Predictors, Outcomes, and Statistical Solutions of Missing Cases in Web-Based Psychotherapy: Methodological Replication and Elaboration Study.
missing data
psychotherapy
statistical bias
treatment adherence and compliance
treatment evaluation
Journal
JMIR mental health
ISSN: 2368-7959
Titre abrégé: JMIR Ment Health
Pays: Canada
ID NLM: 101658926
Informations de publication
Date de publication:
05 Feb 2021
05 Feb 2021
Historique:
received:
21
07
2020
accepted:
13
12
2020
revised:
09
12
2020
entrez:
5
2
2021
pubmed:
6
2
2021
medline:
6
2
2021
Statut:
epublish
Résumé
Missing cases present a challenge to our ability to evaluate the effects of web-based psychotherapy trials. As missing cases are often lost to follow-up, less is known about their characteristics, their likely clinical outcomes, or the likely effect of the treatment being trialed. The aim of this study is to explore the characteristics of missing cases, their likely treatment outcomes, and the ability of different statistical models to approximate missing posttreatment data. A sample of internet-delivered cognitive behavioral therapy participants in routine care (n=6701, with 36.26% missing cases at posttreatment) was used to identify predictors of dropping out of treatment and predictors that moderated clinical outcomes, such as symptoms of psychological distress, anxiety, and depression. These variables were then incorporated into a range of statistical models that approximated replacement outcomes for missing cases, and the results were compared using sensitivity and cross-validation analyses. Treatment adherence, as measured by the rate of progress of an individual through the treatment modules, and higher pretreatment symptom scores were identified as the dominant predictors of missing cases probability (Nagelkerke R The treatment outcomes of the cases that were missing at posttreatment were distinct from those of the remaining observed sample. Thus, overlooking the features of missing cases is likely to result in an inaccurate estimate of the effect of treatment.
Sections du résumé
BACKGROUND
BACKGROUND
Missing cases present a challenge to our ability to evaluate the effects of web-based psychotherapy trials. As missing cases are often lost to follow-up, less is known about their characteristics, their likely clinical outcomes, or the likely effect of the treatment being trialed.
OBJECTIVE
OBJECTIVE
The aim of this study is to explore the characteristics of missing cases, their likely treatment outcomes, and the ability of different statistical models to approximate missing posttreatment data.
METHODS
METHODS
A sample of internet-delivered cognitive behavioral therapy participants in routine care (n=6701, with 36.26% missing cases at posttreatment) was used to identify predictors of dropping out of treatment and predictors that moderated clinical outcomes, such as symptoms of psychological distress, anxiety, and depression. These variables were then incorporated into a range of statistical models that approximated replacement outcomes for missing cases, and the results were compared using sensitivity and cross-validation analyses.
RESULTS
RESULTS
Treatment adherence, as measured by the rate of progress of an individual through the treatment modules, and higher pretreatment symptom scores were identified as the dominant predictors of missing cases probability (Nagelkerke R
CONCLUSIONS
CONCLUSIONS
The treatment outcomes of the cases that were missing at posttreatment were distinct from those of the remaining observed sample. Thus, overlooking the features of missing cases is likely to result in an inaccurate estimate of the effect of treatment.
Identifiants
pubmed: 33544080
pii: v8i2e22700
doi: 10.2196/22700
pmc: PMC7895640
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e22700Informations de copyright
©Eyal Karin, Monique Frances Crane, Blake Farran Dear, Olav Nielssen, Gillian Ziona Heller, Rony Kayrouz, Nickolai Titov. Originally published in JMIR Mental Health (http://mental.jmir.org), 05.02.2021.
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