Repeated assessments of depressive symptoms in randomized psychosocial intervention trials: best practice for analyzing symptom change over time

ABSTRACT

Objective:

Psychotherapy randomized trials rarely have tested for the best fitting model for time effects. We examined the fit of different statistical models for examining time when repeated assessments of depressive symptoms are the primary outcome.

Method:

We used data from three studies comparing psychotherapy treatments for major depressive disorder. Outcome measures were self-report ratings for Study 1 (N = 237) and Study 2 (N = 100) and clinician ratings for Study 3 (N = 120) of depressive symptoms measured at every session (Studies 1 and 2) or monthly (Study 3). We examined the fit of the following time patterns: linear, quadratic, cubic, log transformation of time, piece-wise linear, and unstructured.

Results:

In Study 1, a log-linear model had the best fit (Δ Akaike information criterion [AICc] = 7.5). In Study 2, all models had essentially no support (Δ AICcs > 10) in comparison to the best fitting model, which was the unstructured model. In Study 3, the cubic model had the best fit, but it was not significantly better than a log-linear (Δ AICc = 3.5) or unstructured model (Δ AICc = 2.5).

Conclusions:

Trials should routinely compare different time models, including an unstructured model, when repeated measures of depressive symptoms are the primary outcome.

Keywords:

• depression
• outcome
• longitudinal
• time model
• statistical methods

Supplemental data

Supplemental data for this article can be accessed https://doi.org/10.1080/10503307.2022.2073289.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Agency for Healthcare Research and Quality [Grant Number R01-HS018440], the National Institute of Mental Health [Grant Number R34-MH085841], and the Once Upon a Time Foundation.