WELCOME BACK!
What did we learn yesterday?
•
•
•
•
•
What’s happening in NSCLC – updates from ASCO
Clinical trial endpoint selection
Basics principles of Phase 1 trials
Design considerations in Ph II studies
Design considerations for biomarker-driven trials
What will we learn today?
• How to optimize clinical trial design in oncology
– The role of translational research
– Impact of key statistics principles and methodology
– How to practice good clinical trial compliance
– Your role as a PI
– How to evaluate well-designed vs poorly designed clinical trials
• What you are studying!
– Research project coaching
Afternoon coaching session guidance
• You will have the opportunity to present and receive guidance on your
ongoing research project
• All delegates must participate!
• Each participant will have 5 minutes to present
– Informally, with or without slides, you can use flipcharts or just speak
• You should include:
– What clinical question do you hope to answer?
– Project objective(s)
– Project planning/implementation
– Potential results/conclusions
• Consider any questions for TOP network(including faculty) or opportunities for
partnership with co-delegates
• Following each presentation there will be 5 minutes of discussion/questions
Translational Research in Non–Small Cell Lung
Cancer (NSCLC):
What Are the Available “Tools” & How Can You Use
Them?
David R. Gandara, MD
University of California Davis
Comprehensive Cancer Center
7
Available “Tools” for Translational Research in NSCLC: Interaction of
Pathology, “Omics,” and Immuno-Biology
From “All Patients Are the Same”
From Histology to
Prognostic and/or
Predictive Biomarkers
to Inter- and Intra-Patient
Heterogeneity in Tumour Biology
& Immuno-Biology
Translational Research
Personalised Therapy for
Individual Patients With NSCLC
Adapted from Gandara et al. Clin Lung Cancer. 2012.
8
Moving From Histologic Classification to Molecular
Classification of NSCLC Patients Into Prognostic or
Predictive Subgroups for Therapy
• Histologic subtyping groups tumours based on microscopic pattern recognition
by a pathologist
• At best, Histology = “crude molecular selection”
EGFR Mutation
Positive ALK FISH
9
Evolution of NSCLC Subtyping From Histologic to Molecular-Based
NSCLC
as one
disease
First
Targeted
Therapies
in NSCLC
ALK
EGFR
Li, Mack, Gandara et al. JCO. 2013 (adapted from Pao et al).
10
Magnitude of Genomic Derangement Is
Greatest in Lung Cancer
n=109 81
64
38
316
100
17
82
Mutations
Per Mb DNA
28
119
21
40
20
Carcinogen-induced Cancers
100 / Mb
10 / Mb
Hematologic &
Childhood Cancers
Ovarian, Breast,
Prostate Cancers
0.1 / Mb
Squamous
Adenoma
1 / Mb
??
Adapted from The Cancer Genome Atlas Project: Govindan & Kondath et al. Nature. 2013.
11
Lung Cancer Complexity on an Individual Patient Basis: Squamous-Cell
Lung Cancer Examples (“Circos”)
LUSC-66-2756
LUSC-34-2600
LUSC-56-1622
LUSC-60-2695
LUSC-43-3394
LUSC-60-2711
LUSC-34-2609
LUSC-60-2713
From Ramaswamy Govindan. TCGA (The Cancer Genome Atlas).
12
Integration of Biomarkers Into Clinical Practice: Past, Current &
Future
Empiric Approach (Past)
(Compound-Based Therapy):
Clinical-histologic factors to select drugs for
individual patients
1. Histomorphological
Diagnosis:
Cancerous
2. Molecular Diagnosis:
Archival FFPE
tumour specimens
Archival cancer
specimens
Macro- or
Micro-dissection
of Tumours
Extract tumour
nucleic acids:
DNA and RNA
Representative technologies:
Current Approach (Target-Based Therapy V1.0):
Single gene molecular testing for decision-making in
individual patients
Evolving Approach (Target-Based Therapy V2.0):
Multiplexed molecular tests with increased sensitivity &
output for decision-making in individual patients
Near-Future Approach (Patient-Based Therapy):
Genomic profiling by high throughput next generation
sequencing for decision-making in individual patients
From Li, Gandara et al. JCO. 2013.
Plasma cfDNA by NGS
Single Biomarker Tests:
• Sanger DNA Sequencing
• RT-PCR
• FISH
• IHC
Multiplex, Hot Spot Mutation Tests:
• PCR-based SNaPshot
• PCR-based Mass Array SNP
• Sequenom
Initial High-Throughput Technologies:
• SNP/CNV DNA microarray
• RNA microarray
Next-Generation Sequencing (NGS):
• Whole Genome or Exome Capture Sequencing (DNA)
• Whole or Targeted Transcriptome Sequencing (RNA)
• Epigenetic profiling
13
Comprehensive Cancer Genomic Test: 200+ Genes
Foundation Medicine One
<14 days
14
Guardant360 Panel 2015: Plasma NGS
Complete* or Critical Exon Coverage in 68 Genes
Point Mutations
Amplifications
Fusions
Indels
AKT1
ALK
APC
AR
AR
ALK
EGFR exon 19 deletions
AFAR
ARID1A
ATM
BRAF
BRAF
RET
EGFR exon 20 insertions
BRCA1
BRCA2
CCDN1
CCND2
CCNE1
ROS1
CCNE1
CDH1
CDK4
CDK6
CDK4
NTRK1
CDKN2A
CDKN2B
CTNNB1
EGFR
CDK8
ERBB2
ESR1
EZH2
FBXW7
EGFR
FGFR1
FGFR2
FGFR3
GATA3
ERBB2
GNA11
GNAQ
GNAS
HNF1A
FGFR1
HRAS
IDH1
IDH2
JAK2
FGFR2
JAK3
KIT
KRAS
MAP2K1
KIT
MAP2K2
MET
MLH1
MPL
KRAS
MYC
NF1
NFE2L2
NOTCH1
MET
NPM1
NRAS
NTRK1
PDGFRA
MYC
PIK3CA
PTEN
PTPN11
RAF1
PDGFRA
RET
RHEB
RHOA
RIT1
PIK3CA
ROS1
SMAD4
SMO
SRC
RAF1
STK11
TERT**
TP53
VHL
*Complete exon coverage for genes in bold; **Includes TERT promoter region.
15
Available “Tools” for Translational Research in Advanced
NSCLC
“Targeted Therapy”
Chemotherapy
Histologic
Subtyping for
Chemotherapy
? Targeted
Nintedinib?
Necitumumab?
Ramucirumab?
Checkpoint Immunotherapy
Anti-PD-1
and PD-L1
Anti-CTLA-4
Targeted
TKIs:
-EGFR
-ALK
-ROS1
Translational Research Opportunities: How do we optimize use of
these “tools” (therapeutic modalities) in order to create
new treatment paradigms?
Targeted Therapies in Oncogene-Driven NSCLC:
De Novo & Acquired Resistance
• Targeted Therapies against Oncogene-Driven Cancers, EGFR mutation+ (erlotinib) or ALKfusion+ (crizotinib), improve response and PFS when compared with chemotherapy
• Even in these most sensitive cancers, approximately 25% to 40% do not respond to TKI
therapy (de novo resistance)
• Even in these most-sensitive cancers, acquired resistance is universal, with PFS averaging
≈10-14 months
Oncogene-driven
NSCLC
Gandara, Redman et al. Clin Lung Cancer. 2014.
17
Evolutionary Biology & Acquired Tumour Resistance
• Intra-tumour heterogeneity is present at
baseline (scenarios 1 & 2)
Scenario 1
Scenario 2
“Driver” Oncogene
“Driver” Oncogene
Evolution
over time
with therapy
Evolution
over time
with therapy
New
“Driver”
New
“Driver”
• Reducing sensitive clones by therapy
permits unopposed growth of less fit
resistant clones
or emergence of a new clone
(“Tumour Darwinism”)
• Separating “new drivers” from
“passengers” is complex
• This process is dynamic, not static
• Original sensitive clone is still present ].
at time of resistance
Original
Sensitive
Clone
Adapted from Gandara et al. Clin Lung Cancer. 2012.
18
Available “Tools” for Translational Research:
Re-Biopsy to Assess Tumour Evolution
Referring
Physician
Identify
Patient
Pathologist
Multidisciplinary
Team
(Tumour Board)
Identify
Target
Lesion
Med Oncologist
Thoracic Surgeon
Radiation Oncologist
Pulmonologist
Radiologist
Pathologist
Pulmonologist
Interventional Radiologist
Surgeon
Histology Evaluation
Determine
Therapy
Biopsy
Molecular Biomarker
Testing
When
Progression
 Re-Biopsy
Oncologist
T
r
e
a
t
Determine
New Therapy
Treat
When
Progression
 Re-Biopsy
Adapted from: Gandara. ASTRO/ASCO/IASLC Symposium on Molecular Testing, 2012.
19
Emergence of ALK Resistance Mechanisms
After Crizotinib
•
•
•
•
Secondary resistance ALK mutations
ALK gene copy number increase
Transition to EGFR mutation
Transition to KRAS mutation
Consistent with mathematical models
of evolutionary biology
Doeble, Camidge et al. CCR. 2012.
20
Schema for Multidisciplinary Integration of Biomarker
Testing in Advanced-Stage NSCLC: Looking for “Actionable” Oncogenes
Referring
Physician
Identify
Patient
Pathologist
Multidisciplinary
Team
(Tumour Board)
Identify
Target
Lesion
Med Oncologist
Thoracic Surgeon
Radiation Oncologist
Pulmonologist
Radiologist
Pathologist
Pulmonologist
Interventional Radiologist
Surgeon
Histology Evaluation
Determine
Therapy
Biopsy
Molecular Biomarker
Testing
When
Progression
 Re-Biopsy
Plasma cfDNA
Oncologist
T
r
e
a
t
Determine
New Therapy
Treat
When
Progression
 Re-Biopsy
Plasma cfDNA
Adapted from: Gandara. ASTRO/ASCO/IASLC Symposium on Molecular Testing, 2012.
21
Association Between pEGFR mut+ at C3 and PFS/OS (Both Treatment
Arms Combined)
PFS
OS
C3 mut+
C3 mut+
C3 mut–
C3 mut–
PFS probability
0.8
0.6
0.4
0.2
0.8
0.6
0.4
0.2
7.2 12.0
0
0
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
42 42 35 28 14 7 6 4 1 1 1 1 0 0 0
80 80 77 65 59 47 40 34 32 28 23 19 13 10 7
18.2
31.9
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
Time (months)
Patients, n
C3 mut+
C3 mut–
Median = 18.2 months
(95% CI: 14.2–27.4)
Median= 31.9 months
(95% CI: 23.5–undefined)
HR = 0.51
(95% CI: 0.31–0.84);
P=0.0066
1.0
OS probability
Median = 7.2 months
(95% CI: 6.0–7.8)
Median =1 2.0 months
(95% CI: 9.6–16.5)
HR = 0.32
(95% CI: 0.21–0.48);
P<0.0001
1.0
Time (months)
0
3
0
0
Patients, n
C3 mut+
C3 mut–
42 42 42 41 37 32 30 28 23 21 18 14 14 12 9 4 3 2 0
80 80 80 77 77 77 76 71 68 64 59 52 38 29 22 12 3 1 0
Mok et al. WCLC 2013.
22
Approaches to Acquired Resistance in Oncogene-driven Cancers
(EGFR MT & ALK Fusion)
Systemic-PD
Advanced
NSCLC With
Oncogene-driven
Cancer
Targeted
TKI
-EGFR Mutation
-ALK Fusion
Switch Therapy:
Chemotherapy or 2nd/3rd gen TKI
RECIST
Response
Subsequent
Systemic PD
Continue same TKI alone
(post-progressive disease)
Add Therapy to TKI
-Chemotherapy ?
-Another Targeted Agent?
Re-biopsy
Gandara et al. Clin Lung Cancer. 2014.
23
IMPRESS: Phase III Trial of Post-progression Gefitinib/Chemotherapy vs Chemotherapy
Alone in EGFR Mutation–Positive NSCLC After Prior Response (Acquired Resistance)
Gefitinib 250 mg +
cisplatin + pemetrexed
up to 6 cycles
(n=133)
• Stage IIIB/IV NSCLC
• EGFR mutation positive
• WHO PS 0–1
• Prior response* to 1st-line
gefitinib
PD
• PD <4 weeks prior to study
(n=265)
R
PD
Primary endpoint: PFS
1:1
Placebo +
cisplatin + pemetrexed
up to 6 cycles
(n=132)
PD
Secondary endpoints
• OS, ORR, DCR
• Safety and tolerability, health-related QoL
*CR/PR ≥4 months or SD >6 months.
Mok et al. ESMO-Ann Oncol. 2014;25(suppl 4): abstr LBA2_PR.
24
IMPRESS: Phase III Trial of Post-progression Gefitinib/Chemotherapy vs Chemotherapy
Alone in EGFR Mutation–Positive NSCLC After Prior Response (Acquired Resistance)
PFS (primary endpoint; ITT)
OS (ITT; 33% of events)
Gefitinib
(n=133)
Placebo
(n=132)
5.4
5.4
Median PFS, months
Response:
Number of events, n (%)
34%
0.9
0.9
0.8
0.8
0.7
0.7
0.6
0.5
0.4
0.3
0.2
14.8
17.2
50 (37.6)
37 (28.0)
HRa (95% CI) = 1.62 (1.05, 2.52); p=0.029
1.0
Probability of PFS
Probability of PFS
32%
Placebo
(n=132)
Median OS, months
HRa (95% CI) = 0.86 (0.65, 1.13); p=0.273
1.0
Gefitinib
(n=133)
0.6
0.5
0.4
0.3
0.2
Gefitinib (n=133)
0.1
0
0
2
4
Gefitinib (n=133)
0.1
Placebo (n=132)
Placebo (n=132)
0
6
8
10
12
14
0
2
4
Time of randomisation (months)
Patients at risk:
Gefitinib
133
Placebo
132
110
100
88
85
40
39
25
17
12
5
6
8
10
12
14
16
18 20
22
24
26
2
4
0
2
0
0
Time of randomisation (months)
6
4
0
0
Patients at risk:
Gefitinib
133
Placebo
132
125
129
111
119
88
94
64
76
43
55
27
39
19
27
12
16
8
10
4
7
Mok et al. ESMO-Ann Oncol. 2014;25(suppl 4): Abstract LBA2_PR.
25
Mechanisms of EGFR TKI Resistance (Selected)
• Secondary EGFR mutation
(ie, T790m)
2nd Gen EGFR TKIs
ie, Afatinib
Afatinib/Cetuximab
rd
3 Gen- AZ9291, CO1686
• Bypass signaling via ERBB3
Anti-ERBB3 drugs
ie, MM151 MoAB
• MET over-expression
MET Inhibitors
ie, MET-Mab (MoAB)
ARQ197 (TKI)
• PIK3CA Mutation/AKT
ie, BKM120 (PIK3CA)
ie, MK2206 (AKT)
& Others
HSP inhibitors
ie, Ganetespib
AUY922
Adapted from Engelman et al.
26
Best Response in EGFR-Mutated T790M+ Cancers
CO-1686
(Sequist: Targ Tx 2015)
AZD9291
(Jänne: Targ Tx 2015)
27
28
ETCTN Project Team Proposals: AZD9291 in EGFR-mutated NSCLC
Post-progression After Erlotinib
29
Clinical Trial Designs to Address Circumvention of Acquired
Resistance in Oncogene-Driven NSCLC
Prolongation
of Remission
(delay time to PD)
Oncogene-driven
NSCLC
Targeted TKI Monotherapy
(Standard of Care)
Biopsy
AdvancedStage
NSCLC
Identification
of
Driver
Oncogene
EGFR Mutation
Targeted TKI Monotherapy
(2nd-Generation Agent)
Multi-drug Targeted Therapy
Gandara et al. Clin Lung Cancer. 2014.
30
Phase II/III Trial of Afatinib With or Without Cetuximab in
1st-Line Therapy of EGFR-mutated NSCLC (S1403)
Stage IIIB-IV NSCLC
with EGFR mutation
1st Line
EGFR TKI naive
R
A
N
D
O
M
I
S
A
T
I
O
N
Afatinib*
Afatinib +
Cetuximab*
*at PD: Biopsy for genomic study
& PDX development (selected patients)
PD: progressive disease
PDX: patient-derived xenograft
PIs: Goldberg, Lilenbaum, Politi.
31
Modulator Effects of EGFR MoAB Cetuximab:
Afatinib (BIBW) + Cetuximab in EGFR T790M+ GEMMs
Pretreatment
Cetuximab
H
Cetux/BIBW
H
H
Pretreatment
BIBW
H
Cetux/BIBW
H
H
C
Control
N
T1
T2
T1 T2
B
T1
T2
B+C
T1
T2
pEGFR
tEGFR
Regales et al. J Clin Invest. 2009.
Actin
32
Afatinib + Cetuximab in EGFR-mutated NSCLC Refractory to EGFR
TKI
Response rate: ≈30%
Clinical benefit (DCR): 75%
Janjigian, Pao et al. Cancer Discovery. 2014;4:1036-1045.
33
Phase II/III Trial of Afatinib With or Without Cetuximab in 1st-Line Therapy of
EGFR-mutated NSCLC (S1403)
Stage IIIB-IV NSCLC
with EGFR mutation
1st Line
EGFR TKI naive
R
A
N
D
O
M
I
S
A
T
I
O
N
Afatinib*
Afatinib +
Cetuximab*
*at PD: Biopsy for genomic study
& PDX development (selected patients)
PD: progressive disease
PDX: patient-derived xenograft
PIs: Goldberg, Lilenbaum, Politi.
34
Strategies for Integrating Biomarkers Into Clinical Trial Designs
for NSCLC When Viewed as a Multitude of Genomic Subsets
Evolution of NSCLC  Histologic Subsets  Genomic Subsets
Unmet needs addressed by master
protocols:
• How to develop drugs for
uncommon-rare genotypes?
• How to apply broad-based
screening (NGS)?
• How to achieve acceptable turnaround times for molecular testing
for therapy initiation (<2 weeks)?
• How to expedite the new drugbiomarker FDA approval process
(companion diagnostic)?
Li, Mack, Kung, Gandara. JCO. 2013.
35
“Strategies for Integrating Biomarkers Into Clinical
Development of New Therapies for Lung Cancer”
A Joint NCI Thoracic Malignancies Steering Committee-FDA Workshop
Bethesda MD – February 2-3, 2012
• Trial Design Challenges in the Era of Biomarker-driven
Trials
– Innovative Statistical Designs
– Challenges for Community Oncology Practice Participation
– The Patient Perspective
• Drug & Biomarker Co-Development in Lung Cancer
– Need for Early Co-Development
– Need for Improved Pre-Clinical Models With Clinical
Relevance
• Development of Future Lung Cancer Trials
– TMSC Master Protocol Task Force in NSCLC
– Biomarker-driven trial designs in both early stage adjuvant
therapy & advanced-stage NSCLC
– Account for inter-patient tumour heterogeneity & genomic
complexity of NSCLC
36
Master Protocol Subtypes
Umbrella
Trials
Basket
Trials
Single Type of Cancer:
Test multiple drug-biomarker
combinations
Multiple Cancer Types:
Test multiple drugs against
single or multiple biomarkers
• BATTLE
• Imatinib Basket
• I-SPY2
• BRAF+
• SWOG Lung MAP (S1400):
adv SCCA
• NCI MATCH
• ALCHEMIST: early stage
NSCLC
• ALK Master Protocol: ALK+
NSCLC
37
ALCHEMIST Trial Schema
Non-Match: Phase III trial
of nivolumab vs placebo
X 1 year after any adj tx
(EGFR & ALK testing performed by RGI)
38
ALK Master Protocol: Proposed Trial Design
Control Arm
(Criz.)
Control Arm
(Criz.) Crizotinib
ALK positive
NSCLC Treatment
Naïve patients
Central
Confirmation ALK
NGS
Drug A
Drug B
Drug C
Drug A To B
Drug A To C
Drug A To Criz.
Cross
Over
PD
NGS
PD
NGS
New Biopsy
Drug B to A
Drug B To C
Drug B to Criz.
Archived tissue
From S Malik: NCI.
Drug Criz. To A
Drug Criz. To B
Drug Criz. To C
New Biopsy
Drug C
39
Rationale for “MASTER PROTOCOL” in SCCA
• SCCA represents an unmet need
Therapeutic targets
SCCA-TCGA 2012
• Candidate molecular targets are
available from results of TCGA &
other studies, identified by a
biomarker
• Drugs (investigational) are now
available for many of these targets
• Trials can be designed to allow
testing & registration of multiple
new drug-biomarker combinations
at the same time (“MASTER
PROTOCOL” concept)
• Result of this concept is Lung-MAP
(S1400), activated in June 2014
40
S1400 Lung-MAP Protocol: A Unique Private-Public Partnership
Within the NCTN
Alliance
SWOG
NCI-C
S1400
Master
Protocol
ECOGAcrin
NRG
41
S1400: MASTER LUNG-1: Squamous Lung Cancer —
2nd-Line Therapy
CT
Biomarker
Profiling (NGS/CLIA)
Biomarker
Non-Match
Multiple Phase II-III Sub-studies with Rolling Opening & Closure
Biomarker A
TT A
CT
Primary Endpoint
PFS/OS
Biomarker Β
TT B
CT
Primary Endpoint
PFS/OS
Biomarker C
TT C+CT
CT
Primary Endpoint
PFS/OS
NonMatch
Drug
Biomarker D
TT D+E
E
Primary Endpoint
PFS/OS
TT = Targeted therapy; CT = chemotherapy (docetaxel or gemcitabine); E = erlotinib.
Project Chair: V. Papadimitrakopoulou
Steering Committee Chair: R. Herbst
SWOG Lung Chair: D. Gandara
42
LUNG-MAP (S1400): Squamous Lung Cancer —
2nd-Line Therapy
Common Broad Platform
CLIA Biomarker Profiling◊
CDK4/6
M: CCND1, CCND2,
CCND3, cdk4 ampl
PI3K
M:PIK3CA mut
GDC-0032
CT
Endpoint
PFS/OS
PD-0332991
CT
Endpoint
PFS/OS
FGFR
M: FGFR ampl,
mut, fusion
AZD4547
CT
Non-match
CT
Endpoint
PFS/OS
Anti-PD-L1:
MEDI4736
HGF
M:c-Met Expr
AMG102+E
E
Endpoint
PFS/OS
CT = chemotherapy (docetaxel or gemcitabine); E = erlotinib.
◊ Archival FFPE tumour, fresh CNB if needed.
Project Chair: V. Papadimitrakopoulou
Steering Committee Chair: R. Herbst
SWOG Lung Chair: D. Gandara
43
LUNG-MAP (S1400): Squamous Lung Cancer—
2nd-Line Therapy
Assign Treatment
Arm by Marker
Patient
Registration
Consent
Investigational
Targeted Therapy
Randomisation
Tumour
Collection
Genomic Screening
(FM one)
<2 weeks
New Tumour
Biopsy
(if needed)
Treatment
Interim Endpoint: PFS
NGS/IHC
(Foundation
Medicine)
Primary Endpoint: OS
Standard-of-Care
Therapy
• Organisers: NCI-TMSC, FDA, FNIH, FOCR
• Participants: Entire North American Lung Intergroup
(SWOG, Alliance, ECOG-Acrin, NRG, NCI-Canada)
• Screening: up to 1,000 patients/year
• With 4-6 arms open simultaneously, anticipate a hit rate ≈65% in
matching a patient with a drug/biomarker arm
44
Squamous Lung Master Protocol Clinical Trial Assay Based on Foundation
Medicine NGS Platform
Foundation Medicine NGS test platform (CLIA/CAP)
1) DNA extraction
Classification rules
2) Library construction:
3) Analysis pipeline
selected cancer genes
Illumina HiSeq 2500
4) Master protocol CTA
• Based on FM T5 NGS
platform
• Implemented as “mask” of
T5 content and classification
rules on called alterations
• Rules determine biomarker
positive/negative status
Classification rules (preliminary)
Non-NGS biomarkers:
Supplementary
assays
Non-match arm
MET IHC (+)
All assays (-)
MET pathway
inhibitor
Anti-PD-L1 Ab
PIK3CA mutation
CCND1 amplification or
CDKN2A/B deletion, and
RB1 wild-type
FGFR1/2/3/4
amplification,
mutation or fusion
PI3K inhibitor
CDK4/6 inhibitor
FGFR inhibitor
45
Biomarker Trial Design Based on
Comprehensive Genomic Profiling
60%
50%
Screen success rate: up to ≈80% of patients, depending on
biomarkers selected
PIK3R1/2,
TSC1/2, AKT1/2
5%
CCND3 amp
CCNE1 amp
CDK6 amp
STK11 loss
46%
4%
CCND2 amp
40%
% of
lung
squamous 30%
patients
with alteration
FBXW7 loss
CCND1 amp
PTEN loss
26%
26%
PIK3CA
amp
CDKN2A/B loss
20%
10%
25%
5%
PIK3CA
mutation
47%
4%
FGFR3 amp
FGFR1 amp/
mutation
0%
PI3K/AKT/mTOR
lead candidate biomarkers
Cell Cycle
FGFR
additional potential biomarkers
18%
46
What Are the Statistical Assumptions?
Lung-MAP Sub-studies
Phase 2
Phase 3
Approximate
Time of
Sample
Analysis
Size
Approximate
Time of
Analysis
Prevalence
Estimate
Approximate
Sample Size
56.0%
170
GNE+
5.6%
78
FMI+
8.0%
152
19
400
72
S1400C
11.7%
124
11
312
45
S1400D
9.0%
112
11
302
53
S1400E
16.0%
144
9
326
37
Sub-study ID
S1400A
8
380
21
S1400B
288
47
Is Lung-MAP Self-sustaining?
Activation of Lung-MAP Within 1st Month (July 2014)
48
LUNG-MAP (S1400): Squamous Lung Cancer —
2nd-Line Therapy
Common Broad Platform
CLIA Biomarker Profiling◊
PI3K
M:PIK3CA mut
GDC-0032
CT
Endpoint
PFS/OS
CDK4/6
M: CCND1, CCND2,
CCND3, cdk4 ampl
PD-0332991
CT
Endpoint
PFS/OS
CT
Non-match
FGFR
M: FGFR ampl,
mut, fusion
AZD4547
CT
Endpoint
PFS/OS
Anti-PD-L1:
MEDI4736
HGF
M:c-Met Expr
AMG102+E
E
Endpoint
PFS/OS
CT = chemotherapy (docetaxel or gemcitabine); E = erlotinib.
◊ Archival FFPE tumour, fresh CNB if needed.
Project Chair: V. Papadimitrakopoulou
Steering Committee Chair: R. Herbst
SWOG Lung Chair: D. Gandara
49
50
Is Lung-MAP “Self-sustaining”?
Lung MAP is designed to be adaptable with changes in the therapeutic
landscape
• Example: recent approval of nivolumab in 2nd-line therapy of squamous lung
cancer
– Lung MAP modified to be 2nd-line and beyond (ie, some substudies are now 2nd3rd line, others 2nd-line)
– One planned immunotherapy combination substudy is 2nd- line with nivolumab
control arm
– Another planned substudy is 3rd-line after nivolumab PD
51
Is Lung-MAP “Self-sustaining”?
• New Targets-New Opportunities
– PARP
– mTORC1/mTORC2
(RICTOR)
– PI3K/PTEN
– Wee-1 kinase
– ATR
– VEGFR2
– TRK
– Drug combinations
(Immunotherapies)
Hammerman et al. Nature. 2012.
52
SUPPORTING COOPERATIVE GROUPS:
ALLIANCE
Everett Vokes, M.D.
University of Chicago Medical Center
NCIC-CTG
Glenwood Goss, M.D.
University of Ottawa
ECOG/ACRIN
Suresh Ramalingam, M.D.
Emory University
NRG
Jeff Bradley, M.D.
Washington University School of
SUPPORTING COOPERATIVE GROUPS:
ALLIANCE
Everett Vokes, M.D.
University of Chicago Medical Center
NCIC-CTG
Glenwood Goss, M.D.
University of Ottawa
ECOG/ACRIN
Suresh Ramalingam, M.D.
Emory University
NRG
Jeff Bradley, M.D.
Washington University School of Medicine
53
Discussion: The role of translational research in NSCLC
Facilitated by Dr. David Gandara
55
The Role and Responsibilities of a Principal
Investigator (PI)
Nick Pavlakis, MBBS, MMed (Clin Epi), PhD
57
The Principal Investigator (PI) of a Study: Semantics
• Chief or Lead Investigator (CI) – overall study lead
• Principal Investigator (PI) – sometimes used to refer to overall study lead, but
here refers to PI at an individual site, as
“The person responsible” for the conduct of the clinical trial at a trial site
58
Who Is a Principal Investigator (PI)*?
• “The person responsible” for the conduct of the clinical trial at a trial site. If a
trial is conducted by a team of individuals at a trial site, the investigator is the
responsible leader of the team and is usually called the principal investigator
– If a trial does not have a sponsor, the PI is the Sponsor-Investigator, as in IITs
• Any individual member of the clinical trial team designated and supervised by
the investigator at a trial site to perform critical trial-related procedures and/or
to make important trial-related decisions (eg, associates, residents, fellows are,
under the regulations, considered sub-investigators, not PIs or coinvestigators)
*Source: FDA’s Official Guidance on Good Clinical Practice. Available at:
http://www.fda.gov/downloads/Drugs/Guidances/ucm073122.pdf. Accessed June10, 2015.
Please note that the respective guidance or provisions may vary for other countries or regions.
59
Qualifications and Agreements for PIs
• Qualified by education, training, and experience to assume proper conduct of the trial
• Aware of and complies with GCP*
• Familiar with the use of investigational product(s)
• Interested in the scientific aspects of the trial
• Good clinical practice (GCP)* is an international ethical and scientific quality standard for
designing, conducting, recording, and reporting trials that involve the participation of human
subjects
• GCP compliance provides public assurance that the rights, safety, and well-being of trial subjects
are protected, consistent with the principles that have their origin in the Declaration of Helsinki,
and that the clinical trial data are credible
*FDA’s Official Guidance on Good Clinical Practice.
60
Good Clinical Practice (GCP) and ICH
• International quality standard that is provided by ICH, an international body
that defines standards, which governments can transpose into regulations for
clinical trials involving human subjects
• ICH refers to the International Conference on Harmonisation of Technical
Requirements for Registration of Pharmaceuticals for Human Use, a
project that brings together the regulatory authorities of Europe, Japan, and the
US, and experts from the pharmaceutical industry in the three regions to
discuss scientific and technical aspects of pharmaceutical product registration
– ICH guidelines adopted as law in some countries, used as a guide by US FDA
61
Declaration of Helsinki
• Declaration of Helsinki is a set of ethical principles regarding human experimentation developed
for the medical community by the World Medical Association (WMA)
• The cornerstone modern document on human research ethics
• Origin: June 1964, Helsinki, Finland
– Seven revisions since (most recently at the General Assembly in October 2013) and two clarifications
– Length increased from 11 paragraphs in 1964 to 37 in the 2013 version
• Not used by all countries due to controversy over wording in new versions – refer to GCP and local
guidelines
• History
– The Nuremberg Code was the first set of research ethics principles for human experimentation. It resulted
from the verdict of the Nuremberg Trials (1946 -1949) held at the end of World War II, focusing on doctors
involved in the human experiments in concentration camps
http://www.wma.net/en/30publications/10policies/b3/.
62
Declaration of Helsinki – Basic Principles
• Respect for
–
–
–
–
The individual
Their right to self-determination
Their right to make informed decisions
Regarding participation in research
 Initially and during the course of the research
– Protection of the individual’s health and their rights
• The investigator's duty is solely to the patient, and while there is always a
need for research, the subject's welfare must always take precedence over the
interests of science and society, and ethical considerations must always take
precedence over laws and regulations
http://www.wma.net/en/30publications/10policies/b3/.
63
Qualifications and Agreements for PIs (cont’d)
• Must have adequate time to:
– Discuss, read and approve protocol
– Identify and recruit subjects
– Properly assess and follow subjects
• Must have adequate personnel and resources to conduct the trial
• Must be able to meet the recruitment targets
• Must conduct the trial in compliance with the protocol without deviation
64
General Responsibilities of the PI
• An investigator is responsible for
– Ensuring that an investigation is conducted according to ICH guidelines
– Signing an investigator statement; study protocol, IRB requirements, all applicable
federal, state, and institutional regulations
– Controlling all drugs/agents/devices under investigation
– Protecting the rights, safety, and welfare of subjects under the investigator’s care
65
General Responsibilities of the PI (cont’d)
• Maintaining a list of research team members to whom trial-related duties have
been delegated
• Keeping research team members well informed about the trial at all times
• Permitting monitoring, auditing, and inspection by sponsors and regulatory
authorities
66
Consent of Subjects
• An investigator must obtain the informed consent of each human subject to
whom the drug/agent/device is distributed
– Note: “informed consent” is more than handing the subject a form to sign. It is a
process
– Institutional Standard Operating Procedures (SOP)
Example: Common 2-stage approach
i. Discuss treatment options and trial in first meeting and provide Patient Information and
Consent form (PIC) OR provide PIC prior to meeting in patients referred specifically for
the trial
ii. Meet to go over any questions related to the treatment or the protocol (i.e. complete
“informed” aspect) followed by written consent
67
Investigational Agents and the
Role of the PI
68
Control of Investigational Drug/Agent/Device
• Responsibility for investigational product(s) accountability at the trial site(s)
rests with the investigator/institution
• The PI can delegate investigational product(s) accountability at the trial site(s)
to an appropriate pharmacist or another appropriate individual under their
supervision
– Drug storage, accurate records, inventory
• An investigator shall distribute the drug/agent/device only to subjects under the
investigator’s personal supervision or under the supervision of a subinvestigator responsible to the investigator
• The investigator shall not supply the investigational drug/agent/device to any
person not authorised by the investigator to receive it
69
Investigator Record-keeping and
Record Retention
70
Investigator Record-keeping and Record Retention
• Case Report Form (CRF): a printed or electronic document designed to record
protocol-required information on each subject
• Source Documents: may include hospital records, clinical/office charts, lab
reports, subject diaries, etc
• Investigator should ensure the accuracy, completeness, and timeliness of data
in CRFs
• Data in the CRFs must be consistent with and verifiable with the source
documents
• Correct data in the CRF by striking out and initialing
– Do not use “white out”
– Do not scribble out words
71
Investigator Record-keeping and Record Retention (cont’d)
• Case Histories
– Prepare and maintain adequate and accurate case histories that record all
observations and other data pertinent to the study on each individual distributed to
or employed as a control on the protocol
• CRF, supporting data, signed consent forms, medical records, progress notes,
hospital charts, nurses notes
– Case histories should document that informed consent was obtained prior to the
subject’s participation…this is in addition to the consent form
• “Note to file” to clarify discrepancies or update information
72
Investigator Record-keeping and Record Retention (cont’d)
• Disposition of drug/agent/device
– Maintain adequate records of the disposition of all drugs/agents/devices
– Dates, quantity, subject use, shipping, storage, return/destruction
• Record Retention
– Retain records for a period of 2 years following the date a drug/agent/device is
approved for the indication in which it is being investigated or if no application is
filed/approved, 2 years after the investigation is discontinued
– Note sponsor and local regulatory requirements
 For example, in Australia trial documents must be kept accessible in storage for 15 years
73
Maintenance of a Study Binder for Every Protocol
• List of all study personnel:
– Their initials and signatures
– Their qualifications and responsibilities
– Dates of their participation
• Up-to-date, signed, and dated CVs for staff who:
– Undertake consent and assessments of subjects
– Make entries in case report forms (CRFs)
• Up-to-date licenses of laboratories providing test results and their normal
ranges
74
Investigator Reports
• Progress Reports
– Sending reports to the sponsor as required by the protocol
– Sponsor-investigators are required to submit annual reports to FDA on the progress
of the clinical investigation
• Safety Reports
– Promptly report to the sponsor any adverse effect that may reasonably be regarded
as caused by, or probably caused by, the drug/agent/device
– Sponsor-investigators are required to report adverse effects that are both serious
and unexpected and/or deaths directly to FDA
75
Investigator Reports (cont’d)
• Final Report
– Provide the sponsor/FDA (for sponsor-investigators) with an adequate report shortly
after completion of the investigation
• Financial Disclosure Reports
– Provide sponsor with sufficient accurate and current financial information to allow for
accurate certification/disclosure as required
76
Investigator Reports (cont’d)
• New Information
– New information available during the course of the trial must be passed along to the
IRB/IEC
– If the new information is relevant to the subject’s participation, consent form must be
updated and approved by the IRB/IEC
– For subjects already on study, provide the new information at their next visit or
sooner if there is a risk to the patient or if consent is likely to be revoked
– Current subjects should be “re-consented” with the new IRB-approved consent form
– Delay accrual until IRB/IEC approval of new information
77
Investigator Reports (cont’d)
• Adverse Event/Safety Reporting
– Adverse Event (AE): any untoward medical occurrence in a trial subject, which
does not necessarily have a causal relationship with the study treatment
– Serious Adverse Event (SAE): any untoward medical occurrence that meets one
or more of the following:





Results in death
Is life-threatening
Requires inpatient hospitalisation or prolongation of an existing hospitalisation
Is a congenital anomaly or birth defect
Is a medically significant event, for any reason; these might include pregnancy, cancer,
overdose, etc
78
Investigator Reports (cont’d)
• Adverse Event/Safety Reporting - Questions to Ask
– Is the event “unexpected”?
 Reported in the Investigator’s Brochure
 Known events that have become more frequent or severe
– Is the event treatment-related?
 Reasonable causal relationship to be determined based upon prior experience with
treatment
 If an association cannot be ruled out, then it should be considered to have a reasonable
relationship
 Not-related, unlikely, possible, probable, definite
79
Clinician Conflict of Interest
80
The Clinician-Investigator’s Conflict of Interest
• The clinician-investigator has a dual allegiance
– To study/community
– To patient/subject
• This creates a (legitimate) conflict of interest
• It is important to be aware of this tension
• Other conflicts of interest
– Financial
– Academic
Miller et al. JAMA. 1998;280:1449.
81
The Clinician-Investigator’s Conflict of Interest (cont’d)
• Conflict between your duty to accrue and complete the study – meet
contractual obligation – vs duty to offer best advice to your individual patient
• Duty of care to patient to discuss all options
– “Informed” consent
• Minimising study vs patient conflict
– Independent clinicians
– MDT
– Disease/study team
82
Example of Conflict
• Placebo-controlled trials where evidence has shifted practice such that use of
placebo can be questioned
• Variations in accepted practice for use of standard therapy in control arms
• “Window of opportunity” trials
• How to avoid clinician conflict?
– Informed consent process should include unbiased discussion of all options
– There should be genuine clinical equipoise around the clinical question
– Variations in practice can be accounted for in trial protocol (“clinicians’ choice” reference
therapy, stratification by centres, regions or country)
– Open-label drug access at disease progression in placebo arm or mandatory crossover
83
Case: 1997
• HIV-infected pregnant women enrolled for anti-retroviral therapy to prevent
neonatal infection in Central Africa
• Randomised to AZT vs placebo
• Even though an NIH-funded randomised, controlled trial, it demonstrated in
1994 that AZT (zidovudine) reduced transmission from mother-to-infant by
approximately two-thirds
Mishra R. Placebos break taboo in cancer drug tests. Boston Globe. A1, July 4, 2004.
84
85
ASCO Policy on Conflict of Interest Relating to Trials and Pharmaceutical Companies Implications for Presentation/Publication
http://jco.ascopubs.org/cgi/doi/10.1200/JCO.2013.49.5002.
86
What Makes Clinical Research Ethical?
87
Requirements for Ethical Conduct in a Clinical Trial
JAMA. 2000;283(20):2701-2711. doi:10.1001/jama.283.20.2701.
88
Social or Scientific Value
• Study must ask an important question
– Valuable for improving health and/or for basic scientific knowledge
– Research is unethical if question is trivial, has already been answered, etc
89
Scientific Validity
• Even if the question is important, the study is unethical if the methods are not
likely to answer it
–
–
–
–
–
Not feasible
Poor measures
Inadequate sample size
Poor statistical analysis
Biased reporting (or non-reporting)
90
Fair Subject Selection
• Historically, risky or non-beneficial research has disproportionately enrolled
vulnerable populations
– Ethnic minorities
– Low socio-economic status
– Mentally incapacitated, institutionalised
• Conversely, beneficial research should not selectively enroll privileged
subjects
91
Summary: Your Role as Study PI
• The buck stops with you
– You are in control and responsible
• Know the protocol
• Know, support and ensure you can trust the delegated members of your
research team
– Regular meetings, updates
– Establish structures for optimal trial conduct (eg, trials clinic, fellows, study teams)
• Be diligent in your duties with respect to ICH/GCP and study conduct
• Always be cognisant of the patient – protect their health and rights
* This is not a full summary of your obligations when acting as a PI for a Boehringer Ingelheim trial. Please refer to your Clinical Trial Agreement
and other relevant materials provided by BI.
92
Discussion: The role and responsibilities of a PI
Facilitated by Dr. Nick Pavlakis
94
BIOSTATISTICS IN CLINICAL TRIALS
Shu-Fang Hsu Schmitz
University of Bern, Bern, Switzerland
Biostatistics in clinical trials
Planning
Conduct
Analyses
Reporting
Scenario
A: Standard
R
B: Experimental
CONSORT 2010 Statement: Updated guidelines for reporting parallel-group
randomised trials
Biostatistics-related items
• Sample size
– Primary endpoint(s)
– Null and alternative hypotheses
– Effect size
– Type I and II errors
– Allocation ratio
– Correlated data
– Interim analyses and stopping guidelines
• Randomisation
• Statistical methods
• Outcomes and estimation
Schulz KF, et al. BMC Med 2010;8:18.
Sample size
Primary endpoint(s)
• Number of primary endpoints
Usually one primary endpoint
• Type of endpoint
– Binary endpoint (e.g. Yes/No)
– Categorical endpoint (e.g. none/mild/severe/serious)
– Continuous endpoint (e.g. PSA value)
• Normal distribution (bell shape) or not
– Time-to-event endpoint
(e.g. progression-free survival time)
• Affects choice of statistical approach
PSA = prostate-specific antigen.
Sample size
Null and alternative hypotheses
• Intend to show inequality (2-sided)
Alt.: Effect of Trt B is
different from Trt A
Minimum difference
of clinical relevance
B
A
B
Null: Effect of Trt B is not
different from Trt A
0
• Intend to show superiority (1-sided)
0.5
1
Alt.: Effect of Trt B is better than Trt A
Null: Effect of Trt B is not better than Trt A
• Intend to show non-inferiority (1-sided)
Alt.: Effect of Trt B is not
much worse than Trt A
Null: Effect of Trt B is much
worse than Trt A
0
Alt. = alternative; Trt = treatment.
Non-inferiority margin:
Maximum difference
of clinical irrelevance
A
B
0.5
1
Sample size
Effect size
• A measure to describe between-treatment difference
– Absolute difference: Trt B – Trt A
• Abs. difference = 0  Trt B = Trt A
• Abs. difference > 0  Trt B > Trt A
• Abs. difference < 0  Trt B < Trt A
– Relative difference: Trt B/Trt A
e.g. hazard ratio
• Rel. difference = 1  Trt B = Trt A
• Rel. difference > 1  Trt B > Trt A
• Rel. difference < 1  Trt B < Trt A
• Effect size for sample size
– Minimum difference of clinical relevance
– Maximum difference of clinical irrelevance
Abs. = absolute; Rel. = relative.
Sample size
Type I and II errors
• Type I error
– False-positive rate
– We cannot completely eliminate such random error
– Lower type I error  Larger sample size
– Usually 5%
e.g. if, in fact, Trt B has the same effect as Trt A, we have 5%
chance to wrongly conclude that Trt B is different from Trt A
 The declared difference is not always true
Conclusion
Total
No difference
Difference
Difference
False negative
= Type II error
True positive
= Power
100%
No difference
True negative
False positive
= Type I error
100%
Truth
Sample size
Type I and II errors
• Type II error
– False-negative rate = 1 – power, for a given effect size
– We cannot completely eliminate such random error
– Lower type II error  Higher power  Larger sample size
– Usually 10‒20% type II error (or 80‒90% power)
e.g. if, in fact, Trt B is different from Trt A, we have 20% chance
to wrongly conclude that Trt B is the same as Trt A
 The true difference is not always detected
Conclusion
Total
No difference
Difference
Difference
False negative
= Type II error
True positive
= Power
100%
No difference
True negative
False positive
= Type I error
100%
Truth
Sample size
Allocation ratio
• Equal allocation (1:1)
The total sample size is smallest
 Most efficient
• Unequal allocation
– Less efficient, i.e. larger total sample size
– Might be more attractive for patients
e.g. smaller proportion to the control arm with placebo
• Example sample sizes
nA / nB
nA
nB
nA + nB
0.5
94
187
281
1
124
124
248
1.5
155
103
258
2
186
93
279
Sample size
Correlated data
(Special cases not covered in this CONSORT statement)
• Sources of correlation
– Repeated measurements
i.e. same endpoint assessed at different time points and all data
are included in a single analysis
– Crossover
i.e. each pt receives different treatments at different time periods
– Matched pairs
e.g. different treatments for left and right hands
– Cluster randomisation
e.g. randomise hospitals to different treatments,
all patients of a given hospital receive the same treatment,
outcome is measured at patient level
• Affects choice of statistical approach
pt = patient.
Sample size
Interim analyses and stopping guidelines
Per-arm
sample
size
n1
n2
(If continues)
..........
nK=n
(If continues)
1st interim
analysis
2nd interim
analysis
Final (Kth)
analysis
Sample size
Interim analyses and stopping guidelines
• Number of interim analyses
More interim analyses  Larger final sample size
• Timing of interim analyses
• Allowing early stop for what?
– For benefit only:
Stop if already see a large treatment effect
– For futility only:
Stop if the chance to detect treatment effect is low
– For both:
Stop if either benefit or futility criterion is reached
• Stopping boundary
Easier to stop early  Larger final sample size
Sample size
Interim analyses and stopping guidelines
• Stopping boundary
Example: 3 analyses, overall type I error 0.05
Final boundary
close to 0.05
0.05
0.04
Stopping
boundary
for p-value
0.03
Easy to
stop early
Final boundary
much smaller
than 0.05
0.02
0.01
Difficult to
stop early
0
1
2
Analysis number
3
Final analysis
Sample size
Interim analyses and stopping guidelines
• Stopping boundary
Also other less extreme boundaries
0.05
Final sample size:
168
169
171
0.04
Stopping
boundary
for p-value
176
0.03
193
0.02
0.01
0
1
2
Analysis number
3
Sample size
Interim analyses and stopping guidelines
• Independent data monitoring committee (IDMC)
– Avoid potential data-driven changes resulting in bias
– Members are independent of the trial,
including medical experts of disciplines involved and
statistician(s)
– Based on (un)blinded interim results, give sponsor a
recommendation:
• Stop the trial
• Continue the trial with some modifications
• Continue the trial without modifications
CONSORT 2010 Statement: Updated guidelines for reporting parallel-group
randomised trials
Biostatistics-related items
• Sample size
• Randomisation
– Purpose and requirements
– Types of randomisation
• Statistical methods
• Outcomes and estimation
Schulz KF, et al. BMC Med 2010;8:18.
Randomisation
Purpose and requirements
• Purpose
Ensure that patients assigned to different treatment arms are balanced for both
known and unknown factors,
which could influence their response to treatment
 Reduce bias
• Requirements
– Avoid systematic bias
– Unpredictable
Randomisation
Types of randomisation
• Simple randomisation
– Restriction: No
– Example:
A B B A A A A A B A
– Advantages:
• Simple to implement
• Straightforward for analyses
– Disadvantage: Cannot guarantee allocation ratio
Randomisation
Types of randomisation
• Block randomisation
– Restriction: Yes, by block size
– Example: 1:1 ratio, block size=4
 6 admissible blocks
1=AABB, 2=ABAB, 3=ABBA,
4=BBAA, 5=BABA, 6=BAAB
The randomisation list is
a sequence of admissible blocks in random order
Block number
Treatment allocation
4
3
6
1
BBAA
ABBA
BAAB
AABB
Randomisation
Types of randomisation
• Block randomisation
– Advantage:
Allocation ratio is guaranteed within each full block
– Disadvantage:
Introduce correlation between treatment arms
• Ordinary two-group tests might be biased
• Might require special test or adjusted analysis
– Considerations
• Block size too small  Predictable
• Block size too large  Risk of incomplete block
 Allocation ratio not met
A
A
B
A
B
A
• Use different block sizes within a trial
B
B
A
B
Randomisation
Types of randomisation
• Stratified randomisation
– Restriction: Yes, by stratification factors
– Example: 2 stratification factors, 4 combinations,
 Separate randomisation lists for the 4 combinations
Previous treatment
Stage
Goal:
Similar distributions
between treatment
arms with respect to
each stratification factor
No
Yes
I
Comb 1
Comb 2
II
Comb 3
Comb 4
Factor
Stage
Previous
Trt
Stratum
Arm A
Arm B
I
25%
27%
II
75%
73%
No
33%
30%
Yes
67%
70%
Randomisation
Types of randomisation
• Stratified randomisation
– Advantages
• Take stratification factors into account
• Try to maintain the allocation ratio within each combination
– Disadvantages
• Multiple randomisation lists
• Cannot accommodate too many factors
• Might require adjusted analysis
– Considerations
• Each list can be generated using either simple or block
randomisation
• Only take important prognostic factors into account as
stratification factors
• Keep
sample size
nr combinations
≥5
Randomisation
Types of randomisation
• Minimisation
– Restriction: Yes,
by stratification factors
Trt A
– Example: 3 stratification factors Stage
Already 16 patients
Next (17th) patient
is stage I, not pretreated,
biomarker negative
Goal:
Similar distributions
between treatment arms
with respect to each
stratification factor
Prev. = previous; Neg = negative; Pos = positive.
Trt B
I
3
2
II
5
6
No
3
5
Yes
5
3
Neg
4
4
Pos
3
5
Sum
10
Prev. Trt
Biomarker
<
11
Trt A to the next pt
Randomisation
Types of randomisation
• Minimisation
– Advantages
• Can take more stratification factors into account than
stratified randomisation
• Try to maintain the allocation ratio within each stratum
of each factor
– Disadvantages
•
•
•
•
•
Dynamic allocation, no prepared randomisation lists
Deterministic nature, not really random process
Introduce correlation between treatment arms
Ordinary two-group tests might be biased
Might require special test or adjusted analysis
– Considerations
• Only take important prognostic factors as stratification factors
• Can build in a random element to reduce predictability
CONSORT 2010 Statement: Updated guidelines for reporting parallel-group
randomised trials
Biostatistics-related items
• Sample size
• Randomisation
• Statistical methods
– Primary and secondary outcomes
– Multiplicity
– Subgroup analyses
– Adjusted analyses
– Missing values
• Outcomes and estimation
Schulz KF, et al. BMC Med 2010;8:18.
Statistical methods
Primary and secondary outcomes
• For primary outcome
– Effect size or non-inferiority margin is pre-specified and
used for sample size calculation
– Statistical significance  Clinical relevance
• For secondary outcomes
– Effect size or non-inferiority margin is usually NOT
pre-specified and NOT used for sample size calculation
– Statistical significance  Clinical relevance (retrospectively defined)
Caution in interpretation!
Statistical methods
Multiplicity
Example
Total
number
tests
Indiv.
type I
error
1
5%
95%
5.00%
2
5%
(95%)2 = 95%95% = 90.25%
9.75%
3
5%
(95%)3 = 85.74%
14.26%
4
5%
(95%)4 = 81.45%
18.55%
Prob. all tests reach
correct conclusions
There are statistical approaches
to control overall type I error
Indiv. = individual; Prob. = probability.
Overall
type I
error
Do 4 tests, each at 5% type I error
 Overall, 18.55% chance at least
one test conclusion is wrong!
Statistical methods
Multiplicity
• Problem
– More than one statistical test  Increases overall type I error
– Want to control overall type I error for more than one test
• Sources of multiplicity
– Perform several tests for different endpoints
– For an endpoint, perform several tests at different times,
e.g. interim analyses
– For an endpoint, perform several tests using different
approaches
– For an endpoint, perform several tests in different subgroups
– In a 3-arm trial, perform 3 pairwise tests (A vs B, A vs C, B vs C)
for a given endpoint
Statistical methods
Subgroup analyses
• Predefined subgroup analyses
– Number of analyses is known in advance
– Results of all analyses are reported
– Readers can better judge the credibility of the results,
taking multiple testing into account
• Subgroup analyses not predefined
– Data-driven, fishing for significance!
– Tendency for biased reporting, i.e. only significant results
– Total number of performed analyses is unknown
– Readers cannot judge the credibility of the results
– Purely exploratory
• Sufficient sample size within each subgroup
Statistical methods
Adjusted analyses
• Take factors (e.g. prognostic factors, stratification factors,
etc.) other than treatment group into account in the analysis
• As the primary analysis or sensitivity analysis for an outcome
• Example statistical approaches: Regressions, stratified
analysis, etc.
Statistical methods
Missing values
• Missing at random?
– If ‘missingness’ is related to the outcome measure
 Missing is not at random
e.g. Patients with very bad performance status tend to
have missing values for quality of life questionnaire
 Analysis using all available data is biased
 Other analysis approaches are needed
Statistical methods
Missing values
• Missing at random?
Can you be sure?
– Example
Complete (sorted) data
12
19
32
39
45
55
81
95
97
98
Mean=57.3
97
98
Mean=67.8
95
NA
NA
Mean=47.3
95
97
98
Mean=59.9
Available data set 1 (missing not at random)
NA
NA
32
39
45
55
81
95
Available data set 2 (missing not at random)
12
19
32
39
45
55
81
Available data set 3 (missing at random)
12
NA = not applicable.
19
32
NA
45
NA
81
Statistical methods
Missing values
• Analyses using available data
– Results might be biased due to missing not at random
– Results might be biased because the balance in known and
unknown factors introduced by randomisation might be
destroyed
– Loss of efficiency
• In a univariate analysis
UPN = unique patient number;
CR = complete response;
PR = partial response;
SD = stable disease.
UPN
Outcome
Age
1
CR
90
2
PR
80
3
SD
Missing
4
SD
70
5
CR
80
6
PR
Missing
Statistical methods
Missing values
• Analyses using available data
– Loss of efficiency
• In a multivariate analysis
UPN
Outcome
Age
Stage
Pack year
1
CR
90
3
Missing
2
PR
80
Missing
25
3
SD
Missing
4
40
4
SD
70
1
35
5
CR
80
Missing
30
6
PR
Missing
2
20
CONSORT 2010 Statement: Updated guidelines for reporting parallel-group
randomised trials
Biostatistics-related items
• Sample size
• Randomisation
• Statistical methods
• Outcomes and estimation
– Point and interval estimates
– Confidence interval vs p-value
– Caution for final analysis after interim analyses
Schulz KF, et al. BMC Med 2010;8:18.
Outcomes and estimation
Point and interval estimates
• Point estimate
Mean or median?
Which one is more
representative?
• Interval estimate
– Wider interval
 Larger variation
 Lower precision
5
4
3
2
1
0
Mean: 4
95% CI: 3.1–4.9
Median: 4
IQR: 3–5
1 2 3 4 5 6 7
5
4
– CI: Suitable under (approx) 3
2
normal distribution,
requires standard deviation 1
0
– IQR: 1st quartile –
1 2 3 4 5 6 7
3rd quartile,
25th percentile – 75th percentile
CI = confidence interval; IQR = inter-quartile range.
Mean: 3.1
95% CI: 1.9–4.2
Median: 2
IQR: 1.8–4.3
Outcomes and estimation
CI vs p-value
• CI examples
– Alt. hypothesis: Inequality (2-sided)
– Type I error: 5%
2-sided 95% CI
– Effect size: Absolute difference between treatment arms
– Minimum difference of clinical relevance: 2
6
Absolute
difference
4
1) The CI does not contain 0
 Reject null hypothesis, i.e. statistical significance
 p-value < Type I error 5%
2
2) The width of CI provides hints on precision
0
3) The estimated effect size is likely to be greater
than the minimum difference of clinical relevance
Outcomes and estimation
CI vs p-value
• CI examples
6
4
Absolute
difference
2
0
-2
Clinically relevant:
Yes
?
?
Statistically significant:
Yes
Yes
No
Outcomes and estimation
CI vs p-value
• Information provided by CI and p-value
CI
p-value
Statistical significance
Yes
Yes
Clinical relevance
(if pre-specified)
Yes
No
Precision of estimate
Yes
No
p-value alone is not sufficient for good judgment!
Outcomes and estimation
Caution for final analysis after interim analyses
• A simplified example:
3 successes  in 4 patients considered promising
– Without interim analyses, the chance of a promising result
= 5/16
   
   
   
    
   
   
   
    
   
   
   
    
   
   
    
    
Outcomes and estimation
Caution for final analysis after interim analyses
• A simplified example:
3 successes  in 4 patients considered promising
– With an interim analysis after 2 patients
Stopping rule: If 2 failures , then stop
The chance of promising results after 4 patients
= 5/12 > 5/16
   
   
   
    
   
   
   
    
   
   
   
    
   
   
    
    
Final p-value and CI need to be adjusted
Outcomes and estimation
Caution for final analysis after interim analyses
• The threshold for p-value to declare statistical significance
might be lower than the type I error
e.g. a p-value of 0.05 might not be sufficient for significance
0.0496
0.0483
0.0451
0.05
0.04
Stopping
boundary
for p-value
0.0373
0.03
0.0221
0.02
0.01
0
1
2
Analysis number
3
Summary
Biostatistics in Clinical Trials
• Well designed and properly executed randomised controlled trials
(RCTs) provide the most reliable evidence on the efficacy of
healthcare interventions
• Different statistical methods apply when the endpoint is discrete
(frequency per category), continuous (measurements), or time-toevent (survival analysis)
• Statistical analysis requires careful consideration of the study
objectives and the nature of the endpoints
• Complicating factors include multiplicity, subgroup analysis and
missing data
• Considerations for how to present trial outcomes and estimations
are point and interval estimates, the use of confidence interval vs.
p-values and caution for final analysis after interim analyses.
Discussion: Biostatistics in clinical trials
Facilitated by Prof. Shu-Fang Hsu Schmitz
141
COFFEE BREAK
Breakout Session 1
Breakout Format
• Please go to the room assigned to your group on the next slide for
Breakout Session 1
– Group A: Phase I and Phase II trial design in oncology – Dr. Shu-Fang
Hsu Schmitz
– Group B: good clinical practice compliance – Dr. Clifford Hall
• Once the first breakout is complete, we will have a break for lunch
• Following lunch, you will return to your same breakout room, but the
topic and faculty will be switched for Breakout Session 2
– Group A: good clinical practice compliance – Dr. Clifford Hall
– Group B: Phase I and Phase II trial design in oncology – Dr. Shu-Fang
Hsu Schmitz
• Following Breakout Session 2, please return to this room
Day 2 Breakout Rooms: Eixample and Grácia
145
Group A: Dr. Shu-Fang Hsu Schmitz,
Eixample
Group B: Dr. Clifford Hall,
Grácia
Daniel Gagiannis
Emanuela Salati
Arik Schulze
Cristina Daniela Micu
Marie-Claire Desax
Aija Geriņa-Bērziņa
Michael Schumacher
Erika Korobeinikova
Xu Chong-Rui
Pedro De Marchi
Zhabina Albina
Rafael Caparica
Nadezhda Hamrina
Joan Coves
Ana Gelatti
Esther Holgado
Patricia Cruz
Suneil Khanna
Virginia Calvo
Joaquín Mosquera
Martinez
Natalia Fernandez
Krista Noonan
Manuel Magalhães
Barbara Melosky, David Gandara,
Nick Pavlakis, Ralf Schnall, Angela
Märten, Verena Zahn
Thierry Le Chevalier, Rosario
Garcia-Campelo, Uday Bose,
Georgi Adly, Tara Regan
LUNCH
Breakout Session 2
Day 2 Breakout Rooms: Eixample and Grácia
149
Group A: Dr. Clifford Hall, Eixample
Group B: Dr. Shu-Fang Hsu Schmitz,
Grácia
Daniel Gagiannis
Krista Noonan
Arik Schulze
Cristina Daniela Micu
Marie-Claire Desax
Emanuela Salati
Michael Schumacher
Erika Korobeinikova
Xu Chong-Rui
Aija Geriņa-Bērziņa
Zhabina Albina
Rafael Caparica
Nadezhda Hamrina
Pedro De Marchi
Ana Gelatti
Esther Holgado
Patricia Cruz
Joan Coves
Virginia Calvo
Joaquín Mosquera
Martinez
Natalia Fernandez
Suneil Khanna
Manuel Magalhães
Barbara Melosky, David Gandara,
Nick Pavlakis, Ralf Schnall, Angela
Märten, Verena Zahn
Thierry Le Chevalier, Rosario
Garcia-Campelo, Uday Bose,
Georgi Adly, Tara Regan
Evaluating Well-designed vs Poorlydesigned Randomized Trials
David R. Gandara, MD
University of California, Davis
Comprehensive Cancer Center
151
Evaluating Good vs Poorly Designed
Randomized Clinical Trials
The Good, The Bad and the Ugly
Randomized Clinical Trials:
The Basics
Who, What, Where, Why, When and more
•
•
•
•
•
•
Why do you want to do the study?
Who do you want to study?
How are you going to study them?
What is the study design & primary study endpoint?
Where are you going to conduct the study?
When do you want to look at interim results, if at
all?
Randomized Clinical Trials:
The Basics (cont’d)
• Why do you want to do the study?
– What is the hypothesis?
– Will the results change SOC or lead to definitive trials?
• Who do you want to study?
– What patient population?
– “All comer” or Selected/Enriched?
– What stratifications (for prognostic groups)?
• How are you going to study them?
– Comparison of different treatments? (or against BSC)
• QOL or Comparative Effectiveness?
Randomized Clinical Trials:
The Basics (cont’d)
• What is the study design & primary study endpoint?
– Randomized Phase II, Phase II/III or Phase III?
• How big a patient sample size needed to address the hypothesis?
– If Phase II, new treatment vs SOC or “pick the winner”
– Primary Endpoint: Response, PFS, OS or Other (QOL))
• Where are you going to conduct the study?
– Single institution, multi-site in your country or Global
• If Global: Will there be issues of population-related
pharmacogenomics?
• When do you want to look at interim results, if at all?
– Planned interim analysis?
– Is the study a Phase II/III with “go-no go” decision?
Example: QUARTZ Trial of Whole
Brain Radiotherapy vs Optimal
Supportive Care for NSCLC patients
with brain metastases (ASCO 2015)
Good, Bad or UGLY?
Whole brain radiotherapy for brain metastases from
non-small cell lung cancer:
Quality of life and overall survival results from the
UK MRC QUARTZ trial
PM Mulvenna, MG Nankivell, R Barton, C Faivre-Finn, P Wilson, B Moore,
E McColl, I Brisbane, D Ardron, B Sydes, C Pugh, T Holt, N Bayman,
S Morgan, C Lee, K Waite, RJ Stephens, MKB Parmar, RE Langley
Brain Metastases and NSCLC
•
After radical treatment of primary Non Small Cell Lung Cancer
(NSCLC), the brain remains a frequent and early site of distant relapse,
affecting up to 40% of patients
•
Patients with NSCLC and brain metastases fare poorly even if
irradiated
•
Median survival remains poor – RTOG RPA prognostic classes
– RPA I
7.1 months
– RPA II
4.2 months
all patients received WBRT; 57% NSCLC
– RPA III 2.3 months
•
In the face of modest prognosis, how do we ensure optimal quality of
life?
•
In spite of lack of randomised, controlled data, whole brain
radiotherapy (WBRT) plus steroids standard care
QUARTZ Trial
Randomised Controlled Non-Inferiority Design
March 2007- August 2014
Histologically proven
NSCLC with brain
metastases –
non-resectable
and unsuitable for
stereotactic
radiosurgery
Control Arm:
Optimal Supportive Care
Dexamethasone
+
Whole Brain Radiotherapy
20Gy in 5 daily #
Primary outcome
quality adjusted
life years (QALYS)
R
Secondary outcomes
overall survival
symptom scores
Investigational Arm:
Optimal Supportive Care
Dexamethasone
Main Inclusion Criteria
Pragmatism, Inclusivity
•
Histologically proven primary Non Small Cell Lung Cancer
•
CT/MRI confirming brain metastases – considered inoperable or ineligible for SRS by
lung/neuro-oncology Multi-Disciplinary Teams (Tumour Boards)
•
Previous systemic treatment allowed, at least 4 weeks prior to randomisation
•
Subsequent/simultaneous (extra cranial) palliative RT permitted
•
Subsequent systemic treatment permitted at clinician’s discretion
•
Adapted to changing landscape
Statistical Design
•
•
•
•
•
Non-inferiority design
Aiming to exclude >1 week reduction in QALYs with omission
of WBRT
80% power
Sample size re-assessed in 2009 following poor recruitment
Recalculated independently of results from interim analyses
Patients
WBRT QALY
HR
Onesided α
Original (2006)
1036
6 weeks
1.2
2.5%
Revised (2009)
534
5 weeks
1.25
5%
Challenges
“Treatment vs No Treatment”
Patient / Clinician Preferences
Interim Data Release Oct 2010
538 Patients: Baseline characteristics
69 UK and 3 Australian centres
OSC + WBRT
(N=269)
OSC Alone
(N=269)
66 (38 – 84)
67 (45 – 85)
Age
Median (range)
Sex
Male
58%
58%
Karnofsky
Performance Status
≥70
62%
62%
<70
38%
38%
Adenocarcinoma
55%
51%
Squamous
20%
25%
Large cell
3%
2%
NSCLC NOS
23%
22%
Yes
30%
30%
Histology
Solitary brain
metastasis
RTOG Prognostic classes (RPA)
RPA Class
RTOG QUART
(N=1176
Z
)
(N=400)
RPA KPS >70
I
Controlled
Primary Site
Age <65 years
No extra
cranial
metastases
20%
5%
RPA Neither RPA I
II
nor RPA III
65%
61%
RPA KPS < 70
III
15%
34%
Gaspar et al IJROBP 1997; 37:745-51
Baseline symptoms
Symptoms shown are those affecting at least 15% of patients
Tiredness
Insomnia
Weakness
Drowsiness
Mood
Sight
Any moderate or severe
symptom
OSC +
WBRT
(N=269)
%
OSC
Alone
(N=269)
%
40%
44%
28%
35%
25%
30%
24%
27%
21%
17%
19%
17%
72%
78%
Steroid use
At randomisation - all patients were receiving steroids (dexamethasone)
Median dose 8mg/day
OSC +
WBRT
(N=269)
%
OSC
Alone
(N=269)
%
No
9%
5%
Yes
91%
95%
No
15%
10%
Yes
85%
90%
Requiring steroids
during…
First 4 weeks
First 8 weeks
Whole Brain Radiotherapy (WBRT) administration
OSC + WBRT
(N=269)
%
Dose received
Time to starting
WBRT
0 Gy
12%
<20 Gy
6%
20 GY
82%
≤7 days
39%
8 – 14 days
40%
>14 days
21%
Symptoms at 4 weeks
Worsened
Improved
OSC + WBRT
OSC alone
OSC + WBRT
OSC alone
Tiredness
33%
30%
13%
19%
Drowsiness
29%
22%
9%
14%
Insomnia
17%
14%
19%
22%
Mood
11%
15%
15%
8%
Weakness
26%
21%
10%
13%
Hair Loss
33%
1%
4%
2%
Overall Survival
Median survival (weeks)
Overall survival (all patients)
1.00
0.75
522 deaths
(260 OSC+WBRT vs 262
OSC).
0.50
OSC+WB
RT
9.3 weeks
(7.4, 10.7)
0.25
OSC
alone
8.1 weeks
(7.6, 9.0)
HR
1.05 (0.89,
1.26)
Proportion surviving
--------- OSC+WBRT ------- OSC Alone
0.00
0
8
16
24
32
40
48
56
9
8
5
5
Time from randomisation (weeks)
At risk
OSC + WBRT 269
OSC alone 269
144
141
66
64
32
32
17
16
11
11
P-value
0.52
Better
Overall survival (all patients)
.8
Components of the Primary Outcome Measure
OSC+WBRT
OSC Alone
1.00
0.75
0.50
.4
.6
--------- OSC+WBRT ------- OSC Alone
Average QoL
.2
0.25
EuroQoL EQ-5D
0.00
0
8
16
24
32
40
48
56
Time from randomisation (weeks)
0
At risk
OSC + WBRT 269
OSC alone 269
Worse
0
Proportion surviving
--------- OSC+WBRT ------- OSC Alone
144
141
66
64
32
32
17
16
Overall Survival
11
11
9
8
5
5
8
16
24
32
40
Time from randomisation (weeks)
Quality of Life
48
56
.8
Primary Outcome Measure: Quality Adjusted Life
Years
OSC+WBRT
.4
.6
--------- OSC+WBRT ------- OSC Alone
OSC+WBRT
43.3 days
OSC alone
41.4 days
Difference
-1.9 days
90% CI (Bootstrap)
(-9.1, 6.6)
Non-inferiority
boundary
.2
-7 days
-
0
Average QALY
Mean QALY (days)
OSC Alone
0
8
16
24
32
40
Time from randomisation (weeks)
QUALY
48
56
9.1 -7
OSC+WBRT
better
-2 0
DAYS
6.6
OSC alone
better
Conclusions – QUARTZ Trial
Only large randomized trial of WBRT vs no WBRT for brain
metastases from NSCLC
Detailed QoL data can be collected in poor prognostic group
WBRT does not appear to be a steroid-sparing modality
Similar overall survival (9.3 weeks vs 8.1 weeks)
Similar QALYs (43.3 days vs 41.4 days)
The estimate of the difference in QALYs suggests WBRT provides
no additional clinically significant benefit for this group of patients
Randomized Clinical Trials:
The Basics
• Why did they want to do this study?
– What is the hypothesis?
– Will the results change SOC or lead to definitive trials?
• Who did they want to study?
–
–
–
–
What patient population?
“All comer” or Selected/Enriched?
Eligibility criteria?
What stratifications (for prognostic groups)?
• How did they study them?
– Comparison of different treatments? (or against BSC)
• QOL or Comparative Effectiveness?
Randomized Clinical Trials:
The Basics (cont’d)
• What was the study design & primary study endpoint?
– Randomized Phase II, Phase II/III or Phase III?
• How big a patient sample size needed to address the hypothesis?
– If Phase II, new treatment vs SOC or “pick the winner”
– Primary Endpoint: Response, PFS, OS or Other (QOL))
• Where was the study conducted?
– Single institution, multi-site in your country or Global
• If Global: Will there be issues of population-related
pharmacogenomics?
• When were interim results looked at, if at all?
– Planned interim analysis?
– Is the study a Phase II/III with “go-no go” decision?
Discussion of Abstract #8005:
“Challenging Convention”
• Whole brain radiotherapy for brain metastases
from NSCLC: Quality of life (QoL) & overall survival (OS)
-UK MRC QUARTZ randomised clinical trial
Convention: WBRT is a SOC for brain metastases in NSCLC
Discussion points:
1. Do the data support the conclusions?
2. Do the results change standard
of care or alter current practice?
How do we explain the results of the
QUARTZ trial?
• Who were these QUARTZ patients?
• QUARTZ raises as many questions as it answers
– Patients were deemed “inoperable” or “not suitable for SRS” (yet
30% had a single brain met)
– Median OS was dismal in both arms: ~ 2 months
• Did the study population include many “pre-terminal” cases?
• KPS <70% in ~38%.
– What was the minimum KPS for eligibility? (No minimum KPS for eligibility)
• RPA class III in 34%. (A very poor prognosis group)
– Is this a group appropriate for this QOL study?
– Were they appropriate candidates for WBRT?
QUARTZ Protocol Eligibility:
RTOG Prognostic Classes (RPA) in QUARTZ Study
Gaspar et al: IJROBP 1997
Comparison of Prognostic Indices for Brain Metastases
Sperduto et al: IJROBP 2008
How do we explain the results of this trial?
• Was the QOL tool utilized appropriate for the study
hypothesis & for study design & eligibility criteria?
– EQ5D utilized (Would FACT-BR have provided a better measure?)
• Are there better options for therapy of brain metastasis
in 2015 than that utilized in QUARTZ? (initiated in 2007)
– WBRT with hippocampal sparing
– SRS is an ever expanding option
• Was the QOL tool utilized appropriate for the
study hypothesis & for study design &
eligibility criteria?
Symptoms reported in the presentation
EQ5D is a “Generic Health-related QOL Measure”
Has been employed to
study
Rheumatoid Arthritis,
Parkinson’s Disease
Multiple Sclerosis
CVA
Chronic Hepatitis
Attention Deficit Disorder
Not specific to cancer or
to Brain Metastases
Conclusions – QUARTZ Trial
•
Only large randomized trial of WBRT vs no WBRT for brain
metastases from NSCLC
•
Similar overall survival and QALYs (43.3 vs 41.4 days)
•
Although the results include the pre-specified non-inferiority margin (-9.1
days vs -7 days), the estimate of the difference in QALYs suggests WBRT
provides no additional clinically significant benefit for this group of patients
My Conclusions:
1. Agree, in this group of patients. But uninterpretable for
original intent.
2. Eligibility criteria (~low KPS, high RPA) & selection process
(deemed inappropriate for surgery or SBRT) invalidated
hypothesis testing for QOL endpoint, & for OS.
3. Non-inferiority was not proven.
4. Due to the nature of the patient population, this study does not
alter SOC or current practice.
Discussion
Facilitated by Dr. David Gandara
183
Group Photo!
184
COFFEE BREAK
DELEGATE PROJECT COACHING
(90 mins)
Day 2 Coaching Groups
Group A: Main Room
Group B: Eixample
Group C: Grácia
Barbara Melosky
Thierry Le Chevalier
David Gandara
Clifford Hall
Shu-Fang Hsu Schmitz
Rosario García Campelo
Nick Pavlakis
Arik Schulze
Cristina Daniela Micu
Daniel Gagiannis
Marie-Claire Desax
Xu Chong-Rui
Michael Schumacher
Aija Geriņa-Bērziņa
Emanuela Salati
Erika Korobeinikova
Rafael Caparica
Nadezda Hamrina
Zhamina Albina
Esther Holgado
Pedro De Marchi
Ana Gelatti
Joan Coves
Patricia Cruz
Virginia Calvo
Manuel Magalhães
Natalia Fernandez
Joaquin Mosquera Martinez
Krista Noonan
Georgi Adly
Suneil Khanna
Angela Märten
Uday Bose
Ralf Schnall
Verena Zahn
Tara Regan
187
PLEASE RETURN TO THE GENERAL SESSION
ROOM AT 16.10
Conclusions: Day 2
Barbara Melosky, MD
FOR CONSIDERATION TONIGHT!
Your Feedback on the Programme Will Help Us Plan Future TOP Meetings
• Please consider the following 4 questions – tomorrow we
will ask you to put your responses on the pin boards
– What was the best part of this meeting?
– What is your most important takeaway?
– What would you change if you were in charge of the agenda?
– What would you like to learn at TOP 2 in Vienna?
Reminders
• Please complete your meeting Day 2 evaluation and network
survey and turn them in as you leave the room
– Your feedback is valuable!
• We will meet in the hotel restaurant, Dia i Nit, at 19.00 for dinner
– Same place as breakfast, 4th floor
– Please let Verena know if you are not joining
• Check the departure schedule on the back of the door
– Please let Verena know if you have any changes
• The meeting starts tomorrow at 8.00
– Please have breakfast in the hotel restaurant on the 4th floor before the meeting
starts