TY - JOUR
T1 - Identifying change-dropout patterns during an Internet-based intervention for depression by applying the Muthen-Roy model
AU - Arndt, Alice
AU - Lutz, Wolfgang
AU - Rubel, Julian
AU - Berger, Thomas
AU - Meyer, Björn
AU - Schröder, Johanna
AU - Späth, Christina
AU - Hautzinger, Martin
AU - Fuhr, Kristina
AU - Rose, Matthias
AU - Hohagen, Fritz
AU - Klein, Jan Philipp
AU - Moritz, Steffen
N1 - Funding Information:
was provided by the German Federal Ministry of Health, II A 5 - 2512 FSB 052. Additional funding was provided by GAIA AG (Hamburg, Germany). We would also like to thank the EVIDENT study team: Berlin: Matthias Rose (core team member), Sandra Nolte, Anna Paulitschek, Leonie Gm?hling and Leonie Schickedanz; Bern: Thomas Berger (core team member); Bielefeld: Wolfgang Greiner (core team member) and Viola Gr?fe; Hamburg: Bj?rn Meyer (core team member), Steffen Moritz (core team member), Johanna Schr?der (core team member), Mirja Behrens, Cecile Hoermann, Anna J. Katharina Jahns, Thies L?dtke and Eik Vettorazzi; L?beck: Fritz Hohagen (principal investigator), Philipp Klein (core team member), Christina Sp?th (core team member) and Antje Roniger; Trier: Wolfgang Lutz (core team member), Alice Arndt, Julian Rubel, Liv Glindemann, David Rosenbaum and Kathinka Wolter; and T?bingen: Martin Hautzinger (core team member), Flora Bach, Elisabeth Beck, Kristina Fuhr, Katharina Krisch and Melanie Wahl. External advisors: Franz Caspar, Bern; Bernd L?we, Hamburg; and Gerhard Andersson, Link?ping.
Publisher Copyright:
© 2018, © 2018 Swedish Association for Behaviour Therapy.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2020/1/2
Y1 - 2020/1/2
N2 - To date, only few studies have attempted to investigate non-ignorable dropout during Internet-based interventions by applying an NMAR model, which includes missing data indicators in its equations. Here, the Muthen-Roy model was used to investigate change and dropout patterns in a sample of patients with mild-to-moderate depression symptoms (N = 483) who were randomized to a 12-week Internet-based intervention (deprexis, identifier: NCT01636752). Participants completed the PHQ-9 biweekly during the treatment. We identified four change-dropout patterns: Participants showing high impairment, improvement and low dropout probability (C3, N = 134) had the highest rate of reliable change at 6- and 12-month follow-up. A further pattern was characterized by high impairment, deterioration and high dropout probability (C2, N = 32), another by low impairment, improvement and high dropout probability (C1, N = 198). The last pattern was characterized by high impairment, no change and low dropout probability (C4, N = 119). In addition to deterioration, also rapid improvement may lead to dropout as a result of a perceived “good enough” dosage of treatment. This knowledge may strengthen sensitivity for the mechanisms of dropout and help to consider its meaning in efforts to optimize treatment selection.
AB - To date, only few studies have attempted to investigate non-ignorable dropout during Internet-based interventions by applying an NMAR model, which includes missing data indicators in its equations. Here, the Muthen-Roy model was used to investigate change and dropout patterns in a sample of patients with mild-to-moderate depression symptoms (N = 483) who were randomized to a 12-week Internet-based intervention (deprexis, identifier: NCT01636752). Participants completed the PHQ-9 biweekly during the treatment. We identified four change-dropout patterns: Participants showing high impairment, improvement and low dropout probability (C3, N = 134) had the highest rate of reliable change at 6- and 12-month follow-up. A further pattern was characterized by high impairment, deterioration and high dropout probability (C2, N = 32), another by low impairment, improvement and high dropout probability (C1, N = 198). The last pattern was characterized by high impairment, no change and low dropout probability (C4, N = 119). In addition to deterioration, also rapid improvement may lead to dropout as a result of a perceived “good enough” dosage of treatment. This knowledge may strengthen sensitivity for the mechanisms of dropout and help to consider its meaning in efforts to optimize treatment selection.
UR - http://www.scopus.com/inward/record.url?scp=85061033667&partnerID=8YFLogxK
U2 - 10.1080/16506073.2018.1556331
DO - 10.1080/16506073.2018.1556331
M3 - Journal articles
C2 - 30721109
AN - SCOPUS:85061033667
SN - 1650-6073
VL - 49
SP - 22
EP - 40
JO - Cognitive Behaviour Therapy
JF - Cognitive Behaviour Therapy
IS - 1
ER -