RQ3: Change Trajectories in FCR Over Time and Their Predictors
Cancer
RECURRENCE, POSITIVE, CANCER
First, we identified the optimal number of latent classes, where each class represents a distinct change pattern of FCRI for participants in the intervention group. BIC showed that a model with three different classes fitted the data best, while AIC and AIC3 suggested a model with five different classes. Because of the consensus between AIC and AIC3, we chose the model with five different classes. For each of those five classes, Fear of cancer recurrence (measured with the Fear of Cancer Recurrence Inventory-severity subscale; FCRI-S) across time for five latent classes.Second, we determined predictors of class membership. Clinical and Socio-demographic Predictors of Fear of Cancer Recurrence Latent Class Membership (Research Question 3)Bold values: values of >2 or <−2 are used to determine which class(es) significantly contribute to the effect of the predictor. Positive values show that participants in that class show higher than average scores on that predictor; negative values show that participants in that class show lower than average scores on that predictor.
Significant predictors for the latent classes. − Significantly less; + significantly more. Our results show quite stable FCR trajectories for four (out of five) latent classes over time (indicating stable severe, moderate, mild, and low FCR over time). At first view, one of the classes shows a decrease of five points on the severity subscale of the FCRI over 24 months. Looking closer, we see the decrease occurs after 9 months (6 months after the intervention); hence we do not expect this to be a treatment effect of our online self-help. In conclusion, RMLCA did not reveal classes who benefit from the intervention.