Adaptive biomarker-driven designs in Phase III clinical trials Alex Dmitrienko's slides.pdfApr 14,...

Adaptive biomarker-driven designs in Phase III clinical trials

Alex Dmitrienko Vice President Center for Statistics in Drug Development Quintiles Innovation

KU Medical Center Department of Biostatistics April 18, 2014

Outline

– Personalized medicine approach

• Development of tailored therapies

– Clinical trial designs

• Subpopulation-only designs versus multi-population designs

– Case study

• Oncology Phase III trial

– Adaptive biomarker-driven clinical trial designs

• Development and optimization of complex data-driven decision

rules

2

Personalized medicine approach

– Tailored therapies

• Novel therapies that target subgroups of patients with certain

characteristics

• Patient characteristics include demographic variables, clinical

variables, gene or protein expression markers, etc

– Regulatory guidance documents

• U.S. (Food and Drug Administration): Enrichment strategies for

clinical trials to support approval of human drugs and biological

products (December 2012)

• Europe (Committee for Medicinal Products for Human Use:

Guideline on the investigation of subgroups in confirmatory clinical

trials (January 2014)

3

Clinical trial designs

– Patient populations

• Overall population (OP)

• Marker-positive subgroup (M+)

• Marker-positive subgroup (M-)

– Therapeutic benefit

• Greater therapeutic benefit is expected in the marker-positive

subgroup

4

Clinical trial designs

– Subpopulation-only designs

• Enrollment is restricted to M+ (only marker-positive patients are

enrolled) and M- is not studied (Freidlin, McShane and Korn, 2010)

• Also known as biomarker-enriched designs

– Multi-population designs

• All patients are enrolled and treatment effect is examined in OP as

well as M+ (Millen et al., 2012)

• Also known as biomarker-stratified designs

5

Subpopulation-only designs

– Breast cancer example

• Herceptin (trastuzumab) for treatment of breast cancer (Romond et

al., 2005)

• Important marker: Amplified HER2 gene

• Efficacy in marker-positive patients (with HER2-positive tumors)

was established and marker-negative patients were not enrolled

• Marker-negative patients were denied access to potentially

beneficial treatments but Herceptin is likely to have a positive effect

in classifier-negative patients (Paik et al., 2008)

– Regulatory position

• Desirable to evaluate the efficacy and safety of new treatments in

both marker-positive and marker-negative patients (FDA

enrichment guidance)

6

Case study

– Phase III clinical trial

• Patients with a rare type of cancer

– Treatment comparison

• Experimental therapy plus chemotherapy versus placebo plus

chemotherapy

– Primary endpoint

• Overall survival (OS)

– Potential predictive biomarker

• Biomarker (protein expression marker) discovered in Phase II trial

• Believed to be predictive of treatment response

7

Adaptive biomarker-driven trial design

– Two-stage design

• Interim analysis to review the safety and efficacy data

– Stage I (before the interim analysis)

• Multi-population trial design (both marker-negative and marker-

positive patients are enrolled)

– Stage II (after the interim analysis)

• Trial design may be adaptively modified at the interim analysis to

improve the overall probability of success

8

Two-stage design

9

Futility stopping rules, population selection rules and sample size

adjustment rules will be applied at the interim analysis

Stage I Stage II

Mark

er

po

sitiv

e

Mark

er

ne

ga

tive

???

Interim analysis

Decision rules at the interim analysis

– Futility stopping

• Evaluate futility in the overall population and marker-positive

subgroup

– Population selection

• Select the overall population or marker-positive subgroup for

Stage II

– Sample size adjustment

• Increase the sample size in Stage II if necessary

10

Decision rules at the interim analysis

11

Summary of all decision rules (↑ increase the sample size)

↑ M+

Stop

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h

Probability of success in OP

Low High

M+

Mediu

m

↑ OP

↑ OP M+

OP

Medium

Futility stopping rules

– Futility stopping rules

• Futility stopping rules will be implemented based on predicted

probability of success at the interim analysis

– Commonly used approaches

• Frequentist (conditional power)

• Bayesian (predictive power)

• Bayesian (predictive probability)

12

Conditional power

13

Conditional power function CPn(d)=P(ZNza|Zn,d)

• Likelihood of a statistically significant result given the interim data

• Zn, Test statistic at the interim analysis

• ZN, Test statistic at the planned end

• d, Assumed treatment difference

Observed data Future data are generated

from assumed

treatment difference

Interim look Start End

Sta

tistica

lly

sig

nific

ant

diffe

rence

Predictive power

14

Predictive power function PPn=CPn(d)f(d|Zn)dd

• Likelihood of a statistically significant result given the interim data

averaged over the posterior distribution of d

• f(d|Zn), Posterior density of treatment difference d given the interim

data

Observed data Future data are

generated from

posterior distribution

Interim look Start End

Prior

d

istr

ibu

tion

Poste

rior

dis

trib

ution

Sta

tistically

sig

nific

ant

diffe

rence

Predicted probability of success

– Outcome 1: Low probability of success

• Discontinue enrollment due to futility if the predicted probability of

success at the planned study end is low (e.g., less than 30%)

– Outcome 2: Medium probability of success

• Apply the population selection and sample size adjustment rules if

the predicted probability of success at the planned study end is

medium (e.g., 30-70%)

– Outcome 3: High probability of success

• Apply the population selection and sample size adjustment rules if

the predicted probability of success at the planned study end is

high (e.g., greater than 70%)

15

Population selection rules

16

Since greater benefit is expected in M+, predicted probability of success

in OP is lower than in M+

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h


Low High

Mediu

m

Medium

NA

NA NA


17

No group is selected for Stage II (futility rule is met in both OP and M+)

because the success probability in M+ is low

Stop

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h


Low High

Mediu

m

Medium

Subgroup selection rules

18

OP is selected for Stage II because the success probability in OP is

comparable to that in M+ (non-informative marker)

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h


Low High

Mediu

m

OP

OP

Medium


19

M+ is selected for Stage II because the success probability in M+ is

medium or high and no effect in M-

M+

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h


Low High

M+

Mediu

m

Medium


20

M+ and OP are selected for Stage II because the success probability is

high in M+ and medium in OP

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h


Low High

Mediu

m

OP M+

Medium


21

Summary of population selection rules

M+

Stop

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h


Low High

M+

Mediu

m

OP

OP M+

OP

Medium

Potential regulatory claims

– Broad claim

• Treatment effectiveness in the overall population only (OP only)

– Restricted claim

• Treatment effectiveness in the marker-positive subgroup only (M+

only)

– Enhanced claim

• Treatment effectiveness in the overall population as well as marker-

positive subgroup (OP and M+)

22

Sample size adjustment rules

– Promising zone approach

• Adjust the sample size in the overall population or marker-positive

subgroup in Stage II based on the predicted probability of success

– Outcome 1: Original sample size

• in M+ or OP if the predicted probability of success is high (e.g.,

greater than 70%)

– Outcome 2: Increase the sample size

• Increase the sample size in M+ or OP in Stage II if the predicted

probability of success is medium [promising zone] (e.g., 30-70%)

• The sample size will be adjusted to increase the predicted

probability of success to 70%

• Sample size increase will be capped (e.g., at 30%)

23


24

Use the original sample size in M+ or OP since the predicted probability

of success is high

M+

Stop

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h


Low High

M+

Mediu

m

OP

OP M+

OP

Medium


25

Increase the sample size in M+ since the success probability is medium

M+

Stop

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h


Low High

M+

Mediu

m

OP

OP M+

OP

Medium


26

Increase the sample size in OP since the predicted probability of

success is medium

M+

Stop

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h


Low High

M+

Mediu

m

OP

OP M+

OP

Medium

Final decision rules

27

Summary of all decision rules at the interim analysis (↑ increase the

sample size)

↑ M+

Stop

Low

Pro

ba

bili

ty o

f su

cce

ss in

M+

Hig

h


Low High

M+

Mediu

m

↑ OP

↑ OP M+

OP

Medium

Final analysis

– Impact of data-driven decisions

• Virtually all adaptive (data-driven) decisions at an interim analysis

change the distribution of the treatment effect test statistics in OP

and M+ at the final analysis

• Overall Type I error rate is inflated if no adjustment is introduced

– Adjustment for data-driven changes

• Combination function approach (Brannath, Posch and Bauer, 2002)

will be applied to adjust the test statistics in OP and M+ and protect

the overall Type I error rate

– Adjustment for multiple comparisons

• Fallback procedure (Dmitrienko and D’Agostino, 2013) will be

applied to control the Type I error rate if two population are selected

for the final analysis (OP and M+)

28

Adaptive biomarker-driven clinical trial designs

– Design parameters

• Timing of the interim analysis

• Cutoff points used in the population selection and sample size

adjustment rules

• Cap used in the sample size adjustment rule

– Clinical trial optimization

• Comprehensive quantitative evaluation of candidate trial designs

was performed to optimally select individual designs parameters

• Operating characteristics of over 50 different trial designs were

evaluated to select the final design

29

Summary

– Adaptive designs

• Adaptive designs with complex decision rules are becoming more

widespread in Phase III trials

• Powerful statistical methods are available to support complex data-

driven decision making rules

– Operational issues

• Overall decision making process must be carefully planned, e.g.,

set up an independent data monitoring committee and data

analysis group, set up a flexible randomization system, develop site

expansion strategies, etc

30

References

Brannath, W., Posch, M., Bauer, P. (2002). Recursive combination tests.

Journal of the American Statistical Association. 97, 236-244.

Dmitrienko, A., D'Agostino, R.B. (2013). Tutorial in Biostatistics: Traditional

multiplicity adjustment methods in clinical trials. Statistics in Medicine. 32,

5172-5218.

Freidlin, B., McShane, L.M., Korn, E.L. (2010). Randomized clinical trials with

biomarkers: Design issues. Journal of National Cancer Institute. 102, 152-

160.

Millen, B., Dmitrienko, A., Ruberg, S., Shen, L. (2012). A statistical framework

for decision making in confirmatory multipopulation tailoring clinical trials.

Drug Information Journal. 46, 647-656.

Paik, S et a. (2008). HER2 status and benefit from adjuvant trastuzumab in

breast cancer. New England Journal of Medicine. 358, 1409-1411.

Romond, E.H. et al. (2005). Trastuzumab plus adjuvant chemotherapy for

operable HER2-positive breast cancer. New England Journal of Medicine.

353, 1673-1684. 31

Adaptive biomarker-driven designs in Phase III clinical trials Alex Dmitrienko's slides.pdfApr 14,...

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