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Genito-Pelvic Pain/Penetration-Disorder (GPPPD) is a common disorder butrarely treated in routine care. Previous research documents that GPPPD symptomscan be treated effectively using internet-based psychological interventions.However, non-response remains common for all state-of-the-art treatments and itis unclear which patient groups are expected to benefit most from aninternet-based intervention. Multivariable prediction models are increasinglyused to identify predictors of heterogeneous treatment effects, and to allocatetreatments with the greatest expected benefits. In this study, we developed andinternally validated a multivariable decision tree model that predicts effectsof an internet-based treatment on a multidimensional composite score of GPPPDsymptoms. Data of a randomized controlled trial comparing the internet-basedintervention to a waitlist control group (N =200) was used to develop adecision tree model using model-based recursive partitioning. Model performancewas assessed by examining the apparent and bootstrap bias-correctedperformance. The final pruned decision tree consisted of one splittingvariable, joint dyadic coping, based on which two response clusters emerged. Noeffect 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) arepredicted for those with high dyadic coping at baseline. Thebootstrap-bias-corrected performance of the model was $R^2$=27.74%(RMSE=13.22).

Predicting Individualized Effects of Internet-Based Treatment for  Genito-Pelvic Pain/Penetration Disorder: Development and Internal Validation  of a Multivariable Decision Tree Model