The Promise of Evidence-based Personalized Mental Health Practice

Transcription

1 The Promise of Evidence-based Personalized Mental Health Practice Robert J. DeRubeis Samuel H. Preston Term Professor in the Social Sciences and Professor of Psychology, University of Pennsylvania IFP Congress 2018 Amsterdam

2 Principal Aims of Our Research Estimate the effectiveness and relative effectiveness of treatments for common mental disorders Re-examine assumptions about the effects of treatments using innovative methodologies Develop and implement tools to identify treatment mechanisms and to promote the personalization of mental health interventions

3 Two disciplines of scientific psychology 1. Experiments (RCTs) Main effects 2. Observational studies Predictive relationships Integration of experimental and observational methods: Moderation and mediation of main effects Simulations of commonly encountered scenarios, to illustrate challenges to an empirical clinical science

4 Two disciplines of scientific psychology 1. Experiments (RCTs) Main effects 2. Observational studies Predictive relationships Integration of experimental and observational methods: Moderation and mediation of main effects Simulations of commonly encountered scenarios, to illustrate challenges to an empirical clinical science

5 Two disciplines of scientific psychology 1. Experiments (RCTs) Main effects 2. Observational studies Predictive relationships Integration of experimental and observational methods: Moderation and mediation of main effects Simulations of commonly encountered scenarios, to illustrate challenges to an empirical clinical science

6 A Main Effect Question: What is the comparative efficacy of antidepressant medications (ADM) vs. placebo for patients with MDD?

7 HRSD Improvement (d) Typical Drug > Placebo Advantage Clinical Significance (NICE) 0.35 Mild-Moderate Severe Very Severe

8 HRSD Improvement (d) Typical Drug > Placebo Advantage Clinical Significance (NICE) 0.35 Mild-Moderate A difference of approximately Severe 3 points Very on Severe the Hamilton Rating Scale for Depression

9 Is a treatment that is effective on average equally effective for all?

10 IPD Meta-analysis: Included Patients with a Wide Range of Severity at Intake Non-Severe Patients Severe Patients Intake severity (HRSD Score)

11 Moderation of a Main Effect of Treatment Non-Severe Patients Severe Patients Intake severity (HRSD Score) *(From Fournier, DeRubeis, Hollon, et al., 2010)

12 HRSD Improvement (d) Drug > Placebo Advantage is Moderated by Symptom Severity at Intake 0.11 Clinical Significance (NICE) Mild-Moderate Severe Very Severe Severity of Depression

13 Heterogeneity of Treatment Effects Within a population, the impact of a treatment is a function of: the potency of the treatment the patient s characteristics: His/her potential to improve in any treatment (or no treatment) the degree of match/mismatch between the patient s characteristics and the features of the treatment

14 Heterogeneity of Treatment Effects Patient characteristics can be used to predict the outcomes of treatments in two ways: The likelihood that any treatment (or the absence of treatment) will produce benefit: Prognostic characteristics The differential benefit expected from different treatments: Prescriptive characteristics (moderators)

15 Modeling Heterogeneity to Improve Outcomes Models can inform treatment selection when: A more intensive (or expensive) treatment might not be needed in an identifiable subset of the population ADM vs. ADM+CBT Treatment-as-Usual vs. CBT

16 Modeling Heterogeneity to Improve Outcomes Models can inform treatment selection when: A more intensive (or expensive) treatment might not be needed in an identifiable subset of the population ADM vs. ADM+CBT Treatment-as-Usual vs. CBT The evidence suggests that two (or more) treatments produce similar average effects ADM vs. CBT for depression Prolonged Exposure vs. Cognitive Processing Therapy for PTSD

17 Simplest Use of Modeling to Guide Treatment Selection Identify patients who are most likely to benefit from a stronger (vs. a weaker or control) treatment, even when the stronger treatment is only a little stronger, on average.

18 What Might Account for Small Between-Treatment Effects?

19 What Might Account for Small Between-Treatment Effects? Inadequate delivery of a potent treatment Poor operationalization of outcome

20 What Might Account for Small Between-Treatment Effects? Inadequate delivery of a potent treatment Poor operationalization of outcome Our intuition is wrong about the treatments Or

21 our intuition is correct, but only for patients who could benefit from a strong treatment Our intuitions are not meant to account for patients: who are unlikely to need a strong treatment (Spontaneous Remitters) who are unlikely to benefit from any of our treatments (Intractable patients)

22 our intuition is correct, but only for patients who could benefit from a strong treatment Our intuitions are not meant to account for patients: who are unlikely to need a strong treatment (Spontaneous Remitters) who are unlikely to benefit from any of our treatments (Intractable patients)

23 Patient Response Patterns and Variations in Treatment Strength

24 Improvement 100% Patient Response Patterns Spontaneous Remitter 75% 50% 25% 0% Intractable Strength of Treatment

25 What happens when a strong treatment is compared with a weak one, in different contexts?

26 What happens when a strong treatment is compared with a weak one, in different contexts? A simulation study

27 We Set up These Ideal Conditions Strong treatment 55 points (+15) improvement Weak treatment 45 points (+15) improvement Therefore: SMD of 10/15 (0.67 d-type effect size) We then simulated the findings of 1,000 RCTs, testing the Strong versus the Weak treatment, with each of three distributions of Patient Response Patterns Sample sizes were 60 vs. 60 in each simulated study

28 We Set up These Ideal Conditions Strong treatment 55 points (+15) improvement Weak treatment 45 points (+15) improvement Therefore: SMD of 10/15 (0.67 d-type effect size) We then simulated the findings of 1,000 RCTs, testing the Strong versus the Weak treatment, with each of three distributions of Patient Response Patterns Sample sizes were 60 vs. 60 in each simulated study

29 Improvement 100% Spontaneous Remitter Patient Response Patterns 75% 50% 25% 0% Intractabl e Strength of Treatment

30 Aggregate of Findings from 1,000 Simulations in which Only Pliant Patients are Treated Strong Treatment M = 55 (+15) d = 0.69 Weak Treatment M = 45 (+15)

31 The Advantage of the Strong Treatment Dissipated Dramatically as We Introduced More Realistic Conditions

32 % of Sample 70% 60% Predominantly Pliant 50% 40% 30% 20% 10% 0% Spontaneous Remitter Easy Pliant Challenging Intractable

33 Strong vs. Weak Treatment Mean diff. in population All Pliant Predominantly Pliant Skewed Ave. SMD (d) Power, with Ns of 60 vs % 77% 8% Common

34 % of Sample 70% 60% 50% Skewed (e.g., MDD) 40% 30% 20% 10% 0% Spontaneous Remitter Easy Pliant Challenging Intractable

35 Strong vs. Weak Treatment Mean diff. in population All Pliant Predominantly Pliant Skewed Ave. SMD (d) Power, with Ns of 60 vs % 77% 8%

36 Strong vs. Weak Treatment All Pliant Predominantly Pliant Skewed Mean diff. in population Ave. SMD (d) Power, with Ns of 60 vs % 77% 8% Idealized Rare Typical

37 Applying This Logic to Findings from an RCT Van Straten et al. (2006): RCT of treatments for depression provided in Dutch community settings Participants randomized to: Treatment-as-Usual (TAU; n = 234) Brief Therapy (BT; n = 179) Cognitive Behavior Therapy (CBT; n = 208) Main finding: No substantial or significant differences in outcomes among the treatments

38 Are there patients who show the expected treatment effect (and others who clearly do not)?

39 Percent Improvement in Symptom Severity Hypothesized Response to Treatment as a Function of Pliancy 100% Weaker Tx Stronger Tx 80% 60% 40% 20% 0% 2-Easy 3-Pliant

40 How to identify Easy and Pliant patients in advance: Construct a multivariable model to reflect the range of Patient Response Patterns

41 Variables that contributed to a Prognostic Index (PI) Log Odds of Recovery Unemployment status Recurrent MDD Hostility (SCL) Selected with LASSO Sleep complaints (SCL) Extraversion (NEO) 0.29 *(From Lorenzo-Luaces, DeRubeis, van Straten, & Tiemens, 2017)

42 Does not meet MDD status Outcomes as a Function Treatment 100% 80% 60% TAU BT CBT n.s., ps > % 75% 76% 40% 20% 0% Full sample

43 Does not meet MDD status 100% 80% Outcomes as a Function of PI Status and Treatment TAU BT CBT n.s., ps > % 80% 78% 60% 40% 20% 40% 44% 0% Best 73% Worst 27% Divided by Scores on PI

44 Does not meet MDD status 100% 80% 60% 40% 20% 0% Outcomes as a Function of PI Status and Treatment TAU BT CBT n.s., ps >0.08 OR = 2.85, p < % 86% 78% OR = 2.44, p < % 40% 44% Best 73% Worst 27% Divided by Scores on PI

45 The Case of Two Equally Strong Treatments 45

46 Posttreatment HRSD Main effect of treatment (ADM vs. CBT) in severely depressed patients (Intake HRSD > 20) Elkin* (N=53) Rush* (N=26) ADM Murphy* (N=22) CT Hollon* (N=68) DeRubeis, Hollon, et al. (n=180) Pooled (n=349) *(From DeRubeis, Gelfand, Tang, & Simons, 1999)

47 Percent Improvement Responses to Two Strong Treatments and a Placebo Treatment: Patient Prototypes 80% 70% 60% 50% 40% 30% 20% 10% 0% 1-Spontaneous Remitter Placebo TxA TxB 2 -Needs Strong Tx (Pliant) 3a-Needs 3A-Needs TxA TxA 3b-Needs 3B-Needs TxB 4-Intractable

48 Percent Improvement Responses to Two Strong Treatments and a Placebo Treatment: Patient Prototypes 80% 70% 60% 50% 40% 30% 20% 10% 0% 1-Spontaneous Remitter Placebo TxA TxB 2 -Needs Strong Tx (Pliant) 3a-Needs 3A-Needs TxA 3b-Needs 3B-Needs TxB 4-Intractable

49 ADM vs. CBT in Patients with Moderate to Severe Major Depressive Disorder Response rates were nearly identical (on average)

50 Percent of patients who Responded after 16 weeks of Acute Treatment 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Dropout or nonresponse Dropout or nonresponse 58% 58% ADM (N=120) CBT (N=60) *(From DeRubeis, Hollon, Amsterdam, et al., 2005)

51 The common approach to research on moderators of treatment effects 1. Identify a Big Variable (Moderator), then sort on it

52 Response Rate 100% 80% * * 60% 40% 20% 66% 44% 49% 70% 0% ADM (59) CT (27) ADM (61) CT (33) With co-morbid PD No co-morbid PD *(From Fournier, DeRubeis, Shelton, et al., 2008)

53 Personality Disorder diagnosis moderated the treatment effect But what if there are other moderators?

54 Personality Disorder diagnosis moderated the treatment effect But what if there are other moderators? Marital status Employment status Number of prior medication treatments Severity of recent stressful life events

55 Our approach to research on the moderation of treatment effects 1. Identify a Big Variable (Moderator), then sort on it 2. Identify multiple moderators, generate and test a predictive model 2. Identify multiple moderators, generate and test a predictive model

56 The Question: Can an actuarial approach improve Treatment Selection (aka Precision Mental Health)?

57 The Personalized Advantage Index (PAI) Handles multiple interactions Provides for each patient a signed, ordinal metric that reflects the expected difference between two interventions Therefore, the PAI approach is used to identify patients for whom the expected difference is: Large, in one direction or the other Small or negligible *(From DeRubeis, Cohen, Forand, et al., 2014)

58 Our First Demonstration Standard multivariable modeling approach Dependent variable: Post-treatment Hamilton Depression score Independent variables: simple main effects for our prognostic variables main effects and interactions (with treatment) for our prescriptive variables *(From DeRubeis, Cohen, Forand, et al., 2014)

59 Original Finding (PLOS One, 2014) 1.8 point difference 3.6 point difference

60 end-hrsd Finding Using Refined Methods 14 Figure #. Observed Advantage of Treatment Selection point difference point difference Randomized to Optimal (N=68) Randomized to Non- Optimal (N=86) Randomized to Optimal (N=43) Randomized to Non- Optimal (N=53) Full Sample Stronger PAIs (top 60%)

61 Can predictions from a multivariable model generalize to another population?

62 Testing predictions from a multivariable model in a separate population Goal: Use the PAI approach to identify patients who would most benefit from the addition of CBT to ADM

63 ADM vs. CBT+ADM in patients with Chronic or Recurrent Depression Basic finding: Adding CBT to ADM yielded a 9% increment in the recovery rate (66% 75%) *(Hollon, DeRubeis, Fawcett, et al., 2016)

64 ADM vs. CBT+ADM in patients with Chronic or Recurrent Depression Basic finding: Adding CBT to ADM yielded a 9% increment in the recovery rate (66% 75%) Magnitude hypothesis: Each patient s odds go up a little with the addition of CBT *(Hollon, DeRubeis, Fawcett, et al., 2016)

65 ADM vs. CBT+ADM in patients with Chronic or Recurrent Depression Basic finding: Adding CBT to ADM yielded a 9% increment in the recovery rate (66% 75%) Magnitude hypothesis: Each patient s odds go up a little with the addition of CBT Matching hypothesis: Some patients odds go up a lot, and others go up little if at all *(Hollon, DeRubeis, Fawcett, et al., 2016)

66 CPT3: Recovery in ADM vs. ADM+CBT Whose odds are improved by addition of CBT? Clinical guidelines recommend Combination Treatment for those with severe symptoms We conjectured that a multivariable profile would outperform symptom severity in this context

67 Build the Model with Data from Two Sites, Apply it in a Third

68 Variables featured in the models: # of Prior Depressive Episodes Age of Onset Loss of Interest (MASQ) IQ Estimate (Shipley) Expectations for Therapy Hopelessness Scale GAF-Functioning Marital Status

69 Predicting the differential advantage of ADM+CBT, relative to ADM Alone Multivariable Index (PAI):Highly significant Severity Index (HRSD): Nonsignificant

70 Recovered 100% 80% 60% 40% Patients with concordant predictions ADM CBT+ADM 69% 70% 67% 37% 20% 0% HRSD<25, Lower 70% on PAI HRSD>24, Lower 70% on PAI HRSD<25, Upper 30% on PAI HRSD>24, Upper 30% on PAI

71 Recovered 100% 80% Patients with discordant predictions ADM CBT+ADM 72% 75% 71% 60% 40% 38% 20% 0% HRSD<25, Lower 70% on PAI HRSD>24, Lower 70% on PAI HRSD<25, Upper 30% on PAI HRSD>24, Upper 30% on PAI

72 Prediction of Relapse Resistance: Identifying the mechanisms of CBT s relapseprevention effects Question: Are all recovered patients alike?

73 Sustained Response Percentage of Patients who Exhibited Sustained Response after Positive Response to Initial Treatment 100% 80% 69% 60% 52% 40% 20% 0% 24% ADM Withdrawn ADM Continued Acute (Prior) CBT

74 2 1 0 Level of CBT skill varied substantially after CBT for depression All Responders (N = 30) Post-treatment Level of Patient Cognitive Therapy Skill *(From Strunk, DeRubeis, Chiu, & Alvarez, 2007)

75 CBT Skill predicts sustained response in follow-up Sustained Responders (N = 19) Responders who Relapsed (N = 10) Post-treatment Level of Patient Cognitive Therapy Skill *(From Strunk, DeRubeis, Chiu, & Alvarez, 2007)

76 Our Mission Going Forward Continue to refine our PAI methods to reveal moderated effects Apply related methods to test for the mediation of treatment effects ( moderated mediation ) Look for the opportunity and funding to conduct prospective tests of prescriptive models Promote the further development of precision mental health

77 Later this month 2 nd Treatment Selection Idea Lab (Summer 2018, University College London) Unveiling of Results of a Modeling Tournament

78 Collaborators on Work Presented Lorenzo Lorenzo-Luaces Indiana U. - Bloomington Zachary Cohen Penn Steve Hollon Vanderbilt Dan Strunk The Ohio State U. Jay Fournier U. of Pittsburgh Lois Gelfand Penn

79 European Collaborators Marcus Huibers Ellen Driessen Annemieke van Straten Pim Cuijpers Steve Pilling Josh Buckman Rob Saunders Arnoud Arntz Lotte Lemmens Suzanne van Bronswijk Sigal Zilcha-Mano Kim de Jong Claudi Bockting Tim Dalgleish Susanne Schweizer