Transcript PersonalizedDepressi..

Can we use population-based longitudinal
data to personalize depression treatment?
Gregory Simon MD MPH
Group Health Center for Health Studies
Outline
• Background on predicting response to
depression treatment
• Limitations of randomized trials to identify
predictors of response
• Alternative – large observational studies
using longitudinal data
• Methodologic and statistical issues in large
observational studies
Success of antidepressant treatment
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35-40% remission with 1st treatment
25-30% with 2nd treatment
15-20% after 3rd treatment
Cumulative remission rate: 60-65%
Predicting treatment success
• Moderate ability to predict overall outcome
– severity, chronicity, comorbidity, poor
response to previous treatments, etc.
• Poor (actually zero) ability to predict
specific or differential response based on:
– symptom patterns
– biomarkers
• Some support for genetic predictors of
adverse effects – less clear for benefits
Core assumption
There are stable characteristics of
individuals that predict greater likelihood of
good (or bad outcome) with exposure to:
•Active treatment compared to no treatment
or inactive treatment
•One active treatment compared to another
Traditional method: search for effect
modification in randomized clinical trial
•Random assignment to treatments
(comparing active treatments or active
treatment to placebo)
•Test for interaction between proposed
predictor and treatment assigment
Potential effect modifier:
Prevalence = 50%
Accounts for 80% of benefit
No effect on untreated prognosis
100%
90%
80%
70%
60%
Placebo
Active Treatment
50%
40%
30%
20%
10%
0%
Overall
With
Without
Observed outcomes
With Predictor
No Remission
Remission
Total
Active Treatment
103
97
200
Placebo
148
52
200
137
63
200
148
52
200
536
264
800
Without Predictor Active Treatment
Placebo
Total
Odds Ratios:
With Predictor =
2.68 (1.76-4.08)
Without Predictor = 1.31 (0.85-2.02)
Test for interaction: p=.02
Note: This study would cost $5-6 million
Components of placebo response
• Natural history
– Stable characteristics
– Episode-specific characteristics
• Non-specific benefits of treatment
• Measurement error
We know absolutely nothing about:
• Consistency of placebo response across
episodes
• Consistency of response to same or
different treatment across episodes
Consistency of response across episodes
Placebo
Active Treatment
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
With
Without
Alternative: large observational
studies using longitudinal data
• Use longitudinal data including multiple
treatment episodes per person
• Treatment decision are uncontrolled
• Alternatives for assessing outcome:
– Medical records data (proxy measures)
– Recall across multiple treatment episodes
– Prospective assessment
Complex clustered data structure
Patient A
Episode A1
Episode A2
Patient B
Episode A3
Treatment X
Episode B1
Patient C
Episode C1
Episode C2
Patient D
Episode D1
Treatment Y
Episode D2
Episode D3
Pros and cons of large
observational studies
• Advantages:
– Sample sizes practically unlimited
– Recruitment is much more efficient
– Multiple episodes per person (can separate
episode-level and person-level variation)
• Disadvantages:
– Greater measurement error within episodes
– Treatments are not randomly assigned
Distinguishing stable (person-level) and unstable
(episode-level) predictors
Patient-Level Predictors
Demographics (sex, race/ethnicity)
Genetic variation
Childhood trauma or abuse
Family history
Personality or attachment style
Episode-Level Predictors
Treatment Characteristics
Pre-treatment sy mptom severity
Pre-treatment episode duration
Recent stressful events
Current substance use
Medication vs. Psychotherapy
Specific medication or drug class
Treatment intensity
Treatment duration
Therapeutic alliance
Sources of variance in clinical trials
and observational studies
True Response
Nonspecific Tx Effect
Measurement Error
Person-Level Factors
Episode-Level Factors
Biased Tx Assignment
Methodologic questions:
• Use of claims data for proxy outcomes (or
at least to identify enriched samples)
• Accuracy of recall for past episodes
• Biased assignment of treatments in later
episodes
• Consistency of response across episodes
Feasibility of recruitment
• What proportion of those approached
agree to participate in assessments
• What proportion agree to provide genetic
material
• How do participants and non-participants
differ in:
– Demographics
– Treatment history
– Current mood
Utilization as a proxy for outcome
• Proportion of early discontinuers who
reach remission (akin to placebo
responders)
• Continued use of original drug as proxy for
good response
• Early discontinuation as proxy for adverse
effects
• Medication switch or specialty referral as
proxy for poor response
Accuracy of recall
• Interested in accuracy of recall for both
benefits and adverse effects
• Likely that accuracy of recall decreases
with time
• Recall may be influenced by current mood
Biased assignment of treatments
• Likely that good response to a treatment
increases likelihood of re-exposure
• May inflate estimates of consistency of
good response across episodes
General modeling approach
• Random coefficient regression models to
account for clustering of episodes within
individuals
• Can consider both general tendency to respond
to treatment and tendency to respond to specific
treatments
• Consider treatment response as a function of:
– Stable person-level characteristics (measured and
unmeasured)
– Treatment exposure
Two approaches to biased
selection of treatments
• Decompose variation into between-person
(i.e. general tendency to respond favorably
or unfavorably) and within-person (i.e.
tendency to respond specifically to a given
treatment)
• Explicitly model selection process
Managing effects of recall error
• Random error – ? overcome with brute
force
• Decay in recall over time – may need to
censor remote observations
• Effect of current mood state – may need to
account for explicitly in models