View
217
Download
0
Tags:
Embed Size (px)
Citation preview
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
• 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 benefitNo effect on untreated prognosis
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Overall With Without
Placebo
Active Treatment
Observed outcomes
No Remission Remission Total
With Predictor Active Treatment 103 97 200
Placebo 148 52 200
Without Predictor Active Treatment 137 63 200
Placebo 148 52 200
Total 536 264 800
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
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
With Without
Placebo Active Treatment
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
Episode A1 Episode A2 Episode A3 Episode B1 Episode C1 Episode C2 Episode D1 Episode D2 Episode D3
Patient A Patient DPatient B Patient C
Treatment X Treatment Y
Complex clustered data structure
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 Episode-Level Predictors Treatment Characteristics Demographics (sex, race/ethnicity) Pre-treatment sy mptom severity Medication vs. Psychotherapy Genetic variation Pre-treatment episode duration Specific medication or drug class Childhood trauma or abuse Recent stressful events Treatment intensity Family history Current substance use Treatment duration Personality or attachment style 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