Symptom courses during and after psychotherapy: models and simulations
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Transcript of Symptom courses during and after psychotherapy: models and simulations
Symptom courses during and after psychotherapy: models and
simulations Robert Perčević
June 2007
Treatment Courses
The Random Walk Model
xt= ?
The Random Walk Model
xt=xt-1
x
t
The Random Walk Model
xt=xt-1+c c<0
x
t
The Random Walk Model
xt=xt-1+c+ψ c<0
V(ψ)>0, E(ψ)=0
Random Variable
The Random Walk Model with Measurement Error
xt=xt-1+c+ψ+εt-εt-1 c<0
V(ψ)>0, E(ψ)=0 V(ε)>0, E(ε)=0
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
Empirical Verification
• Findings:
β1 sd(I) p β2 p AR MA res
GES -0,0201 0,0103 <0,01 -0,0008 <0,01 0,0036 -0,3473 0,0859
KOE -0,0346 0,0011 <0,01 -0,0002 0,3516 -0,0466 -0,2983 0,1036
PSY -0,0185 0,0179 <0,01 -0,0006 <0,01 0,1622 -0,6501 0,0791
SOZ -0,0042 0,0104 <0,01 -0,0007 <0,01 0,1654 -0,6204 0,0886
KOM -0,0070 0,0003 <0,01 -0,0005 <0,01 0,0035 -0,4071 0,0867
ZUF -0,0109 0,0004 <0,01 -0,0006 <0,01 -0,0812 -0,3539 0,1139
SOU -0,0018 0,0002 <0,01 -0,0006 <0,01 -0,0781 -0,3559 0,0820
How to improve Outcomes?
• Increase treatment length
• Increase effectiveness
• Match treatment and patient – Outcome monitoring
• Identify non-reponders• Adapt treatment length to patients needs
How to improve Outcomes?
How to improve Outcomes?
• Increase treatment length
• Increase effectiveness
• Match treatment and patient – Outcome monitoring
• Identify non-reponders• Adapt treatment length to patients needs
How to improve Outcomes?
• Increase treatment length
• Increase effectiveness
• Match treatment and patient – Outcome monitoring
• Identify non-reponders• Adapt treatment length to patients needs
How to improve Outcomes?
• Increase treatment length
• Increase effectiveness
• Match treatment and patient – Outcome monitoring
• Identify non-reponders• Adapt treatment length to patients needs
How to improve Outcomes?
• Increase treatment length
• Increase effectiveness
• Match treatment and patient – Outcome monitoring
• Identify non-reponders• Adapt treatment length to patients needs
1 1.5 2 2.5 3 3.5 4
"Krank"
"Gesund"
Dichotomes Gesundungskriterium
KPD38 GES
Outcome criteria
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)
Simulation: Treatment Length
0 10 20 30 40 50 600
0.1
0.2
0.3
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0.6
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0.8
0.9
1Entlassungszustand <2.5 mit EM (Grün) und ohne EM (Rot) :
Durchschittliche Dauer der Behandlung
p
Simulation: Treatment Length
0 10 20 30 40 50 600
0.1
0.2
0.3
0.4
0.5
0.6
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1Entlassungszustand <2.5 mit EM (Grün) und ohne EM (Rot) :
Durchschittliche Dauer der Behandlung
p
Dose-Effect / Cost-Effect / Cost-Benefit
Simulation: Treatment Length
0 10 20 30 40 50 600
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1Entlassungszustand <2.5 mit EM (Grün) und ohne EM (Rot) :
Durchschittliche Dauer der Behandlung
p
Dose-Effect / Cost-Effect / Cost-Benefit
Efficiency: the relation between Effect and Effort
0 10 20 30 40 50 600
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1Entlassungszustand <2.5 mit EM (Grün) und ohne EM (Rot) :
Durchschittliche Dauer der Behandlung
p
Simulation: Effectiveness
Simulation: Identification of Nonresponders
0 50 100 150 200 250 300 3500
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1
Beobachtungsdauer
p
Anteil der Patienten mit c>=0 unter allen Patienten welche nach der gegebenen Beobachtungsdauer keine Besserung zeigen
0 10 20 30 40 50 600
0.1
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1Entlassungszustand <2.5 mit EM (Grün) und ohne EM (Rot) :
Durchschittliche Dauer der Behandlung
p
Simulation: Adapting Length of Treatment
1 1.5 2 2.5 3 3.5 40
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1
KPD38 GES
p
Wahrscheinlichkeit das Norm(al)
Different Outcome Criteria & Adapting Length of Treatment
2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 30.4
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1
1.2
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Empfohlener Entlassungswert
Effizie
nz
2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 30.2
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1
Empfohlener Entlassungswert
max p
"Gesu
nd"
Different Outcome Criteria & Adapting Length of Treatment
Aftertreatment Courses
Stylized Facts
Stylized Facts
r = -0.435
Model
xt=xt-1+εt-εt-1 V(ε)>0, E(ε)=0
Model
xt=xt-1+εt-εt-1 V(ε)>0, E(ε)=0
0 1 2 3 4 5 6 7 8 9 10
How to avoid Relapses?
0 1 2 3 4 5 6 7 8 9 10
How to avoid Relapses?Example
0 1 2 3 4 5 6 7 8 9 101
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How to avoid Relapses? Better outcome at primary treatment
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How to avoid Relapses? Low-intensity continuation of primary
treatment
0 1 2 3 4 5 6 7 8 9 101
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How to avoid Relapses? Maintanance treatment
0 1 2 3 4 5 6 7 8 9 101
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How to avoid Relapses? Outcome Monitoring