|Title:||Multiple imputation in the outcome evaluation of psychotherapies of variable duration|
|Authors :||Crameri, Aureliano|
von Wyl, Agnes
|Conference details:||European Conference on Data Analysis, German Classification Society (GfKl) and the French speaking Classification Society (SFC), Luxembourg, July 10–12, 2013|
|License (according to publishing contract) :||Licence according to publishing contract|
|Type of review:||Not specified|
|Subjects :||Missing data; Outpatient psychotherapy; Multiple Imputation; propensity score|
|Subject (DDC) :||150: Psychology |
500: Natural sciences and mathematics
|Abstract:||Participants who drop out of studies have on average a poorer outcome than completers and therefore should not be ignored in the evaluation of treatment outcome. Multiple imputation is a technique to obtain unbiased statistical results from incomplete data. We developed a strategy to impute complex longitudinal data from psychotherapies of different lengths using functions from the MICE package in R. Missing data were imputed within a multilevel framework with bayesian or bootstrap methods. Data analysis after imputation encompassed both the calculation of pre-post-di erences and the comparison of groups after a propensity score matching. We used a cross-validation to successfully verify the capability of our procedure to discriminate su cently between improved patients and those with a poor outcome.|
|Organisational Unit:||Psychological Institute (PI)|
|Publication type:||Conference Other|
|Appears in Collections:||Publikationen Angewandte Psychologie|
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