  Genito-Pelvic Pain/Penetration-Disorder (GPPPD) is a common disorder but
rarely treated in routine care. Previous research documents that GPPPD symptoms
can be treated effectively using internet-based psychological interventions.
However, non-response remains common for all state-of-the-art treatments and it
is unclear which patient groups are expected to benefit most from an
internet-based intervention. Multivariable prediction models are increasingly
used to identify predictors of heterogeneous treatment effects, and to allocate
treatments with the greatest expected benefits. In this study, we developed and
internally validated a multivariable decision tree model that predicts effects
of an internet-based treatment on a multidimensional composite score of GPPPD
symptoms. Data of a randomized controlled trial comparing the internet-based
intervention to a waitlist control group (N =200) was used to develop a
decision tree model using model-based recursive partitioning. Model performance
was assessed by examining the apparent and bootstrap bias-corrected
performance. The final pruned decision tree consisted of one splitting
variable, joint dyadic coping, based on which two response clusters emerged. No
effect was found for patients with low dyadic coping ($n$=33; $d$=0.12; 95% CI:
-0.57-0.80), while large effects ($d$=1.00; 95%CI: 0.68-1.32; $n$=167) are
predicted for those with high dyadic coping at baseline. The
bootstrap-bias-corrected performance of the model was $R^2$=27.74%
(RMSE=13.22).
