Metacognitive Therapy and Intolerance of Uncertainty Therapy for Generalized Anxiety Disorder in Primary Care: Randomized Controlled Pilot Study
Phase: NA
Conditions: Generalized Anxiety Disorder
Sponsor: Karolinska Institutet

Study Protocol and Statistical Analysis Plan  
 
 
Official Title: Metacognitive Therapy and Intolerance-of-Uncertainty Therapy for Generalized 
Anxiety Disorder in Primary Care: Randomized Controlled Pilot Study  
NCT Number: NCT03621371 
Document Date: 4 July 2024 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Study Protocol 
Objective. Feasibility and preliminary efficacy of two psychological treatments based 
on cognitive-behavioral therapy – metacognitive therapy (MCT) and intolerance-of-
uncertainty therapy (IUT) – for primary care patients with a principal diagnosis of generalized 
anxiety disorder in Stockholm, Sweden are investigated in a pilot study using a randomized 
controlled trial design. The purpose of the study is to examine the feasibility of a full-scale 
randomized controlled trial. Design. Randomized controlled pilot trial. Methods. Research 
questions primarily concern recruitment, assessment, and therapist competence and 
adherence. Secondary research questions concern treatment efficacy. Primary treatment 
outcome measure is total scores on the Penn State Worry Questionnaire (PSWQ).  
 
Statistical Analysis Plan  
Statistical analyses are performed using the SPSS (Version 27, SPSS Inc., Chicago, 
IL). Proportions, means, and standard deviations (SDs) are calculated for feasibility measures. 
Differences in session attendance between the IUT and MCT conditions are investigated 
using an independent t-test and differences in dropout with a Fisher’s exact test. In the 
preliminary evaluation of treatment effects, multilevel modeling is used to estimate the effects 
of time and of time by condition on continuous outcome measures from the pre-treatment to 
the post-treatment assessment and from the post-treatment to the follow-up assessment. The 
maximum likelihood method is used to estimate model parameters. Different covariance 
structures are tested. Models are built in a stepwise fashion, starting with a basic model with a 
fixed intercept, then adding random parameters (intercept and slope), and finally adding a 
time by group interaction term to the model. Each model’s fit to observed data is evaluated 
with the likelihood ratio test. Models with significantly better fit than previous models are 
retained. Standardized effect sizes for between-group effects at mid-treatment, post-treatment, 

and follow-up are calculated as Cohen’s d for multilevel models, using the SD for the pooled 
sample at pre-treatment and the pooled sample SD at post-treatment (for post-treatment to 
follow-up). For between-group effect size estimations, the beta coefficient (difference in 
change trajectories between treatments) is multiplied by treatment duration at mid-treatment, 
the average treatment duration, or the follow-up duration, and then divided by the pooled SD 
of observed values at pre-treatment or post-treatment. For model-based d, 95% confidence 
intervals are calculated. In keeping with the principle of intention-to-treat, data from all 
participants are used in the multilevel models.  
Treatment response is assessed with the reliable change index (RCI), which is 
calculated using the internal consistency reliability of the PSWQ and the sample SD of this 
measure at pre-treatment. An RCI of z ≥ -1.96 indicates reliable improvement, whereas an 
RCI of z ≥ 1.96 indicates reliable deterioration. Differences in improvement and deterioration 
between conditions at the post-treatment and follow-up assessment are investigated with chi-
square tests or Fisher’s exact tests.  
To assess recovery rates, two procedures are employed that combine statistically 
reliable change with clinically meaningful change. First, an RCI of 7 points and a cut-off of 
53 points on the PSWQ as used in a previous study are applied. Second, the same RCI and a 
cut-off of 47 points as used in another previous study are applied. Differences in recovery 
rates between groups at post-treatment and follow-up assessment are investigated with chi-
square tests or Fisher’s exact tests.