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Issues in Trial Design and Analysis of
CBER Precision Medicine Products
Shiowjen Lee, PhD
Office of Biostatistics and Epidemiology
Center for Biologics Evaluation and Research
US Food and Drug Administration
SAMSI Workshop, August 13, 2018
Disclaimer: This presentation reflects views of the presenter and
should not be construed to represent FDA’s view or policies
2
Brief Background
 CBER products with precision medicine
implications:
 Autologous products
 Companion diagnostics products
e.g. inclusion of development of a testing tool for
selection of pts to be treated with an investig.
biologic product
3
Brief Background (cont.) – Autologous Prod.
e.g., CAR-T Therapy manufacturing process
Source: ODAC on July 12, 2017
Cancer Gene Therapy (2015) 22, 79–84
Vector
4
Brief Background (cont.)
 Companion diagnostic products
 Two components
 Investig. biologic product
 Marker test to select pts to be treated
 Ideally
 Marker with biological basis and validated
 Clear cut-point to differentiate M+ and M−
pts
5
Challenges/Issues – Autologous Product
Manufacturing issues
 Manufacturing failure (none or insufficient
product)
Consequence:
a. Affect efficacy outcome, e.g. insufficient prod.
b. Impact study power (in addition to missing obs
or/and delayed effect during trial)
6
Challenges/Issues – Autologous Prod. (cont.)
Manufacturing issues
 Time to completion of product manufacture
a. Subj became ineligible/progression
b. Subj ineligible/progression followed in the study,
but allow to receive other therapies outside study
Consequence:
a. Affect study power
b. Impact interpretation
7
Example – Autologous
Autologous vaccine
Phase 3 study:
– Control: SOC
– Primary endpoint: progression-free survival (PFS)
– Intent-to-treat (ITT) specified as the primary
 Product mfg:
– Length: ~5-10 months
8
Example (cont.): Design Schema and Enrollment
Assess
CR/CRu
BiopsyChemo
Randomize
1:1 Vaccine
Control
5 to 10 Months
Vaccine Production
8 Months
Treatment
350
300
Randomization 150 150
Subjs ineligible/progress
100
95
Start Here
9
• Data analyses of mPFS:
– Inclusion: 25 vs 23 month for V vs C
– Exclusion: 45 vs 30 month for V vs C
• > 30% of subjects became ineligible/progression
• Two sets of analyses different
Example (cont.)
9
10
Example (cont.)
PFS for all randomized subj
proportional hazard
assump. not met
11
Companion Diagnostic Product – Basic Study Design
A. Randomize-All design:
B. Marker-Stratified design:
C. Enrichment design:
12
CBER Companion Diagnostic Submissions
 Marker validation and Phase 3 trials concurrently
 Recommend randomized-all or marker-stratified design
 Statistical testing among groups using alpha
adjustment* strategy
*To control overall rate of erroneously concluding trt efficacious when in fact product
isn’t
13
Statistical Testing: M+ vs. Overall
Two common statistical testing strategies:
 Hierarchical order testing M+ pts first (use α = 0.05)
then overall (α = 0.05) OR
 Allocate
M+ pts Trt shown benefit (stat. sign.)
Overall Trt shown benefit (stat. sign.)
Q: Label for overall?



=
+=
04.0
01.0
overallfor
ptsMfor
α
α
14
Summary
 Autologous products:
How to account for manufacturing issues into design
and analysis?
 Companion diagnostic products:
What to consider in statistical testing of benefits in M+
and overall pop. to warrant labeling of overall pop?

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PMED Opening Workshop - FDA Panel - Issues in Trial Design & Analysis of CBER Precision Medicine Products - Shiowjen Lee, August 13, 2018

  • 1. Issues in Trial Design and Analysis of CBER Precision Medicine Products Shiowjen Lee, PhD Office of Biostatistics and Epidemiology Center for Biologics Evaluation and Research US Food and Drug Administration SAMSI Workshop, August 13, 2018 Disclaimer: This presentation reflects views of the presenter and should not be construed to represent FDA’s view or policies
  • 2. 2 Brief Background  CBER products with precision medicine implications:  Autologous products  Companion diagnostics products e.g. inclusion of development of a testing tool for selection of pts to be treated with an investig. biologic product
  • 3. 3 Brief Background (cont.) – Autologous Prod. e.g., CAR-T Therapy manufacturing process Source: ODAC on July 12, 2017 Cancer Gene Therapy (2015) 22, 79–84 Vector
  • 4. 4 Brief Background (cont.)  Companion diagnostic products  Two components  Investig. biologic product  Marker test to select pts to be treated  Ideally  Marker with biological basis and validated  Clear cut-point to differentiate M+ and M− pts
  • 5. 5 Challenges/Issues – Autologous Product Manufacturing issues  Manufacturing failure (none or insufficient product) Consequence: a. Affect efficacy outcome, e.g. insufficient prod. b. Impact study power (in addition to missing obs or/and delayed effect during trial)
  • 6. 6 Challenges/Issues – Autologous Prod. (cont.) Manufacturing issues  Time to completion of product manufacture a. Subj became ineligible/progression b. Subj ineligible/progression followed in the study, but allow to receive other therapies outside study Consequence: a. Affect study power b. Impact interpretation
  • 7. 7 Example – Autologous Autologous vaccine Phase 3 study: – Control: SOC – Primary endpoint: progression-free survival (PFS) – Intent-to-treat (ITT) specified as the primary  Product mfg: – Length: ~5-10 months
  • 8. 8 Example (cont.): Design Schema and Enrollment Assess CR/CRu BiopsyChemo Randomize 1:1 Vaccine Control 5 to 10 Months Vaccine Production 8 Months Treatment 350 300 Randomization 150 150 Subjs ineligible/progress 100 95 Start Here
  • 9. 9 • Data analyses of mPFS: – Inclusion: 25 vs 23 month for V vs C – Exclusion: 45 vs 30 month for V vs C • > 30% of subjects became ineligible/progression • Two sets of analyses different Example (cont.) 9
  • 10. 10 Example (cont.) PFS for all randomized subj proportional hazard assump. not met
  • 11. 11 Companion Diagnostic Product – Basic Study Design A. Randomize-All design: B. Marker-Stratified design: C. Enrichment design:
  • 12. 12 CBER Companion Diagnostic Submissions  Marker validation and Phase 3 trials concurrently  Recommend randomized-all or marker-stratified design  Statistical testing among groups using alpha adjustment* strategy *To control overall rate of erroneously concluding trt efficacious when in fact product isn’t
  • 13. 13 Statistical Testing: M+ vs. Overall Two common statistical testing strategies:  Hierarchical order testing M+ pts first (use α = 0.05) then overall (α = 0.05) OR  Allocate M+ pts Trt shown benefit (stat. sign.) Overall Trt shown benefit (stat. sign.) Q: Label for overall?    = += 04.0 01.0 overallfor ptsMfor α α
  • 14. 14 Summary  Autologous products: How to account for manufacturing issues into design and analysis?  Companion diagnostic products: What to consider in statistical testing of benefits in M+ and overall pop. to warrant labeling of overall pop?