimate of the parameter of interest and thus compromise the generalizability of research findings. A variety of statistical techniques are available to assist researchers with the identification and management of outlier cases. The purpose of this paper is to provide a conceptual overview of univariate outliers with special focus on common techniques used to detect and manage univariate outliers. Specifically, this paper discusses the use of histograms, boxplots, interquartile range, and z-score analysis as common univariate outlier identification techniques. The paper also discusses the outlier management techniques of deletion, substitution, and transformation.
Background:The increasing use of vancomycin to treat methicillinresistant Staphylococcus aureus (MRSA) has resulted in reduced susceptibility of MRSA to this drug. It is important to optimize vancomycin dosing in patients who are undergoing hemodialysis to attain a pre-hemodialysis serum concentration sufficient to eradicate MRSA, in accordance with recent guideline recommendations.Objectives: To establish the optimal strategy for vancomycin loading dose in patients undergoing hemodialysis and to explore the determinants of pre-hemodialysis serum concentration of vancomycin measured in these patients. Methods:A prospective observational cohort study was conducted between January and June 2010. Eligible participants were adults with established stage 5 chronic kidney disease who were undergoing inpatient hemodialysis. Data were collected on loading dose administered, body weight, serum concentration of vancomycin before the subsequent hemodialysis session (pre-hemodialysis concentration), and time between end of vancomycin infusion and measurement of pre-hemodialysis serum concentration. Multivariate stepwise linear regression was performed to examine independent associations between variables and measured pre-hemodialysis serum concentration of vancomycin.Results: Eighty-one patients were included in the study. Of 24 patients who achieved the recommended pre-hemodialysis serum concentration of vancomycin (15-20 mg/L), 14 had a loading dose between 15 and 20 mg/kg. Further analysis suggested that the pre-hemodialysis serum concentration of vancomycin was independently associated with weightbased loading dose (mg/kg) (ß = 0.293, p = 0.003), age (ß = -0.358, p < 0.001), and time between administration of the loading dose and initiation of hemodialysis (ß = -0.247, p = 0.011). Conclusions:The findings of this study indicate that a loading dose of 15-20 mg/kg (actual body weight) is likely to yield an optimal pre-hemodialysis serum concentration at a median elapsed time of 24 h. In addition to loading dose, patient age and time between administration of the loading dose and initiation of hemodialysis also influenced the pre-hemodialysis serum concentration of the drug.
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