Universität zu Köln

Mattes, André ORCID: 0000-0002-4821-8012 and Roheger, Mandy ORCID: 0000-0002-6015-3194 (2020). Nothing wrong about change: the adequate choice of the dependent variable and design in prediction of cognitive training success. BMC Medical Research Methodology, 20 (1). LONDON: BMC. ISSN 1471-2288

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Abstract

Background Even though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions. Methods We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) x 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability. Results Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges. Conclusion Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Mattes, André UNSPECIFIED orcid.org/0000-0002-4821-8012 UNSPECIFIED Roheger, Mandy UNSPECIFIED orcid.org/0000-0002-6015-3194 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-308592
DOI:	10.1186/s12874-020-01176-8
Journal or Publication Title:	BMC Medical Research Methodology
Volume:	20
Number:	1
Date:	2020
Publisher:	BMC
Place of Publication:	LONDON
ISSN:	1471-2288
Language:	English
Faculty:	Faculty of Human Sciences
Divisions:	Faculty of Human Sciences > Department Psychologie
Subjects:	Psychology Medical sciences Medicine
Uncontrolled Keywords:	Keywords Language Prognostic research English Simulation study English Methodology English Regression analysis English Cognitive decline English Cognitive training English
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/30859