Title: Multiple imputation in the outcome evaluation of psychotherapies of variable duration
Authors : Crameri, Aureliano
Koemeda, Margit
Schulthess, Peter
Tschuschke, Volker
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
Issue Date: 2013
License (according to publishing contract) : Licence according to publishing contract
Type of review: Not specified
Language : English
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.
Departement: Angewandte Psychologie
Organisational Unit: Psychological Institute (PI)
Publication type: Conference Other
URI: https://digitalcollection.zhaw.ch/handle/11475/3448
Appears in Collections:Publikationen Angewandte Psychologie

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