Sequenced Treatment Alternatives to Relieve Depression (STAR*D)

1. Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Stephen Wisniewski, PhD Epidemiology Data Center STAR*D Data Coordinating Center University of Pittsburgh

2. Outline • Overview of STAR*D • Introduction to the Equipoise-Stratified Randomized Design • Implementation of the Equipoise Stratified Randomized Design in STAR*D

3. Overview of STAR*D • Organization • National Coordinating Center (Department of Psychiatry, University of Texas Southwestern Medical Center) • Data Coordinating Center (Epidemiology Data Center, University of Pittsburgh) • 14 Regional Centers • 2-4 Clinical Sites • Primary Care and Specialty (Psychiatry) • Goal: Determine the best “next step” treatments for those with treatment resistant depression

4. Obtain Consent Follow-up Satisfactory Response CIT Unsatisfactory Response* Level 2 *Defined as nonremission Level 1

5. Which treatments to test as second-step treatments? Efficacy studies have identified a number of different types of treatments that were effective in treating depression. After much discussion, debate, arguing, etc., seven treatments were selected for the Level 2: Level 2 Treatment Options Venlafaxin Sertraline Bupropion Cognitive Therapy Citalopram + Cognitive Therapy Citalopram + Bupropion Citalopram + Buspirone Randomization

6. Randomize CIT +CT CIT +BUP-SR CIT +BUS SER BUP-SR VEN-XR CT Level 2 SwitchOptions AugmentationOptions

7. Randomize VEN-XR BUP-SR Switch Level 2A

8. Randomize L-2 Tx +Li L-2 Tx +THY MRT NTP Switch Augmentation Level 3

9. Randomize TCP VEN-XR + MRT Switch Level 4

10. How do we randomly assign a subject to one of the seven treatments in Level 2?

11. Complete Randomization • Patient and clinician must be willing to accept all treatments offered • Advantage: simple approach • Disadvantage: • Subjects and clinicians may have treatment preferences and would not be willing to be randomly assigned to a number of treatments. • Because of this, those that are willing to accept all the treatment assignments do not represent a general population

12. Clinician’s Choice • Define broad classifications and let the clinician choose the treatment within the class. • Patient and clinician must be willing to accept at least one treatment option within each class • Advantages: • Clinician, in theory, knows something about the patient so the choice of the treatment can be optimized • More generalizable

13. Clinician’s Choice • Disadvantages: • Because the assignment of treatment options within a class are not randomly assigned, the “best” treatment option within a class cannot be identified

14. The Equipoise-Stratified Design • Equipoise-Stratified (Lavori et al., 2000) • What is equipoise? • To be in equipoise with respect to a set of prospective treatment options is to regard them as approximately equal in terms of the likelihood of success. • To consider a patient for entry into a study, the clinician and patient must be in equipoise with respect to the treatment options.

15. Example Application of ESRD • Conducting a study to compare four treatments (TX1, TX2, TX3, TX4). • The treatment options can be combined into two treatment strategies • Strategy A (TX1, TX2) • Strategy B (TX3, TX4) • This would create the following acceptability strata

16. Example Application of ESRD Acceptability of Treatment Options

17. Example Application of ESRD Acceptability of Treatment Options

18. Example Application of ESRD • For the equipoise-stratified design, subjects from acceptability strata 1 through 11 are included in the study • For the completely randomized design, only those from acceptability stratum 1 are included in the study • For the clinician’s choice design, the comparison of treatments cannot be made.

19. Example Application of ESRD • Want to do identify best treatment • Conduct all pairwise treatment comparisons • TX1 vs. TX2, TX1 vs. TX3, TX1 vs. TX4, TX2 vs. TX3, TX2 vs. TX4, TX3 vs. TX4 • For a given comparison (e.g., TX1 vs. TX2), compare rate out binary outcome across two treatments, stratified by acceptability stratum (Srata 1, 2, 3 and 6). • Use Mantel-Haenszel chi-square test to combine comparison across strata.

20. Example Application of ESRD • Because conducting many pairwise tests, need to maintain the Type I error to be .05 • Use Bonferroni corrections, so each pairwise comparison is conducted at the .0083 (.05/6) level.

21. The Equipoise-Stratified Design • Equipoise-Stratified • Advantages • Generalizable • Pairwise contrast can be built. For example, to compare A to B, can take subjects that selected either the ABC strata or the AB strata, and were randomly assigned to receive either treatment A or B. • Disadvantage: Complicated

22. The Equipoise-Stratified Design • In the second-step treatments of STAR*D • Patients/clinicians considered four strategies • Medication switch • Medication augment • Cognitive Therapy switch • Cognitive Therapy augment • Could exclude any of these, as long as multiple treatments were still available. • Exclude medication augment, cognitive therapy switch, cognitive therapy augment - OK • Exclude medication switch, medication augment, cognitive therapy switch – not OK

23. ESRD in STAR*D

24. Randomize CIT +CT CIT +BUP-SR CIT +BUS SER BUP-SR VEN-XR CT ESRD in STAR*D Study Design: Level 2 SwitchOptions AugmentationOptions

25. ESRD in STAR*DLevel 2 Approach • Goal: Identify most effective 2nd step treatment • Seven treatment options created too many strata • Create acceptability stratum pooling strategies • Must be willing to accept all medication switches • Must be willing to accept all medication augments • Creates four treatment strategy strata • Medication Switch • Medication Augment • Cognitive Therapy Switch • Cognitive Therapy Augment

26. ESRD in STAR*DLevel 2 Approach • Analysis approach – step up procedure • Identify most effect medication switch • Identify most effect medication augment • Identify most effective treatment strategy • If a most effective medication switch or medication augment was identified, use those randomly assigned to that specific medication in the comparison across strategies. • If a most effective medication switch or medication augment was not identified, pool those randomly assigned to any treatment that strategy for the comparison across strategies.

27. ESRD in STAR*D : Level 2 Expected Acceptability

28. ESRD in STAR*D : Level 2 Observed & Expected Acceptability

29. ESRD in STAR*D • Any treatment: 1.5% (21/1,438 ) • Cognitive Therapy: 25.6% (368/1,438) • Medication Switch: 55.8% (803/1,438) • Medication Augment: 48.4 (696/1,438)