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Multiple Interventions for Multiple Targets

Multiple Interventions for Multiple Targets. Emilia Bagiella, PhD Columbia University. Traumatic Brain Injury (TBI). An injury to the head arising from blunt or penetrating trauma or from acceleration-deceleration forces Damage can be focal or diffuse Closed head injury

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Multiple Interventions for Multiple Targets

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  1. Multiple Interventions for Multiple Targets Emilia Bagiella, PhD Columbia University

  2. Traumatic Brain Injury (TBI) • An injury to the head arising from blunt or penetrating trauma or from acceleration-deceleration forces • Damage can be focal or diffuse • Closed head injury • Penetrating head injury

  3. Traumatic Brain Injury (TBI) • An estimated 1.5 million head injuries occur every year in the United States • TBI is the leading cause of death and disability in children and adults ages 1 to 44 • Approximately 52,000 deaths every year

  4. Traumatic Brain Injury (TBI) • More than 5.3 million Americans, 2% of the U.S. population, currently live with disabilities resulting from TBI • Direct medical costs and indirect costs (e.g. lost productivity) due to TBI is estimated at $56.3 billion annually

  5. How does TBI happen? • Falls (28%) • Motor vehicle-traffic crashes (20%) • Struck by/against events (19%); and • Assaults (11%)

  6. Short term effect of TBI Initial acute insult (diffuse axonal injury) followed by a cascade of events involving multiple secondary injuries • Significant tissue damage • Ischemia/hypoxia • Brain swelling • Brain hemorrhage

  7. Long term consequences • Wide range of functional changes affecting thinking, sensation, language, and/or emotions. • Epilepsy and increase the risk for Alzheimer’s and Parkinson’s disease, and other brain disorders • Virtually no injury is without consequence

  8. Etiology • Unpredictable • No known risk factors • Low socio-economic class • Drug and alcohol abuse • War • Affects mostly otherwise healthy individuals

  9. Clinical Trials in TBI So far… • Difficult to design and perform • Hampered by many problems • Most trials have failed to show improvement in (any) outcomes

  10. Clinical Trials in TBI • Have learned more from clinical practice than from clinical trials • Guidelines based on common sense • Back to square 1

  11. Possible interventions Pharmachological Physiological - Surgical • Neuroprotective agents • Steroids • Free radicals scavangers • Insuline like growth factor (IGF) • Progesterone • Biomarkers(?) • Hypothermia • Decompressive craniectomy • O2 monitoring • ICP/CBF management

  12. Problems • Pre-clinical and clinical data are disconnected • Need adequate pre-clinical TBI models • Virtually no early phase trials in TBI • Dosing • Duration of treatment • Time of treatment initiation

  13. Design Problems • Weaknesses in study design • Insufficient power/sample size • Inadequate outcome measures or lack of sensitivity of the outcomes measure • Too small effect sizes • Too variable population

  14. Facts • No single measure can capture the multidimensional nature of TBI outcome • Combination of drugs are needed for the treatment of TBI

  15. What should the outcome be? ?

  16. Glasgow Outcome Scale (GOS) • 5-category scale • Gold standard for trial in TBI • The only measure of functional recovery accepted by the FDA • Very broad and not specific • Non differential misclassification • Considerable loss of statistical power and the attenuation of the true treatment effect

  17. Goal • To identify measures that together would reflect the “global” status of TBI patients • Functional, physical, emotional, cognitive, and social spheres

  18. What should the outcome be?

  19. Global Statistical Analysis Approach • Offers a method to utilize several outcome measures without need to pre-specify one as primary • Avoids loss of power due to multiple comparisons • Easily interpreted and of direct clinical interest

  20. Binary Outcome Let Yi = 1(0) denote the success(failure) for the i th measure (i =1, … ,k) Let T = 1(0) for experimental(standard) Tx.

  21. Binary Outcome • Interest centers on treatments where (even approximately) b1 = b2 = … = bk = b • The results of the analysis are estimate of the common odds ratio, exp(b ), together with a standard error, z-score, p-value and confidence intervals

  22. Statistical power is greatest when the odds ratios are all equal, but that is nor required to test H0: all bi = 0 (no treatment effect with any measure) • Analogous to the Mantel-Haenszel procedure with correlated tables

  23. Power is reduced if some bi = 0 or are of opposite signs. But that is an ambiguous situation where careful judgment is required, in which case reduced power may not be inappropriate

  24. The methods can be easily extended to clinical trials with stratified randomization. • Estimation of b is absolutely conventional using weighted least squares. • Further adjustment for additional covariates can be handled by GEE methods.

  25. Benefits • Targets multiple areas of recovery • Reduces the trial size • Has greater statistical power than any single outcome measure

  26. Limitations • Need to know the correlation among outcomes • (May) still need large sample size • FDA (?)

  27. Example: COBRIT trial • Fix OR=1.4 (8% improvement on the GOS) • Choose 9 measures of functional and cognitive status • Fix the type I error at 0.05 and the power at 85% • Needed 1124 patients to detect the effect size of interest. • Would need 1836 participants to run a trial with the same power, type I error and effect size with GOS alone.

  28. What should the treatment be?

  29. Single treatments do not work • “Cocktails” or combination treatment may work • What to combine and at what dose?

  30. Sequential Selection Procedure • Dichotomize the outcome as success or failure • Use a coin tossing model for the outcome data • Determine the number of treatment combinations to be tested (k) • Determine the number of treatment combinations to be chosen (b)

  31. Levin-Robbins-Leu (LRL) Procedure • Choose a positive integer r to guarantee high probability of correct selection • Compare all k treatment combinations at the same time • Eliminate “inferior” treatments as soon as they follow r successes behind the treatment with the currently b largest tally

  32. Levin-Robbins-Leu (LRL) Procedure • Recruit “superior” treatments as soon as they pull r successes ahead of the treatments with the currently held (b+1) largest tally • The procedure stops when b treatments are recruited and k-b treatments are withdrawn

  33. Benefits • Very efficient in pre-clinical studies • It allows early stopping • Reduces total number of experiments • Maximize probability of correct selection of the best treatment

  34. Limitations • Difficult to apply to clinical setting • Must start from a small number of treatments • No hypothesis testing

  35. Example • 13 agents • 3 doses • Start from 558 two-way treatment/dose combinations • Assume 10 combinations are truly beneficial with 63% probability of success • LRL procedure requires 2502 experiments compared to 11160 for fixed sample size

  36. Open Problem Outcome Tx Outcome Tx Outcome Tx Tx Outcome

  37. THANK YOU!

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