02460nas a2200433 4500008004100000022001400041245007700055210006900132260001600201300001000217490000800227520120800235653003901443653000901482653002201491653002501513653002801538653001901566653001901585653002001604653003701624653002601661653001101687653001101698653001801709653000901727653002701736653002401763653003001787653003201817653001701849653001301866653002201879653001801901653002701919100002101946700002401967856003501991 2003 eng d a0002-926200aMultiple imputation of baseline data in the cardiovascular health study.0 aMultiple imputation of baseline data in the cardiovascular healt c2003 Jan 01 a74-840 v1573 a
Most epidemiologic studies will encounter missing covariate data. Software packages typically used for analyzing data delete any cases with a missing covariate to perform a complete case analysis. The deletion of cases complicates variable selection when different variables are missing on different cases, reduces power, and creates the potential for bias in the resulting estimates. Recently, software has become available for producing multiple imputations of missing data that account for the between-imputation variability. The implementation of the software to impute missing baseline data in the setting of the Cardiovascular Health Study, a large, observational study, is described. Results of exploratory analyses using the imputed data were largely consistent with results using only complete cases, even in a situation where one third of the cases were excluded from the complete case analysis. There were few differences in the exploratory results across three imputations, and the combined results from the multiple imputations were very similar to results from a single imputation. An increase in power was evident and variable selection simplified when using the imputed data sets.
10aAfrican Continental Ancestry Group10aAged10aAged, 80 and over10aAnalysis of Variance10aCardiovascular Diseases10aCause of Death10aCohort Studies10aData Collection10aData Interpretation, Statistical10aEpidemiologic Studies10aFemale10aHumans10aLinear Models10aMale10aMathematical Computing10aModels, Statistical10aPredictive Value of Tests10aProportional Hazards Models10aRisk Factors10aSoftware10aSurvival Analysis10aUnited States10aVentricular Remodeling1 aArnold, Alice, M1 aKronmal, Richard, A uhttps://chs-nhlbi.org/node/717