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- Imputation of missing data in clinical trials + ('''Planning stage of the clinical trial''' … '''Planning stage of the clinical trial'''</br></br>To reduce the risks of missing data in the panning of the clinical trial, statistical analyses should be specified, key data items should be identified, and the procedures to prevent missing data '"`UNIQ--ref-0000056C-QINU`"'.</br></br>'''Analysis stage'''</br></br>There are different strategies to deal with missing data that will depend on the specific clinical trial and type of missing data:</br></br>1. Complete cases analysis could be used when the proportions of missing data are below 5%and the potential impact of the missing data is negligible '"`UNIQ--ref-0000056D-QINU`"'.</br></br>2. Single imputation replaces missing values by a value defined by a certain rule. However, this method ignores the data variation and can potentially introduce bias and should be used with great caution '"`UNIQ--ref-0000056E-QINU`"'.</br></br>3. When the missing data accomplish certain characteristics, multiple imputation may be used to minimize bias . Missing values are replaced by a random sample of plausible values imputations. There are several multiple imputation methodologies that must be chosen according to the variable with missing values '"`UNIQ--ref-0000056F-QINU`"'.</br></br>To conclude, handling missing data validly is an important, yet difficult and complex, task. This theme showed different strategies to handle missing data but always statistical expertise’s advice is needed.</br>'"`UNIQ--references-00000570-QINU`"'eded. '"`UNIQ--references-00000570-QINU`"')
