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 2 nd treatment 15-20% after 3 rd 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

Observed outcomes No RemissionRemissionTotal With PredictorActive Treatment Placebo Without PredictorActive Treatment Placebo Total Odds Ratios: With Predictor = 2.68 ( ) Without Predictor = 1.31 ( ) 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

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 A1Episode A2Episode A3Episode B1Episode C1Episode C2Episode D1Episode D2Episode D3 Patient APatient DPatient BPatient C Treatment XTreatment 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

Sources of variance in clinical trials and observational studies

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