Randomized Trials Outcomes and Adverse Events

1. Randomized TrialsOutcomes and Adverse Events Steven R. Cummings, MD Director, UCSF Coordinating Center Assistant Dean for Clinical Research

2. Fracture Avoidance Trial (FAT) Potential outcomes • All diagnosed fractures • Symptomatic vertebral fractures • New vertebral fractures detected by x-ray • Osteoporosis Quality of Life scale • Number of days of disability due to fracture • Height loss

3. How to start? • Measure all of these outcomes • Call one “primary” and assess all the others as “secondary” outcomes

4. Why one “primary” outcome? • To calculate sample size • Gives that outcome more credibility • In general, the FDA requires that an outcome be “primary” in order to approve a drug for that indication • Primary vs. secondary is artificial, but useful. Credibility should derive from plausibility.

5. Which primary for FAT? Potential outcomes • All diagnosed fractures • Symptomatic vertebral fractures • New vertebral fractures detected by x-ray • Osteoporosis Quality of Life scale • Number of days of disability due to fracture • Height loss

6. Alternatives All fractures Symptomatic vert fx New vert fx on x-ray Quality of Life scale Days of disability Height loss Considerations Most clinically important Inexpensive measure Smallest & shortest study Can be used for FDA approval Which Primary Outcome?

7. Alternatives All fractures Symptomatic vert fx New vert fx on x-ray Quality of Life scale Days of disability Height loss Considerations Most clinically important Inexpensive measure Smallest & shortest study Can be used for FDA approval Other assessments included as ‘secondary’ Which Primary Outcome?

8. Why not make bone density the primary outcome? • Vertebral fracture on x-ray requires 2,000 • BMD as primary requires < 200 women

9. Why not make bone density the primary outcome? • Vertebral fracture on x-ray requires 2,000 • BMD as primary requires < 200 women • Not yet accepted as a ‘valid surrogate’ • What makes a ‘surrogate’ valid?

10. A valid surrogate is Strongly associated with the outcome Treatment induced changes in the surrogate consistently predict changes in the clinical outcomes Believed and accepted BMD Low BMD strongly predicts fractures Treatment induced changes in BMD underestimate reduction in fractures. Inconsistently. FDA does not accept it Why not use BMD as a surrogate?

11. Composite endpoints Increase number of events Improve power… unless they dilute effect Must reflect the same (or very similar) underlying biology Combining all fractures Doubles the number of events Txs decrease vert fxs 50%, other types 0-25% Fractures have different relationships to bone density and trauma How about combining events?

12. One more thing... • Women can suffer recurrent fractures • Alternatives • Number of fractures • Number of women who suffer at least one fracture • Time to first fracture

13. Count subjects or events? • Difficult issue • Counting multiple events increases power • Conservative approach: count subjects • Because events cluster in subjects, are not statistically independent. Counting events tends to overestimate the effect.

14. On the other hand... • Counting subjects (or time to first event) • Ignores effect of treatment on recurrent events • Can underestimate the long-term effect of treatment by “depletion of susceptibles.”

15. Depletion of susceptibles • Assume a randomized trial of a treatment to prevent fractures: 100 pbo vs. 100 treatment • 50 subjects susceptible; 50 would NOT fx • No treatment (on placebo) 20% of susceptibles fracture/year • Treatment reduces risk of fracture 50% in susceptibles, year after year

16. Depletion of susceptibles • 100 pbo vs. 100 treatment • No treatment (on placebo) 20 fracture/year • Treatment reduces fracture 50% year after year

17. Depletion of susceptibles underestimates long-term effects PBO TX Fx N (susc) Fx N (susc) RR Baseline 100 (50) 100 (50) Year 1 10 90 (40) 5 95 (45) 0.5 Year 2 8 82 (32) 5 90 (40) Year 3 6 76 (26) 4 86 (36) Year 4 5 71 (21) 4 82 (32) 0.7

18. The lessons • Keep subjects in treatment and follow-up to the degree it is ethical • Don’t stop after 1st event: assess recurrent events • Be careful about estimating long-term effects of treatment • Analyze effect on recurrent outcomes • Consider ‘frailty models’ (time between events rather than time to the first event)

19. Adverse Events • Alternative approaches • Elicited vs. volunteered • Simple counts vs. severity • At the end vs. along the way • The FDA system • Serious “AEs” • Attribution to the study treatment

20. Pro elicited Standardized More sensitive Easier to code Con: Miss unexpected AEs More positives Milder, less certain cases Approaches to AEsVolunteered vs. elicited Pro volunteered • Catch unexpected AEs • Fewer data to code • Finds serious cases Con: • Unstandardized • Less sensitive; misses cases • Hard to code

21. Which approach is most likely to find real AEs? • Evidence is mixed • Sensible approach: standard questions to elicit uncommon AEs known to be related to drug. • Additional open ended questions to capture unexpected AEs.

22. The Bunion Problem • FIT Trial of alendronate in 6,400 women for 4 years • Recorded over 20,000 episodes of URIs (and thousands of reports of bunions!) • Enormous data management effort and cost • How could this be avoided?

23. How to minimize ‘nuisance’AEs • Elicit uncommon, plausible and important AEs • Limit collection of minor AEs to samples of subjects

24. FDA AE classifications • Serious AEs • Deaths • Hospitalized overnight • Cancer (except skin cancer) • Birth defects • SAEs “definitely or probably” due to study drug must be reported to company and by the company to FDA in 24°

25. Attribution • Serious AEs* must be classified as • Definitely • Probably • Possibly, or • Not... ...related to the study drug *This is only required of SAEs

26. Attribution • Attributions to drug as generally as likely with placebo as with active drug…