functioning vs. symptoms - adaa
TRANSCRIPT
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H O W C A N W E B E S T M E A S U R E O U T C O M E ?
FUNCTIONING VS. SYMPTOMS
Lily A. Brown, M.A., Michelle G. Craske, Ph.D., Jennifer Krull, Ph.D., Peter
Roy-Byrne, M.D., Cathy Sherbourne, Ph.D., Murray B. Stein, M.D., M.P.H.,
Greer Sullivan, M.D., Raphael D. Rose, Ph.D., Alexander Bystritsky, M.D.
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DISCLOSURE
• This work was supported by the following National
Institute of Mental Health grants: U01 MH070018, U01
MH058915, U01 MH057835, UO1 MH057858, U01
MH070022, K24 MH64122, and K24 MH065324.
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SYMPTOMS AND FUNCTIONING
• Evidence in support of CBT for anxiety disorders (Deacon & Abramowitz, 2004; Olatunji et al., 2010)
• Focus on symptom levels as the primary outcome
• How do we know that symptom reduction leads to
improved functioning?
• Is the directionality of our thinking reversed?
• Do improvements in functioning lead to reductions
in symptoms?
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HYPOTHESES
• Symptoms and functioning are equally important
predictors of each other
• This relationship will remain at 6, 12, and 18 month
follow-ups
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METHODS
• 1,004 participants were recruited from 17 primary care sites
• All participants (CALM and TAU) were included in the current study
• Symptom measures: • Anxiety Sensitivity Inventory (ASI; Reiss et al., 1986)
• Brief Symptom Inventory(BSI; Derogatis et al., 1983)
• Patient Health Questionnaire (PHQ-8; Spitzer et al., 1999)
• Functioning measures • Short Form-12 oblique subscales for physical and mental
functioning (Ware et al., 1995)
• Sheehan Disability Scale(SDS; Sheehan, 1983)
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ANALYTIC STRATEGY
• EQS-Structural Equation Modeling Software (Bentler, 2006)
• Cross-lagged panel model (Martens & Haase, 2006)
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ANALYTIC PLAN
• Autoregressive model
Step 1
• EQS-Structural Equation Modeling Software (Bentler, 2006)
• Cross-lagged path analysis
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AUTOREGRESSIVE EXAMPLE
BSI 00 BSI 06 BSI 12 BSI18
SDS 00 SDS 06 SDS 12 SDS18
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ANALYTIC PLAN
• Autoregressive model
Step 1
• FunctioningSymptoms
Step 2
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FUNCTIONINGSYMPTOMS
BSI 00 BSI 06 BSI 12 BSI18
SDS 00 SDS 06 SDS 12 SDS18
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ANALYTIC PLAN
• Autoregressive model
Step 1
• FunctioningSymptoms
Step 2
• SymptomsFunctioning
Step 3
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SYMPTOMSFUNCTIONING
BSI 00 BSI 06 BSI 12 BSI18
SDS 00 SDS 06 SDS 12 SDS18
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ANALYTIC PLAN
Step 1
• Autoregressive
• (BSI 00BSI 06, BSI 06BSI 12)
Step2
• Functioningsymptoms
• (SDS 00BSI 06, SDS 06BSI 12)
Step 3
• Symptomsfunctioning
• (BSI 00SDS 06, BSI 06SDS 12)
• Full Model
• Deviance change of Step 2 to 4;
Deviance change of Step 3 to 4 Step 4
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FULL MODEL
BSI 00 BSI 06 BSI 12 BSI18
SDS 00 SDS 06 SDS 12 SDS18
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ANALYTIC STRATEGY
• Errors allowed to correlate at the same time-point
• Modification indices:
• Include paths from baseline to all follow-up points of same
measure
• Diagrammed paths do not include coefficients from
autoregressive model
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ASI AND SDS
ASI 00 ASI 06 ASI 12 ASI 18
SDS 00 SDS 06 SDS 12 SDS18
Fit Indices:
BENTLER-BONETT NORMED FIT INDEX = 0.955 (Over .95 is “good”)
.079
.136
.112
.151
.073
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BSI AND SDS
BSI 00 BSI 06 BSI 12 BSI18
SDS 00 SDS 06 SDS 12 SDS18 .123
.213
.128 .169
.141
Fit Indices:
BENTLER-BONETT NORMED FIT INDEX = 0.957 (Over .95 is “good”)
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PCS, MCS, PHQ
PHQ 00 PHQ 06 PHQ 12 PHQ 18
PCS 00 PCS 06 PCS 12 PCS 18
MCS 00 MCS 06 MCS 12 MCS 18
Fit Indices:
BENTLER-BONETT NORMED FIT INDEX = 0.973(Over .95 is “good”)
.063
-.217
-.299
-.123
-.165
-.105
-.367
.061
-.256
-.127
-.342
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PCS AND PHQ
PHQ 00 PHQ 06 PHQ 12 PHQ 18
PCS 00 PCS 06 PCS 12 PCS18 -.138
-.061 -.052
-.128 -.209
Fit Indices:
BENTLER-BONETT NORMED FIT INDEX = 0.970 (Over .95 is “good”)
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MCS AND PHQ
PHQ 00 PHQ 06 PHQ 12 PHQ 18
MCS 00 MCS 06 MCS 12 MCS18
-.086
Fit Indices:
BENTLER-BONETT NORMED FIT INDEX = 0.976(Over .95 is “good”)
-.262 -.086
-.091
-.307
-.088
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DISCUSSION
• Symptom reduction is important to improving
functioning, vice versa
• Treatments should therefore focus on both
• RCT should measure both
• Clinicians do not need to wait until symptoms
improve to work on functioning
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REFERENCES
• Deacon, B.J., Abramowitz, J.S. (2004). Cognitive and Behavioral Treatments for Anxiety Disorders: A
review of meta-analytic findings. Journal of Clinical Psychology, 60, 429-441.
• Derogatis, L. R., & Melisaratos, N. (1983). The Brief Symptom Inventory: an introductory report.
Psychological Medicine, 13(03), 595-605.
• Olatunji, B., Cisler, J.M, Deacon, B.J. (2010). Efficacy of cognitive behavioral therapy for anxiety
disorders: a review of meta-analytic findings. Psychiatric clinics of North America, 33, 557-577.
• Martens, M.P., Haase, R.F. (2006). Advanced applications of structural equation modeling in counseling
psychology research. The Counseling Psychologist, 34, 878-911.
• Reiss, S., Peterson, R. A., Gursky, D. M., & McNally, R. J. (1986). Anxiety sensitivity, anxiety frequency, and
the prediction of fearfulness. Behavior research and therapy, 24, 1-8.
• Sheehan, D. V. (1983). The anxiety disease. New York: Scribner.
• Spitzer, R. L., Kroenke, K., & Williams, J. B. W. (1999). Validation and utility of a self-report version of PRIME-
MD: The PHQ primary care study. Primary care evaluation of Mental Disorders. Patient Health
Questionnaire. JAMA: The Journal of the American Medical Association, 282, 1737-1744.
• Ware, J.E., Kosinski, M., Keller, S.D. (1995). SF12: How to score SF12 Physical and Mental Health Summary
Scales, 2nd edition, Boston, MA: The Health Institute, New England Medical Centerr.