2012
DOI: 10.1007/s10461-011-0125-6
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Are We Missing the Importance of Missing Values in HIV Prevention Randomized Clinical Trials? Review and Recommendations

Abstract: Missing data in HIV prevention trials is a common complication to interpreting outcomes. Even a small proportion of missing values in randomized trials can cause bias, inefficiency and loss of power. We examined the extent of missing data and methods in which HIV prevention randomized clinical trials (RCT) have managed missing values. We used a database maintained by the HIV/AIDS Prevention Research Synthesis (PRS) Project at the Centers for Disease Control and Prevention (CDC) to identify related trials for o… Show more

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Cited by 33 publications
(21 citation statements)
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References 80 publications
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“…Missing data can introduce bias into a study if appropriate analytical steps are not taken 50. Among studies that reported how they treated missing data, three excluded participants with missing data,3 40–44 three used modelling techniques that accounted for missing data29 34 35 49 and one used imputation 33.…”
Section: Resultsmentioning
confidence: 99%
“…Missing data can introduce bias into a study if appropriate analytical steps are not taken 50. Among studies that reported how they treated missing data, three excluded participants with missing data,3 40–44 three used modelling techniques that accounted for missing data29 34 35 49 and one used imputation 33.…”
Section: Resultsmentioning
confidence: 99%
“…Among the options available for managing missing values, we selected to analyze all available data because we assume the missingness is not systematic and does not have direct bearing on our statistical models. (30) We chose not to remove participants with incomplete data (i.e., complete case analysis) and we did not impute missing values. For all analyses, groups were compared using logistic regression models with odds ratios and significance defined as p < .05.…”
Section: Methodsmentioning
confidence: 99%
“…Analyses used a complete case approach to missing values (<5% missing on any variable). 16 Results report ORs with 95% CIs.…”
Section: Data Analysesmentioning
confidence: 99%