Symptom courses during and after psychotherapy: models and simulations

1. Symptom courses during and after psychotherapy: models and simulations Robert Perčević June 2007

2. Treatment Courses

3. The Random Walk Model xt= ?

4. The Random Walk Model xt=xt-1 x t

5. The Random Walk Model xt=xt-1+c c<0 x t

6. The Random Walk Model Random Variable xt=xt-1+c+ψc<0 V(ψ)>0, E(ψ)=0

7. The Random Walk Model with Measurement Error xt=xt-1+c+ψ+εt-εt-1c<0 V(ψ)>0, E(ψ)=0 V(ε)>0, E(ε)=0

8. Empirical Verification • Hypothesis: • (a) homogeneous and (b) independent change rates • Sample: • Specialized psychotherapeutic hospital • 1210 patients • Most common diagnosis: F32, F33, F60, F50 • Treatment between 2 and 77 days • Up to 9 assessments per patient • 4149 observations • Method: • HLM; (xi,t - xi,t-1) / Δt = (β1 + ai) + (β2 + bi)t + ε

9. Empirical Verification • Findings:

10. How to improve Outcomes?

11. How to improve Outcomes? • Increase treatment length • Increase effectiveness • Match treatment and patient • Outcome monitoring • Identify non-reponders • Adapt treatment length to patients needs

12. How to improve Outcomes? • Increase treatment length • Increase effectiveness • Match treatment and patient • Outcome monitoring • Identify non-reponders • Adapt treatment length to patients needs

13. How to improve Outcomes? • Increase treatment length • Increase effectiveness • Match treatment and patient • Outcome monitoring • Identify non-reponders • Adapt treatment length to patients needs

14. How to improve Outcomes? • Increase treatment length • Increase effectiveness • Match treatment and patient • Outcome monitoring • Identify non-reponders • Adapt treatment length to patients needs

15. How to improve Outcomes? • Increase treatment length • Increase effectiveness • Match treatment and patient • Outcome monitoring • Identify non-reponders • Adapt treatment length to patients needs

16. Outcome criteria

17. Simulation: Treatment Length x(1…n,0)=initial_distress_distirbution FOR patient = 1 to n FOR time = 1… max_treatment_time x(patient,time)=x(patient,time-1)+c+randomvariable IF x(patient,time)<cutoff positive(time)=positive(time)+1 ENDIF ENDFOR ENDFOR PLOT(positive, 1… max_treatment_time)

18. Simulation: Treatment Length

19. Simulation: Treatment Length Dose-Effect / Cost-Effect / Cost-Benefit

20. Simulation: Treatment Length Dose-Effect / Cost-Effect / Cost-Benefit Efficiency: the relation between Effect and Effort

21. Simulation: Effectiveness

22. Simulation: Identification of Nonresponders

23. Simulation: Adapting Length of Treatment

24. Different Outcome Criteria & Adapting Length of Treatment

25. Different Outcome Criteria & Adapting Length of Treatment

26. Aftertreatment Courses

27. Stylized Facts

28. Stylized Facts r = -0.435

29. Model xt=xt-1+εt-εt-1 V(ε)>0, E(ε)=0

30. Model xt=xt-1+εt-εt-1 V(ε)>0, E(ε)=0

31. How to avoid Relapses?

32. How to avoid Relapses?Example

33. How to avoid Relapses? Better outcome at primary treatment

34. How to avoid Relapses? Low-intensity continuation of primary treatment

35. How to avoid Relapses? Maintanance treatment

36. How to avoid Relapses? Outcome Monitoring